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June 2013, Vol. 32, No. 6
Special Section:Special Section:
Nonreflection seismic and inversion of surface and guided waves
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594 The Leading Edge June 2013
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ts Special section: Nonreflection seismic and inversion of surface and guided waves
610 ........ An introduction to this special section: Nonreflection seismic and inversion of surface and guided waves, M. Haney and R. Miller
612 ........ Near-surface shear-wave velocities and quality factors derived from high-frequency surface waves, J. Xia, C. Shen, and Y. Xu
620 ........ Estimating deep S-wave velocity structure in the Los Angeles Basin using a passive surface-wave method, K. Hayashi, A. Martin, K. Hatayama, and T. Kobayashi
628 ........ Love waves from local traffic noise interferometry, M. Behm and R. Snieder
634 ........ Surface-wave observations after integrating active and passive source data, Y. Xu, B, Zhang, Y. Luo, and J. Xia
638 ........ Surface- and guided-wave inversion for near-surface modeling in land and shallow marine seismic data, D. Boiero, E. Wiarda, and P. Vermeer
648 ........ Exploring nonlinearity and nonuniqueness in surface-wave inversion for near-surface velocity estimation, H. Douma and M. Haney
656 ........ MASW for geotechnical site investigation, C. Park
664 ........ Bedrock mapping in shallow environments using surface-wave analysis, D. Boiero, L. V. Socco, S. Stocco, and R. Wisén
674 ........ MASW surveys in landfill sites in Australia, K. Suto
680 ........ Resolving complex structure in near-surface refraction seismology with common-offset gathers, D. Palmer
692 ........ The joint analysis of refractions with surface waves (JARS) method for finding solutions to the inverse refraction problem, J. Ivanov, J. Schwenk, S. Peterie, and J. Xia
699 ........ Field testing of fiber-optic distributed acoustic sensing (DAS) for subsurface seismic monitoring, T. Daley, B. Freifeld, J. Ajo-Franklin, S. Dou, R. Pevzner, V. Shulakova, S. Kashikar, D. Miller, J. Goetz, J. Henninges, and S. Lueth
Departments598 .......Editorial Calendar600 .......President’s Page602 .......From the Other Side604 .......Foundation News606 .......SEAM Report708 .......Bright Spots710 .......Announcements714 .......Calendar716 .......Membership718 .......Personals722 .......Advertising Index724 .......Interpreter Sam
Cover design by Kathy Gamble. Photo: Photographer is Jan Pedersen, Rambøll Danmark A/S.
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596 The Leading Edge June 2013
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STEVEN DAVIS, SEG executive director; TED BAKAMJIAN, director, publications; DEAN CLARK, editor; JENNY KUCERA, associate editor; SPRING HARRIS, assistant editor; KATHY GAMBLE, graphic design manager; TONIA GIST, senior graphic designer; JILL PARK, graphic designer; MERRILY SANZALONE, manuscript tracking specialist.
Advertising information and rates: MEL BUCKNER, phone 1-918-497-5524. Editorial information: phone 1-918-497-5535; fax 1-918-497-5557; e-mail [email protected]. Subscription information: e-mail [email protected].
THE LEADING EDGE® (Print ISSN 1070-485X; Online ISSN 1938-3789) is published monthly by the Society of Exploration Geophysicists, 8801 S. Yale Ave., Tulsa, Oklahoma 74137 USA; phone 1-918-497-5500. Per iodicals postage paid at Tulsa and additional mailing offi ces. Print subscriptions for members of the Society in good standing are included in membership dues paid at the World Bank III and IV rate. Dues for Active and Associate members for 2013 vary depending on the three-tiered dues structure based on World Bank classifi cation of the member’s country of citizenship or primary work residence. Dues
are US$90 (World Bank IV countries), $48 (World Bank III countries), and $12 (World Bank I and II countries). Dues for all Student members regardless of country of citizenship or primary residence are $21 and include online access to journals. Students may receive TLE in print by paying an additional $36. Print and online single-site subscriptions for academic institutions, public libraries, and nonmembers are as follows: $155, Domestic (United States and its possessions); $190, Surface Freight (Canada, Mexico, Central and South America, Caribbean); and $200, Mandatory Air Freight (Europe, Asia, Middle East, Africa, and Oceania). For corporations and government agencies, print and online single-site subscriptions are: $840, Domestic (United States and its possessions); $875, Surface Freight (Canada, Mexico, Central and South America, Caribbean); and $885, Mandatory Air Freight (Europe, Asia, Middle East, Africa, and Oceania). Print-only subscriptions for corporations and government agencies are: $340, Domestic (United States and its possessions); $375, Surface Freight (Canada, Mexico, Central and South America, Caribbean); and $385, Mandatory Air Freight (Europe, Asia, Middle East, Africa, and Oceania). Rates are subject to change without notice. Subscriptions to the SEG Digital Library include subscriptions to TLE. Subscribers to GeoScienceWorld are entitled to a $30 discount off print-only subscriptions to TLE. See www.seg.org/publications/subscriptions for ordering information and details. Single-copy price is $16 for members and $32 for nonmembers. Postage rates are available from the SEG business offi ce. Advertising rates will be furnished upon request. No advertisement will be accepted for products or services that cannot be demonstrated to be based on accepted principles of the physical sciences. Statements of fact and opinion are made on the responsibility of the authors and advertisers alone and do not imply an opinion on the part of the offi cers or members of SEG. Unsolicited manuscripts and materials will not be returned unless accompanied by a self-addressed, stamped envelope. Copyright 2013 by the Society of Exploration Geophysicists. Material may not be reproduced without written permission. Printed in USA.
POSTMASTER: Send changes of address to THE LEADING EDGE
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William GoodwayApache Canada 9 Avenue Southwest Calgary, AB T2P 3V4 CanadaTel: +1-403-303-5958 [email protected]
Gregory S. BakerUniversity of Tennessee 1412 Circle Drive Knoxville, TN 37996, USATel: +1-865-974-6003 [email protected]
Ezequiel F. GonzalezShell International E&P3333 Highway 6 S.Houston, TX 77082, USATel: [email protected]
Shuki [email protected]
Tad SmithApache Corporation2000 Post Oak Blvd. Suite 100Houston, TX 77056, USATel: [email protected]
Carlos Torres-VerdinUniversity of Texas Department of Petroleum and Geosystems Engineering1 University Station, Mail Stop C0300 Austin, TX 78712-0228, USATel: [email protected]
Special editorChristopher L. LinerUniversity of ArkansasDepartment of Geosciences346 Arkansas Avenue, STOS G18Fayetteville, AR 72701-1201Tel: [email protected]
PresidentDavid J. MonkApache Corporation2000 Post Oak Blvd.Houston, TX 77056, USATel: [email protected]
President-Elect Don W. SteeplesUniversity of Kansas1475 Jayhawk Blvd.Lawrence, KS 66045, USATel: [email protected]
First Vice-President Richard D. MillerKansas Geological Survey1930 Constant Ave., Campus West Lawrence, KS 66047, USATel: [email protected]
Second Vice-President Dennis A. CookeThe University of AdelaideSantos Building/Australian School of PetroleumAdelaide, South Australia, 5005 AustraliaTel: +61-(0)[email protected]
Treasurer Gary G. ServosOvation Data Services14199 Westfair East Dr.Houston, TX 77041, USATel: [email protected]
Editor Tamas NemethChevron6001 Bollinger Canyon RoadSan Ramon, CA 94583, USATel: [email protected]
Past-President Bob A. HardageBureau of Economic GeologyUniversity Station, Box XAustin, TX 78713, USATel: [email protected]
Director at Large Christine E. KrohnExxonMobil Upstream Research3319 Mercer StreetHouston, TX 77252, USATel: [email protected]
Director at Large Alfred LiawAnadarko Petroleum Corporation1201 Lake Robbins Dr.The Woodlands, TX 77380, USATel: [email protected]
Director at LargeElsa Jeanneth Jaimes R.OGX Petróleo e Gás LtdaAv Calle 116 #7-15 Of 1402Bogotá, ColombiaTel: [email protected]
Director at Large Samir AbdelmoatyTel: +20100 166 [email protected]
Director at Large A. Peter AnnanSensors & Software Inc.1040 Stacey CourtMississauga, ON L4W 2X8, CanadaTel: [email protected]
Director at Large Edith J. MillerChevron U.S.A. Inc.1500 Louisiana St., 20-040Houston, TX 77002, USATel: [email protected]
Chair of the CouncilMike GraulTexSeis, Inc.10810 Katy Freeway, Ste. 201Houston, TX 77043, USATel: [email protected]
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598 The Leading Edge June 2013
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r Issue ... Special Section theme ........................................ Due date ............. Guest editors
2013Jul .........Hydrogeophysics ................................................................past due .................... Rick Miller, [email protected] Kamini Singha, [email protected] .......Gravity and potential fi elds ................................................past due .................... Michal Ruder, [email protected]........................................................................................................................................... Robert Pawlowski, [email protected] ........ Full waveform inversion ...............................................................past due ......................Antoine Guitton, [email protected] Tariq Alkhalifah, [email protected] Chris Liner*, [email protected] ......... Geohazards ..................................................................................15 Jun 2013 ................Carlos Torres-Verdin*, [email protected] 3D VSP Jitendra Gulati, [email protected] Robert Stewart, [email protected] .......Offshore and onshore broadband seismology ....................15 Jul 2013 ............... Shuki Ronen*, [email protected]........................................................................................................................................... William Goodway*, [email protected] ........Unconventional resources technology ................................15 Aug 2013 ............. Tad Smith*, [email protected]........................................................................................................................................... Carlos Torres-Verdin*, [email protected]........................................................................................................................................... Ezequiel Gonzalez*, [email protected] ........Middle East .........................................................................15 Sep 2013 ............. Chris Liner*, [email protected] Adel El-Emam, [email protected] Said Mahrooqi, [email protected] Feb ........4D .......................................................................................15 Oct 2013 .............. William Goodway*, [email protected]........................................................................................................................................... Alan Jackson, [email protected] .......Rock physics ......................................................................15 Nov 2013 ............. Tad Smith*, [email protected]........................................................................................................................................... Carlos Torres-Verdin*, [email protected]........................................................................................................................................... Ezequiel Gonzalez*, [email protected]
(* Current TLE Board member)
www.seg.org/meetings/pol13
Notice to authorsTLE publishes articles on all areas of applied geophysics and disciplines which impact it. To submit a paper for possible publication in a specifi c issue, please e-mail an inquiry to the appropriate guest editor for that issue. Authors are encouraged to submit their papers at any time, regardless of whether they fi t the schedule. To submit an article on an unscheduled topic, contact Dean Clark, TLE editor, [email protected] or 1-918-497-5535.
Electronic submission of articlesElectronic submissions should include the manuscript fi le, fi gures and other graphics, a PDF of the manuscript and fi gures, and the author’s contact information. These fi les can be uploaded to an FTP site (the preferred method) or burned to a CD and mailed to the appropriate editor. Once accepted for TLE, the fi les will be opened and edited on a Mac or a PC using various software applications. To simplify conversion, fi gures should be submitted in TIFF, PDF or EPS (.tif, .pdf or .eps) fi le formats, with a resolution of at least 300 dpi (pixels per inch). High-resolution images can be placed in Word or PowerPoint if placed large on the page; these will be converted to PDF format. Once the paper is accepted, please also mail the appropriate editor a printed color copy of the manuscript with any fi gures, tables, and equations to be included. For assistance with electronic submission, contact Tonia Gist, [email protected] or 1-918-497-5575. More details are online at http://www.seg.org/resources/publications/tle/submission-guidelines.
Notice to lead authorsLead authors of articles published in TLE who are not members of SEG should apply for a one-year free membership and subscription to TLE by contacting Member Services, fax 1-918-497-5557 or [email protected]. Lead or corresponding authors also are required to sign a copyright transfer agreement, which gives TLE permission to publish the work and details the magazine’s and the authors’ rights. TLE staff will send a form to be signed and sent back after the article is accepted for publication. The form can be downloaded at http://www.seg.org/documents/10161/74670/SEG_Copyright_form.pdf.
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600 The Leading Edge June 2013
Over the past few years, there has been much anticipation and more than a little industry anxiety concerning the waves of departures of seasoned workers—the “silver
tsunami.” Th eir incoming, less-experienced replacements were certain to arrive at their jobs without the guidance and mentoring necessary to ensure the continuity of leadership and quality work product required by a demanding and high-stakes business.
In the September 2007 issue of Th e Leading Edge, outgo-ing SEG President Leon Th omsen wrote a President’s Page discussing, among other things, SEG’s role in facilitating the knowledge transfer from the long-term experienced Earth-science professionals to the younger geophysicists beginning to enter the workforce.
Two months later, in the November issue, President-elect Larry Lines addressed the importance of developing greater communication between industry and the institutions re-sponsible for educating future Earth scientists. He also point-ed to the growing SEG student membership (exceeding 7000 at that time) as an indication of a level of confi dence that a steady pipeline of eager and motivated professionals could be counted on to meet demand—provided they started their of-fi cial employment better prepared than any previous genera-tion of workers.
Now I, as a member of this change, am taking the oppor-tunity to put forth my own observations and provide some insight as a member of that new crew on how SEG is meeting its goal of being a vital player in what most would agree is the important aff air of wisdom transfer.
I entered the workforce in 2006, with legions of peers, only to see the 2008 crash drastically cut the supply of new Earth scientists. Fortunately, the tide has changed and the new crew is growing.
SEG indeed serves an important role in the transition of crews. It has put programs in place, many with the help of generous sponsors and donors, that better prepare students for their professional careers, and that help early-career geo-physicists ramp up faster by providing access to greater re-sources and knowledge.
Th e SEG Web site is the organization’s front door, and it now off ers entry to a variety of services that benefi t all mem-bers. For me, most members of the new crew and our more global membership, organizations don’t exist if they don’t have a Web site. Th is is our point of access.
Th e Web site (www.seg.org) has undergone an extensive transformation over the last couple of years through major eff orts by staff , and additional work by the purpose-built On-line Committee. It should be a place for one-stop-shopping
Society of Exploration Geophysicists
President’s Page
The great crew change redux
for geophysicists, providing access to journals, lecture record-ings, opportunities for online collaboration, information on the student chapters, and events and event registration. Much of what you can fi nd there has been made possible by mul-tiyear “sustaining investments” to the SEG Foundation pro-vided by corporations that understand the importance of a vibrant online off ering: CNPC, ION, Landmark, PGS, IHS, and Apache.
Yes, it is a work in progress as are many things worthwhile and broad in scope. If you feel somewhat overwhelmed by the off erings, I direct you to just a few of off erings you can fi nd there:
• Th e SEG Wiki. Th is content started with Sheriff ’s Encyclo-pedic Dictionary and the intent is to provide an accurate, timely, and useful collection of information that will prove to be an invaluable resource for working geophysicists, educators, and students in the fi eld of geophysics. Th is is a living document that will grow with input from the mem-bership of SEG. Any SEG member can add descriptions of his or her favorite geophysical terms and methods and users are encouraged to read, verify, and improve the con-tents of these pages. Visit Wiki.seg.org.
• Recordings of Honorary Lectures and Distinguished Lec-tures from the past 10 years are available to watch free of charge. Honorary Lecture tours are region-specifi c, and typically you wouldn’t have a chance to see the lectures in regions other than your own. Now you can! And if the Distinguished Lecture tour didn’t stop in your town, or you just want to see it again, go visit http://www.seg.org/education/online-education/online-presentations, or from the SEG home page, see under Resources → Multimedia. It’s an online Disneyland for geophysical professionals!
• Th e complete Continuing Education curriculum. Th e cur-riculum has grown quickly in the last couple of years and we are working on providing more courses for early-career geophysicists. Th e curriculum consists not only of face-to-face courses, but also of virtual classes (log on to a live 90-minute seminar), and a broad range of interactive on-line (3–4 hour) classes off ered through IHRDC.
• Information about the SEG Foundation at http://www.seg.org/web/foundation, which includes a description of nu-merous programs that advance our fi eld and scholarship opportunities.
Th ese are just a sampling of how SEG is reaching out to its members, whether long-standing or new to the club, around the world. How are these and other programs work-ing for you? Please send your feedback to me ([email protected]) or your friendly SEG staff member, Board mem-ber, or committee chair (fi nd them online!). We won’t know unless you tell us.
—EDITH MILLER
SEG Director at large
Th e SEG Web site is the organization’s front door and it now off ers entry to a variety of services that benefi t all members.
Visit us at EAGE stand no. 930
slb.com/isometrix
*Mark of Schlumberger. © 2013 Schlumberger. 13-se-0062
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602 The Leading Edge June 2013
For some reason, unknown to me, subjects come to mind that have no relationship to
anything going on at the time. I was talking with a friend about sundry things when the subject of the solid inner core of the Earth was
mentioned. Strange, what? “I wonder how someone decided on that,” I asked
innocently. He replied that they used earthquake data. Th at didn’t
satisfy me. “Show me the data! I can’t imagine a solid core to the Earth. I can see the molten lava bubbling out of the ground.” I vowed to look into this. To the Internet! To Wiki-pedia! To the Core of the Earth! It turns out that a seismolo-gist back in 1936 named Inge Lehmann deduced its presence from observations of earthquake-generated seismic waves that refl ect off the boundary of the inner core and can be de-tected by sensitive seismographs on the Earth’s surface. Fairly straightforward don’t you think? A refl ected seismic wave! Sensitive seismographs!
Many universities have these large cylinders with an ink stylus that record data on paper records. Th ese are the sen-sitive seismographs? Most diagrams in text books show the raypaths of an earthquake. Th e fi rst arrivals are most interest-ing because the later arrivals are often well below the signal-to-noise levels. But wait! I know little of this subject. I think we should think like exploration geophysicists rather than to take a statement that a refl ection off of an inner core can be detected and more importantly ascribed to an interface 3000 miles deep.
Th at reminds me of one of my fi rst jobs after I spent a few years in the fi eld. I was given old single-fold seismic data to map, located in the Anadarko Basin. Most of the data had been limited to 2–3 s. It is clear to me that if we are going to search for a refl ection from the “Lehmann layer,” we will need more record time! At 20,000 ft/s, we need a recorded time of about 1584 s (two-way time) for the 3000-mile depth. I am not sure of the velocity. Does it get faster when the subsurface is compressed or liquid? Velocity increases with temperature but the change is small up where we exploration geophysicists are working. Let’s round off . It would take about half an hour to get the refl ection back to the surface. But also consider the other interfaces between the source and the detector array. Th ere are other layers in the Earth, the crust, the Moho and the Gutenberg discontinuities, and other sundry layers.
Will the refl ection be a fi rst arrival? Th is should be easy for an exploration geophysicist. Th ese are fi rst principles. Consider a plane through the center of the Earth with the earthquake on the great circle. Concentric circles inside the
The Leading Edge
From the other side
A column by Lee Lawyer with stories about geophysics and geophysicists
outer circle are the layers of the Earth. How could we as ex-ploration geophysicists determine that there is a solid core? We “shoot” an earthquake at one point on the Earth and fol-low the rays through the various interfaces. We have sensitive geophones located encircling the Earth. Remember that the molten part of the Earth won’t support shear.
We could try shear-wave splitting but that is beyond my imagination. How about converted waves? We shoot at the surface. Th is generates P- and S-waves, etc. Th e S-waves are attenuated in the molten zone. Th e P-wave plows ahead. It encounters a solid core and a converted shear wave is created, which follows the parent P-wave. When the S-wave gets to the other side of the solid core, it converts back to a P-wave, which then follows the parent P-wave to the surface. Th e time diff erence of those two arrivals is a measure of the size and physical properties of the solid core. Th at seems simple to me. Whew!
Th e little reading I have done says that the inner core is solid iron-nickel. It can be solid at the temperature and pres-sure at that depth. Th is is verifi ed in the laboratory (some lab!). Th e reason the iron is at the core is “diff erentiation,” which has been around a long time; that is, it sank there! Some say the inner core is growing as the Earth cools. (Oops ... back to global warming.) Pressure is interesting. At the center of the Earth, gravity is zero (theoretically). Th e people who went to the center of the Earth in the movie should have been fl oating around like they do going up to outer space. Th e corrections for a borehole gravity meter are sort of a re-verse elevation correction or terrain correction. Calculating the pressure is complicated by the decreasing eff ect of grav-ity as you approach the core. Objects “weigh” less as you get deeper into the Earth.
As exploration geophysicists, we want to see the data (but please don’t send it)! Where are the data brokers when you need one?
To contact the “Other Side,” call or write L. C. (Lee) Lawyer, Box 441449, Houston, TX 77244-1449 (e-mail [email protected]).
604 The Leading Edge June 2013
SEG continues to develop a fi rst-class off ering of online education courses and state-of-the-art online lectures to
better serve our members around the world. High-quality online education must be available to SEG members no matter where they live and work, and when their work requires the most up-to-date geophysical knowledge. Our success over the past fi ve years would not have been possible without the leadership support and investment of CNPC.
As CNPC Assistant President Xu Wenrong stated in November 2007 when announcing the CNPC investment, “SEG works as a good bridge connecting the east and west geophysical communities, thus bringing together all ex-perts in the oil and gas industry and geophysical societ-ies. Th e fi nancial support from CNPC will reinforce this bridge, provide investment for the virtual research insti-tute that is SEG, promote training and technical exchange among the geophysical community, and build the educa-tion and training of students.” We are pleased that the on-line education progress of the last fi ve years is making this aspiration a reality.
In 2007, SEG Online Education was limited to the addition of two Distinguished Lectures and one Distin-guished Instructor Short Course (DISC) each year. Today, with CNPC support, SEG off ers the following:
• Distinguished Lectures. Recordings of lectures by globally recognized experts on current geophysical topics, ranging from 2–3 per year.
• Honorary Lectures. Recordings of lectures by regionally rec-ognized experts on locally relevant topics, ranging from 6–7 per year.
• Translations. Select Distinguished and Honorary Lec-tures are translated to assist members in core regions of the world. Chinese, Spanish, and Russian are available today. Translations range from 1–3 per year.
• Distinguished Instructor Short Courses (DISC). Each year, SEG selects a world-renowned expert to provide an eight-hour short course at select locations around the world. Th e course is recorded and provided online.
• HRDC Introductory Courses. SEG off ers more than 30 introductory courses on a broad range of geophysical topics.
• Technical Program Recordings. Many technical pre-sentations at SEG’s annual meetings from 2010 to 2012 have were recorded and made avail¬able on DVD. Starting with the upcoming 2013 An-nual Meeting in Houston, they will be made avail-able on USB. In addition, many of the 2012 pre-sentations are online and available for individual purchase. More information is at http://shop.seg.org/OnlineStore/Courses/OnlineCourses/tabid/178/Default.aspz?Category=TECH PRES AM12.
The Leading Edge
Foundation News
Advancing the online education experience
CNPC, ION leading the way
• Virtual Courses. Live online short courses on emerging technologies, with student-instructor communication, have been off ered since 2011. Students can participate live and/or review the recorded presentation at any time, and courses happen six times per year.
• Multilesson Courses. SEG is developing long courses on topics of fundamental importance to the geophysical profession. Th ese courses follow best practices for online learning. Th e fi rst such course is Geophysics 101: Seis-mic Waves in Hydrocarbon Exploration, taught by Leon Th omsen; the fi rst lesson for this course is currently avail-able for members to download.
Th e SEG Online eff ort has become even more of a crit-ical factor as our organization continues to grow globally, providing a means for the geophysical community around the world to interact and to have ready access to a wealth
Growth of SEG Online Education off erings from 2008–2013.
June 2013 The Leading Edge 605
of information and knowledge. Our progress would not be as rapid without the support of the visionary corporations and individuals making sustaining investments through the SEG Foundation. Th e SEG Foundation thanks CNPC and ION (see sidebar) for their investment in Online Educa-tion, and Apache for its investment in the new SEG Wiki; as well as IHS, Rutt Bridges, and Susie and Bob Peebler for their passionate fi nancial support of SEG’s online vision.
ION renews support to SEG Online EducationIn 2007, ION was one of
the fi rst corporations to invest in the SEG Online project, designed to provide news and resources to SEG’s global mem-bership. In 2012, ION renewed its fi ve-year commitment of US $400,000 and focused its con-tinued investment on to SEG Online Education, making the announcement at SEG Foun-dation’s 2012 Donor Luncheon in Las Vegas.
“As we truly have the mem-bership continuing to globalize, and as the nature of learning
To participate in SEG Online Education today, visit www.seg.org/elearning. To learn more about the vari-ous SEG programs or to help support these programs through a donation to the SEG Foundation, please visit www.seg.org/foundation.
—NATALIE BLYTHE
Communications SpecialistSEG Foundation
ION Chairman Bob Peebler with Foundation Chair Th omas Smith after announcing ION’s $400,000 renewal commit-ment to SEG Online Education.
has changed so dramatically with new students and new members, it is important to have the right infrastruc-ture to provide online sup-port. Even in the last four or fi ve years, things have progressed in the ways people communicate. We need knowledge without borders,” said Bob Peebler, ION chairman. “SEG On-line is exciting and will continue to evolve and grow, and we are excited to continue our support.”
606 The Leading Edge June 2013
A t the end of January, SEAM Corporation issued the fi rst request-for-bids (RFB) on numerical simulations for its
Phase II consortium “Land Seismic Challenges.” Th is RFB was for synthetic 3D seismic acquisition using the SEAM Unconventional Model, a model designed to represent challenges of imaging and characterizing subtle features of shale-gas reservoirs set in a realistic stratigraphic section and overlain by buried topography typical of midcontinent basins. SEAM is in the process of evaluating the bids received in response to the RFB and expects to start production simulations with the Unconventional Model in July.
Two other models are nearing completion in SEAM Phase II. Th e Arid Model places the reservoirs and stratigraphic sec-tion of the Unconventional Model beneath a 500-m thick near-surface region with features typical of desert areas. Th e Foothills Model, the last model to be built in Phase II, con-tains complex fold-and-thrust structures at reservoir depths, rapid lateral velocity variation at shallow depths, and extreme topography at the surface—all of which characterize the geol-ogy of compressive tectonic zones such as the Andes Moun-tains of South America. Th is update describes the Arid Model, which will be the next SEAM model to enter production nu-merical simulations.
Features of desert terrains that can disrupt seismic explora-tion include karsts, wadis or buried river channels, sand and unconsolidated surface sediments, outcropping bedrock, and topography. Figures 1 and 2 illustrate the challenge of rep-resenting desert regions in a single Earth model. Figure 1 is a shaded relief map of mountainous desert called jebels, which contain deeply eroded plateaus and high isolated peaks,
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SEAM Phase II: The Arid Model—Seismic
exploration in desert terrains
creating rough topography at scales from kilometers to tens of meters. Figure 2 is a horizontal slice through a large 3D seismic image collected on the Saudi Arabian Peninsula; the slice is at a depth of several hundred meters and covers a region about 25 × 15 km in lateral extent. Th e quasi-circular objects stippling the image are buried karsts, caused by acidic rain-water percolating through and dissolving limestone bedrock. Like jebel topography, karst structures occupy a wide range of scales. In this image, the largest ones are 1–2 km in diameter; the smallest visible ones are a few tens of meters in diameter. Smaller karsts are undoubtedly present below the resolution of the image. Figure 2 shows an outcropping karst large enough to swallow a fi eld vehicle.
Representing these desert features at their natural size and distribution would require an Earth model extending 50–100 km with individual features at the meter scale. Numerical simulations with such a model would be well beyond the ca-pabilities of today’s largest computers using the most advanced seismic modeling algorithms. Figure 3 shows an early design
Figure 1. Shaded relief map of jebel topography in the Eastern Province of the Saudi Arabian peninsula (the Arabic word “jebel” means hill or mountain). Th ese terrains are created when plateaus are deeply eroded by water and then shaped by winds, and are characterized by reliefs ranging from gentle slopes to sharp scarps and isolated peaks. Th e region shown is about 50 × 50 km. Maximum elevation diff erence is 250 m.
Figure 2. (top) A horizontal slice at a depth of several hundred meters through a 3D seismic image collected on the Saudi Arabian Peninsula. Th e dark circular objects in the lower right of the image are about 2 km in diameter and are limestone dissolution structures called karsts. Th e dots stippling the image are literally hundreds of karsts of smaller sizes, just within the resolution of the image. (bottom) Photo of a karst break-ing through the surface.
June 2013 The Leading Edge 607
Figure 3. Early design concept for the near-surface geology of the Arid Model. Th e near-surface region is 10 km × 10 km × 500 m and incor-porates a series of features characteristic of desert areas, including karsts, buried wadis, dry riverbeds, and outcropping bedrock.
Figure 4. Features of the Arid model represented as geobodies. (top)Th e deep karst fi eld buried at a depth of about 400 m. Th e large karst is about 2 km in diameter; the intersecting karst fairways follow the trends of buried stream channels. (bottom) A series of braided stream channels representing a shallow wadi in the fi rst 100 m of the subsurface.
Figure 5. Slices through the Arid Model. Th ese renderings of the com-pressional-wave velocity fi eld at 50 and 400 m show the juxtaposition of braided stream channels with shallow and deep karst fi elds.
concept for the Arid Model, in which two compromises were made for a manageable realization. Th e fi rst was to ignore to-pography; the second, to restrict the modeled volume to the same size and resolution as the SEAM Unconventional Mod-el—that is, to a region 10 ×10 km in lateral extent and 3.75 km in depth, sampled on a uniform 6.25-m grid. (Represent-ing topography and accurately modeling its eff ects on seismic waves will be a top goal of the Foothills model; in addition, members of the SEAM Phase II consortium are now enter-taining a proposal to extend the project by an additional year to add additional features such as topography to Unconven-tional and Arid models.)
Within these constraints, construction of geologic features in the digital Arid Model was based on size and shape dis-tributions seen in natural analogs. Figure 4 shows two major components of the model: a series of buried stream channels making up a shallow wadi and a deep karst fi eld. Th ese digital realizations of geologic structures were made with modeling software that can populate 3D volumes with a variety of re-alistic shapes, based on the statistics of natural occurrences, while retaining each object’s identity as an individual geo-body. Figure 5 shows two shallow depth slices through the compressional-wave velocity model, illustrating the juxtaposi-tion of wadi and karst structures. Th e subsurface of the Arid Model below 500 m incorporates the stratigraphic section of the Unconventional Model, including full orthorhombic elas-tic anisotropy in the overburden and fractured shale reservoirs (see the update in the March 2013 TLE).
Of the three geologic models in Phase II, the Arid Model will likely encompass the largest range of velocity contrasts. Karsts in arid regions are often air-fi lled voids, which means juxtaposing the seismic velocity of air (about 343 m/s) with that of hard limestone bedrock (3500–4000 m/s)—a 10:1 contrast. Th e next big advance in exploration seismic model-ing will come from routine use of conforming fi nite-element or other types of structured, multiresolution grids that can handle large contrasts with high accuracy without imposing a fi ne grid on the entire model.
Acknowledgments and further information: Design of the near-surface geologic model for the SEAM Phase II Arid Model
608 The Leading Edge June 2013
was carried out in the near-surface modeling technical committee chaired by Tim Keho of Saudi Aramco. Carl Regone of BP and Joe Stefani of Chevron made helpful early contributions to un-derstanding how karst fi elds could be represented realistically for numerical seismic modeling. Peter Wang of Schlumberger West-ernGeco built the fi nal digital realization of the model for the
SEAM Phase I Produces Results!!
SEAM Data–Public Availability Coming Soon...Watch the SEAM Website for more announcements: www.seg.org/SEAM.
Thanks to these Phase I Participants
SEAM consortium using the Petrel E&P Software Platform. Th e images in Figures 1–3 are provided courtesy of Saudi Aramco; those in Figures 4 and 5 are courtesy of Schlumberger.
—MICHAEL ORISTAGLIO
SEAM Phase II Project Manager
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Nonreflection seismic and inversion of surface and guided waves
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Introduction to this special section: Nonreflection seismic and inversion of surface and guided waves
Seismic imaging need not be synonymous with or rely on the presence of reflectors in the Earth. Much can be
gleaned from the nonreflected wavefield. For example, direct waves map out smooth velocity variations in crosswell seismic tomography, wide-angle refracted waves play a crucial role in full waveform inversion (Hole et al., 2005), and surface waves provide unmatched sensitivity to near-surface shear-wave velocity structure. Guided waves exist in both cracks (Korneev, 2008) and boreholes, the latter referred to as tube waves. The famous Biot slow wave is itself a guided-wave phenomenon akin to a tube wave (Norris, 1987). Surface-wave dispersion has a long history in seismology and was the first seismic characteristic to be subjected to an automated inversion procedure (Dorman and Ewing, 1962).
Surveys based on surface waves provide a low-cost, non-invasive means of probing the shallow subsurface using either active sources (Xia et al., 1999) or in passive mode using mi-crotremors (Aki, 1957, 1965; Louie, 2001; Okada, 2003). In fact, the past decade has witnessed a revival in the micro-tremor method because of the realization that ambient noise correlations are closely related to the surface-wave Green’s function (Campillo and Paul, 2003). Recent work has shown that other surface-wave observables besides dispersion pos-sess sensitivity to density in addition to shear-wave velocity (Lin, et al., 2012). When velocity decreases with depth, the existence of leaky waves (Ryden and Park, 2004) attests to the richness of nonreflected-wave phenomena. In a way, it is too bad that these topics must be collectively identified by what they are not (i.e., nonreflection); however, the negative termi-nology emphasizes the gaping hole in our understanding of the subsurface if only reflections are taken into account.
It is with this background that the special section has come together. Surface waves feature prominently in many of the outstanding articles that follow. Guided, leaky, and re-fracted waves round out the cast of seismic wave types that appear in this special section. The articles are grouped into four subsections:
1) Surface waves—active and passive2) Surface waves—industry data applications3) Surface waves—case histories4) Refracted waves and instrumentation
Leading off the subsection on active and passive surface waves, Xia et al. give an overview of the multichannel analysis of surface waves (MASW) and its extension to Love waves. Because of their complete insensitivity to P-wave velocity, Love waves, in comparison to Rayleigh waves, have simpler dispersion curves with a more stable inversion. Xia et al. fur-ther discuss the amplitude of Love waves and the inversion
Matthew M. haney, Anchorage, USARick MilleR, Lawrence, USA
for Q profiles from active-source data. Next, Hayashi et al. determine S-wave velocity structure to depths of 2–3 km at sites within the Los Angeles Basin using two-station micro-tremor array measurements (2ST-MAM). Hayashi et al. com-pare their method to MASW, borehole velocity logs, and a community velocity model for Southern California. In the third article of the section, Behm and Snieder show that Love waves offer the possibility of further reducing the inherent ambiguity in surface-wave imaging compared to the more commonly used Rayleigh waves. From a continuous broad-band data set of more than 50 stations that recorded traf-fic noise, Behm and Snieder were able to measure Love-wave dispersion and relate its spatial variation to the local geology. The authors comment on the unexpectedly high signal-to-noise ratio of the traffic-induced Love waves over the frequen-cy band of 1.5–5 Hz. This observation is in harmony with that reported in the earlier article by Xia et al. for Love waves. The subsection ends with an article by Xu et al. that discusses the application of seismic interferometry to surface-wave im-aging. Close attention is paid by Xu et al. to the effects of sta-tion density and spread length on dispersion-curve resolution with passive techniques. The authors are able to detect two low-velocity zones near a tunnel on the Yangtze River that they interpret as potential landslide planes.
The subsection on industry applications includes articles by Boiero et al. and Douma and Haney. Boiero et al. demon-strate remarkable success fusing leaky waves into the surface-wave inversion problem. The authors show definitively that subsurface velocity information accurately obtained from guided P- and S-waves can be jointly inverted with S-wave velocity information estimated from Rayleigh or Scholte waves. Boiero et al. compare their results to acoustic full waveform inversion and point out that the enhanced near-surface models computed from joint inversion of surface and guided waves can provide better shallow starting models for full waveform inversion. In a study of the nonlinearity inher-ent to dispersion curve inversion, Douma and Haney present a method designed to quantify the nonuniqueness of accept-able subsurface models. The authors compare linearized sur-face-wave dispersion-curve inversion initialized with different starting models to the results of a nonlinear search. Douma and Haney make the point that, for bandlimited data, the highest frequencies define a shallow unresolved region in the subsurface in a similar way to how the lowest-frequencies de-termine the maximum depth of resolution.
Case histories form the theme of the third subsection. The utility of MASW to a broad array of problems in civil engi-neering is demonstrated in Park’s article by novel approaches to site characterization. Park highlights applications target-ing quantification of a ground-shaking hazard, foundation
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with the potential to provide nearly continuous sampling in space, in contrast to traditional point sensors. Daley et al. conduct tests at sites in Alabama, Australia, and Germany in both surface and borehole environments where high-ampli-tude ground roll was observed; as result, the authors speculate that fiber optic cables may be well-suited for MASW surveys.
ReferencesAki, K., 1957, Space and time spectra of stationary stochastic waves,
with special reference to microtremors: Bulletin of the Earthquake Research Institute: Tokyo University, 25, 415–457.
Aki, K., 1965, A note on the use of microseisms in determining the shallow structures of the Earth’s crust: Geophysics, 30, no. 4, 665–666, http://dx.doi.org/10.1190/1.1439640.
Campillo, M. and A. Paul, 2003, Long-range correlations in the dif-fuse seismic coda: Science, 299, no. 5606, 547–549, http://dx.doi.org/10.1126/science.1078551.
Dorman, J. and M. Ewing, 1962, Numerical inversion of seismic sur-face wave dispersion data and crust-mantle structure in the New York-Pennsylvania area: Journal of Geophysical Research, 67, no. 13, 5227–5241, http://dx.doi.org/10.1029/JZ067i013p05227.
Hole, J. A., C. A. Zelt, and R. G. Pratt, 2005. Advances in controlled-source seismic imaging: Eos, Transactions, American Geophysical Union, 86, 177 and 181.
Korneev, V. A., 2008, Slow waves in fractures filled with viscous fluid: Geophysics, 73, no. 1, 1–7, http://dx.doi.org/10.1190/1.2802174.
Lin, F.-C., B. Schmandt, and V. C. Tsai, 2012, Joint inversion of Rayleigh wave phase velocity and ellipticity using USArray: con-straining velocity and density structure in the upper crust: Geo-physical Research Letters, 39, no. 12, L12303, http://dx.doi.org/10.1029/2012GL052196.
Louie, J. N., 2001, Faster, better: shear-wave velocity to 100 meters depth from refraction microtremor arrays: Bulletin of the Seis-mological Society of America, 91, no. 2, 347–364, http://dx.doi.org/10.1785/0120000098.
Norris, A., 1987, The tube wave as a Biot slow wave: Geophysics, 52, no. 5, 694–696, http://dx.doi.org/10.1190/1.1442336.
Okada, H., 2003, The microtremor survey method: SEG, http://dx.doi.org/10.1190/1.9781560801740.
Ryden, N. and Park, C. B., 2004, Surface waves in inversely dispersive media: Near Surface Geophysics, 2, 187–197.
Xia, J., R. D. Miller, and C. B. Park, 1999, Estimation of near-surface shear-wave velocity by inversion of Rayleigh waves: Geophysics, 64, no. 3, 691–700, http://dx.doi.org/10.1190/1.1444578.
properties of a proposed nuclear plant site, and improving the understanding of highway stability. Park describes the advan-tages of combining active and passive surveys to sense shal-low and deep structure in South Africa. A second article in this special section with Boiero as lead author describes two field cases from Scandinavia concerning water resource map-ping and site characterization for tunnel construction. Boiero et al. analyze surface-wave dispersion curves with a Monte Carlo inversion technique and in the process address the nonuniqueness problem, thereby avoiding falling into local minima of the misfit function. Comparisons of the surface-wave profiles are made to borehole data and the results of seis-mic reflection surveying. Several case studies by Suto describe MASW surveys at landfill sites in Australia where objectives include precise delineation of the base of a landfill, monitor-ing of compaction, characterization of an incipient sinkhole, and gauging ground strength in lieu of a large commercial development. Suto discusses a study in which disagreement exists between results from MASW data and dynamic cone penetration tests. In closing, the author speculates as to why shear-wave velocity analysis of the near surface is yet to be embraced by geotechnical engineers.
Articles in the final grouping push the state-of-the-art in near-surface refraction imaging and instrumentation. Palmer discusses the generalized reciprocal method of refraction im-aging and adaptations of the method to work with common-offset gathers (COG). The adaptations offer benefits in the form of convenient determination of the crossover distance and improved resolution at the base of the weathering zone, in particular for low-angle thrust faults. An advantage of the COG adaptations lies in their ability to rapidly assess large volumes of refraction data. Ivanov et al. present a study on the joint analysis of refractions with surface waves, or JARS method. In contrast to conventional refraction tomography, the JARS method addresses the inherent nonuniqueness of the inverse refraction problem by defining an initial Vs model based on MASW. Ivanov et al. describe several field applica-tions of the method, including one from a levee in southern New Mexico where a velocity inversion is resolved at 10-m depth. Daley et al. give an account of field testing of a fiber optic cable for seismic applications. The fiber optic cable is an exciting new development in near-surface instrumentation
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Near-surface shear-wave velocities and quality factors derived from high-frequency surface waves
Shear (S)-wave velocities of near-surface materials can be derived from inverting the dispersive phase velocity
of high-frequency (≥ 2 Hz) surface (Rayleigh and/or Love) waves (e.g., Song et al., 1989). Multichannel analysis of surface waves (MASW) uses phase information of high-frequency Rayleigh waves recorded on vertical component geophones to determine S-wave velocities (Miller et al., 1999). Multichannel analysis of Love waves (MALW) uses phase information of high-frequency Love waves recorded on horizontal (perpendicular to the direction of wave propagation) component geophones to determine S-wave velocities (Xia, 2012b). Both MASW and MALW possess stable and effi cient inversion algorithms to invert phase velocities of surface waves but MALW has some attractive advantages: (1) Love-wave dispersion curves are simpler than those of Rayleigh waves; (2) dispersion images of Love-wave energy have a higher signal-to-noise ratio and are more focused than those generated from Rayleigh waves; and (3) inversion of Love-wave dispersion curves is less dependent on initial models and more stable than from Rayleigh waves.
S-wave velocities of near-surface materials that are de-rived from high-frequency surface waves utilize only the sur-face wave’s phase information. Th e feasibility of also using the amplitude information to estimate near-surface quality factors (Q s and/or Q p) has been studied (Xia et al., 2002a and 2012b). Quality factors (Q p, Q s) can be obtained by inverting attenua-tion coeffi cients calculated from the amplitude of high-frequen-cy surface (Rayleigh and/or Love) waves. Inversion of attenua-tion coeffi cients of Love waves to estimate Q s is simpler than for Rayleigh waves because they are independent of Q p.
Methods that use high-frequency surface waves to estimate near-surface S-wave velocities and quality factors are noninva-sive, nondestructive, environmental-friendly, low-cost, fast, and in-situ methods. Th is article describes key aspects of the multichannel analysis of high-frequency surface-wave methods through discussion of inversion systems and real-world exam-ples. Challenges in applying high-frequency surface-wave meth-ods in practice are also presented the end of the article.
Inversion of Rayleigh-wave phase velocitiesPhase information of high-frequency surface waves can be used to estimate near-surface S-wave velocities. Th e Ray-leigh-wave phase velocity of a layered Earth model is a func-tion of frequency and four properties: P-wave velocity (VP), S-wave velocity (VS), density (ρ), and thickness (h) of layers. Rayleigh-wave phase velocity, cRj, is determined by a charac-teristic equation F (Schwab and Knopoff , 1972), in its non-linear, implicit form F( fj, cRj, VS, VP, ρ, h) = 0, ( j = 1, 2, ..., m, f is frequency).
Analysis of the Jacobian matrix provides a measure of the dispersion-curve sensitivity to Earth properties (Xia et al.,
JIANGHAI XIA, CHAO SHEN, and YIXIAN XU, China University of Geosciences (Wuhan)
1999). S-wave velocity is the dominant infl uence on a dis-persion curve so, for our purposes, only S-wave velocities are considered unknowns in their inversion. A key consideration
Figure 1. (a) 48-channel Rayleigh-wave data acquired in Wyoming along a WE line. Th e data were acquired with the source at the west end of the line. (b) Dispersion property of Rayleigh-wave was clearly shown in the f-v domain. Th e solid dots represent phase velocities that were used in inversion to estimate near-surface S-wave velocities.
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is the degree that solution nonuniqueness will increase if the thickness values are treated as unknowns. Th is is a reason thickness should be defi ned as an input value in the inversion of Rayleigh-wave phase velocities.
A Rayleigh-wave survey was conducted in Wyoming to determine S-wave velocities in near-surface materials (upper 7 m). Rayleigh-wave energy was strong and easily recognized (Figure 1a and Figure 1b). By following the peaks of the energy trend in Figure 1b, we can pick up phase velocities at diff erent frequencies (dots in Figure 1b). Inversion of the phase velocities with an initial model determined with the suggested formula by Xia et al. (1999) provided an S-wave velocity model that was supported by borehole measurements (Figure 2).
S-wave velocity profi les derived by MASW compared fa-vorably to direct borehole measurements at numerous sites (e.g., Xia et al., 2002b). On average, the diff erence between MASW-calculated VS and borehole-measured VS is less than 15%. MASW not only provides accurate near-surface S-wave velocities but in some geologic settings it may be the only method to obtain S-wave velocity information—e.g., a set-ting with a dipping layer where converted P-wave energy could be dominant in a shear-wave refraction survey (Xia et al., 2002b) or a velocity inversion (a higher-velocity layer on the top of a lower-velocity layer), resulting in no refraction event from the interface.
Accuracy of S-wave velocity models derived from MASW can be improved when inversion simultaneously includes the fundamental- and high-mode phase velocities of Rayleigh waves (Xia et al., 2003). Moreover, the inversion is more
Figure 2. S-wave velocities from inverted S-wave velocities labeled as MASW W-E (E) and MASW W-E (W) that represent sources at the east and west ends of the line, respectively, and the suspension log. Th e S-wave velocity model labeled MASW W-E (W) was inverted from phase velocities shown as dots in Figure 1b. Results of SH-wave refl ection data were also shown in the fi gure, which were interpreted as velocities of converted waves except for the fi rst layer (adapted from Xia et al., 2002b).
Figure 3. (a) SH-wave data acquired in the grassy area in front of the KGS building. (b) An image of Love-wave energy generated with high-resolution linear Radon transform (Luo et al., 2008). Th e sharp Love-wave energy trend makes picking phase velocities easier and more accurate. Th e solid dots represented phase velocities that were used in inversion to estimate near-surface S-wave velocities.
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stable when higher-mode data are included in the process. Th is is because high-mode Rayleigh-wave data behave like Love waves, which as we know are independent of P-waves. Numerical modeling results (Xia et al., 2003) indicate that the eff ects of P-wave energy are negligible in high modes of Rayleigh waves.
Inversion of Love-wave phase velocitiesTh e Love-wave phase velocity of a layered Earth model is in-dependent of P-wave velocity and is a function of frequency and three properties: SH-wave velocity (Vsh), density (ρ), and thickness (h) of layers. S-wave velocity associated with Love waves is only the SH. Love-wave phase velocity, cLj, is deter-mined by a characteristic equation F (Schwab and Knopoff , 1972), in its nonlinear, implicit form F( fj, cLj, vhs, ρ, h) = 0, ( j = 1, 2, ..., m, f is frequency).
Some (Renalier et al., 2011; Xia et al., 2012b) suggest estimating SH-wave velocity using Love-wave inversion for near-surface applications may become more appealing than Rayleigh-wave inversion because of the simplicity of Love-wave dispersion curves. Numerical modeling results suggest the independence of Love-wave energy from P-waves reduces the chances that Love-wave dispersion curves will be com-plicated by “mode kissing” (an undesired and frequently oc-curring phenomenon in Rayleigh-wave analysis which often results in mode misidentifi cation).
Real-world examples (Xia et al., 2012b) demonstrate two other advantages of inverting Love-wave dispersion curves. Love-wave dispersion images in the frequency-velocity (f-v) domain have a higher signal-to-noise ratio and are more fo-cused than equivalent images generated from Rayleigh waves. Th is advantage is generally related to the long geophone spreads commonly used for SH-wave refraction surveys. Pick-ing Love-wave phase velocities is easier and more accurate from the images of Love-wave energy from longer-off set data because they possess higher resolution than those from near-off set data. In addition, inversion of Love-wave dispersion curves is more stable and less dependent on initial models than Rayleigh waves.
Th e same algorithm used for the inversion of Rayleigh-wave dispersion curves displayed previously was used to in-vert Love-wave phase velocities. Love-wave data (Figure 3a) were acquired on the grass-covered lawn at the Kansas Geo-logical Survey with 40 14-Hz horizontal-component geo-phones oriented perpendicularly (SH) along the same survey line where Rayleigh-wave data were collected previously (Xia et al., 1999). Geophones were spaced at 1-m intervals. A po-larized seismic energy pulse was provided by delivering an im-pact to a block, oriented in a direction parallel to that of the geophone’s maximum sensitivity, with a 6.3-kg hammer. A resolution overtone image (Figure 3b) was generated from the shot gather. Phase velocities from the fundamental mode can be picked from 12 to 45 Hz on the image (dots in Figure 3b).
An initial model used for inversion of the dispersion curve (dots in Figure 3b) was general because inversion of Love-wave phase velocities is not sensitive to initial models. Obvi-ously, in this example, an initial value for the fi rst layer could
be more accurately determined (Figure 4) because phase ve-locities approach the S-wave velocity of around 110 m/s for the fi rst layer at the high-frequency range.
After fi ve iterations, the root-mean-squares error was re-duced from 280 m/s to 5 m/s. Th e fi nal inverted S-wave ve-locity model (Figure 4) compares favorably to the borehole measurements. We note that relatively large diff erences exist between inverted results and borehole measurements in the top layers (0–3 m) and layers from 6 to 12 m (Figure 4). Th e asymptote of the Love-wave energy (Figure 3b) clearly indicates the SH-wave velocity of the top layer is consistent with the in-verted velocity of 110 m/s. Th e diff erence observed between 6 and 12 m may be indicative of S-wave velocity anisotropy.
Figure 4. S-wave velocity models: the initial model, the inverted model from the picked phase velocities shown by solid dots in Figure 3b, and borehole measurements. See the text for explanation of the discrepancy between the inverted model and borehole measurements.
Figure 5. An example of an L-curve generated with diff erent damping factors. A wise choice of solutions should be within a trade-off zone.
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Inversion of attenuation coeffi cients of Rayleigh wavesAmplitude information from high-frequency surface waves can be used to estimate near-surface quality factors (Q). Laboratory experiments (Johnston et al., 1979) showed that Q may be independent of frequency over a broad bandwidth (10−2–107 Hz), especially for some dry rocks. Th e quality fac-tor as a function of depth, which is directly related to the material damping ratio D (= 0.5Q−1) (Rix et al., 2000), is of essential interest in earthquake engineering (Kramer, 1996), geotechnical engineering, groundwater, and environmental
Figure 6. (a) Raw Rayleigh-wave data used for determining near-surface quality factors. (b) Attenuation coeffi cients from 23 to 75 Hz shown in diamonds were determined from data shown in (a) and those in solid squares were determined with inverted quality factors shown in (d). (c) Th e L-curve used to determine the trade-off value of a damping factor (a solid square). Th e numbers next to the symbols are values of damping factors associated with the solutions. (d) A trade-off Qs model (in the logarithmic scale) under the constraint 0 < Qs < 100.
studies, as well as in oil exploration and earthquake seis-mology. To fully understand seismic-wave propagation in the Earth, the quality factors are parameters that must be known.
Modeling results (Xia et al., 2002a) suggest it is feasible to estimate QP and QS in a layered Earth model by inverting Rayleigh-wave attenuation coeffi cients when VS/VP reaches 0.45. Only QS can be estimated from Rayleigh-wave attenu-ation coeffi cients when VS/VP is less than 0.45, which is a common situation for near-surface materials.
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Shallow Rayleigh-wave data (Figure 6a) were acquired in an arid region of the southwestern United States during the spring of 2010. Th e objective of this survey was to determine the seismic properties of near-surface sediments. Rayleigh-wave data were acquired using a towed streamer that con-sisted of 24 4.5-Hz vertical component geophones with a nearest-off set of 41 m, a geophone interval of 1.2 m, and an accelerated weight drop as a seismic source.
We calculated attenuation coeffi cients (triangles in Figure 6b) using the assumption Q P = 2Q S during inversion. S-wave velocities of the model were determined by inverting phase velocities of Rayleigh waves before inversion of attenuation coeffi cients.
Inversion of attenuation coeffi cients of Love wavesInformation about the amplitude of high-frequency Love waves can be used to estimate near-surface quality factors (Q s). Un-like Rayleigh waves, attenuation coeffi cients (amplitude) of Love waves are independent of the quality factors of P-waves and are functions of quality factors of Love waves. In theory, fewer pa-rameters make the inversion of attenuation coeffi cients of Love waves more stable and reduce the degree of nonuniqueness.
SH-wave refraction data (Figures 7a) acquired in Wyo-ming were used to estimate near-surface quality factors. Th e data were acquired using 48 28-Hz horizontal-component geophones oriented NS. Geophones were deployed at a 0.9-m interval along a WE spread. A polarized seismic pulse was generated by a 6.3-kg hammer impacting a coupled plate (S-wave source plate) perpendicular to the spread. Attenuation coeffi cients from 10 to 45 Hz (diamonds in Figure 7b) were determined from data shown in Figure 7a. As mentioned in the previous sections, S-wave velocities were estimated by in-verting phase velocities of Love waves using MALW.
We used the same layer model in inverting attenuation coeffi cients in terms of thickness of layers as inverting phase velocities of Love waves using MALW. Th e fi nal model pos-sesses a reasonable range of quality factors for near-surface materials. Because of the constraints applied to the inverse system, we expect the fi t in the data space to decrease. Th is is the price we have to pay for no a-priori information on quality factors at this site. Calculated attenuation coeffi cients (solid squares in Figure 7b) from the inverted quality factor model (Figure 7c) fi t measured data (diamonds in Figure 7b) reasonably well.
Figure 7. (a) 48-channel SH-wave refraction data in Wyoming. (b) Attenuation coeffi cients from 10 to 45 Hz (diamonds) from data shown in (a) and attenuation coeffi cients (solid squares) determined with the inverted model shown in (c). (c) Th e inverted Qs model (in the logarithmic scale) under the variable constraints (see the text for details).
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Other developments and challengesHigh-frequency surface-wave methods have received in-creased attention in the near-surface geophysics and geo-technical communities in the last 20 years. Th e noninvasive, nondestructive, effi cient, environmental friendly, and low-cost nature of these methods matched with their proven suc-cess in environmental and engineering applications and the upswing in interest should not be unexpected. Th ese meth-ods are viewed by the near-surface geophysics community as one of most promising techniques for determining elastic properties. However, they face unique problems related to the extremely irregular velocity variations common in near-surface geology or man-made structures (e.g., highway, foun-dation, dam, levee, jetty, etc.). It is these irregularities that inhibit solving this characterization problem with techniques or algorithms that are in wide use in earthquake seismology or oil/gas seismic exploration.
Calculating dispersion curves by existing algorithms may fail for velocity models with velocity inversions (a high-ve-locity layer on the top of a low-velocity layer). Th ere are two velocity models that are most common in near-surface appli-cations: a low-velocity half-space model and a high-velocity surface-layer model. Th e low-velocity half-space model re-sults in a complex matrix that has no roots in the real number domain. Th erefore, based on current algorithms, no phase ve-locities can be calculated in certain frequency ranges. A work-around for this dilemma is to use only the real part of the root of the complex number (Pan et al., 2013). It is well-known that surface-wave phase velocities approach about 92% of the surface layer S-wave velocity when wavelengths of surface waves are much shorter than the thickness of the fi rst layer. For the high-velocity surface-layer model, however, phase ve-locities calculated using the current algorithms approach, in a high-frequency range, the value associated with the lowest S-wave velocity of the model rather than with the S-wave ve-locity of the surface layer. We are working on this problem.
Horizontal resolution of an S-wave velocity profi le de-rived from inversion of surface-wave phase velocities is critical in imaging subsurface in near-surface applications, although it is not a serious challenge for some applications, such as seismic zonation (Yilmaz et al., 2009). Th e horizontal resolu-tion is mostly infl uenced by the length of the receiver spread. A short receiver spread may be the ultimate solution for in-creasing horizontal resolution. For this to become common practice, algorithms need to be developed that could generate an image of surface-wave energy with high resolution in the f-v domain or are stable in terms of handling noise.
Accurately determining attenuation coeffi cients still re-mains a challenge. It requires highly consistent geophones and careful placement of those geophones. To the reduce ef-fects of geophone consistency and placement, averaging mul-tiple traces in the frequency domain may be useful.
ReferencesJohnston, D. H., M. N. Toksöz, and A. Timur, 1979, Attenuation of
seismic waves in dry and saturated rocks: II. Mechanisms: Geo-physics, 44, no. 4, 691–711, http://dx.doi.org/10.1190/1.1440970.
Kramer, S. L., 1996, Geotechnical earthquake engineering: Prentice HallLuo, Y., J. Xia, R. D. Miller, Y. Xu, J. Liu, and Q. Liu, 2008, Rayleigh-
wave dispersive energy imaging by high-resolution linear Radon transform: Pure and Applied Geophysics, 165, no. 5, 903–922, http://dx.doi.org/10.1007/s00024-008-0338-4.
Miller, R. D., J. Xia, C. B. Park, and J. Ivanov, 1999, Multichannel analysis of surface waves to map bedrock: Th e Leading Edge, 18, no. 12, 1392–1396, http://dx.doi.org/10.1190/1.1438226.
Pan, Y., J. Xia, and C. Zeng, 2013, Verifi cation of correctness of us-ing real part of complex root as Rayleigh-wave phase velocity by synthetic data: Journal of Applied Geophysics, 88, 94–100, http://dx.doi.org/10.1016/j.jappgeo.2012.09.012.
Renalier, F., G. Bièvre, D. Jongmans, M. Campillo, and P.-Y. Bard, 2011, Clayey landslide investigations using active and passive VS measurements, in R. Miller, J. Bradford, and K. Holliger, eds., Ad-vances in near-surface seismology and ground-penetrating radar: SEG, 397–414, http://dx.doi.org/10.1190/1.9781560802259.
Rix, G. J., C. D. Lai, and A. W. Spang Jr., 2000, In situ measurement of damping ratio using surface waves: Journal of Geotechnical and Geoenvironmental Engineering, 126, no. 5, 472–480, http://dx.doi.org/10.1061/(ASCE)1090-0241(2000)126:5(472).
Schwab, F. A. and L. Knopoff , 1972, Fast surface wave and free mode computations, in B.A. Bolt, ed., Methods in computational phys-ics: Academic Press, 87–180.
Song, Y. Y., J. P. Castagna, R. A. Black, and R. W. Knapp, 1989, Sensi-tivity of near-surface shear-wave velocity determination from Ray-leigh and Love waves: 59th Annual International Meeting, SEG, Expanded Abstracts, 509–512.
Xia, J., R. D. Miller, and C. B. Park, 1999, Estimation of near-surface shear-wave velocity by inversion of Rayleigh waves: Geophysics, 64, no. 3, 691–700, http://dx.doi.org/10.1190/1.1444578.
Xia, J., R. D. Miller, C. B. Park, and G. Tian, 2002a, Determining Q of near-surface materials from Rayleigh waves: Journal of Ap-plied Geophysics, 51, no. 2-4, 121–129, http://dx.doi.org/10.1016/S0926-9851(02)00228-8.
Xia, J., R. D. Miller, C. B. Park, E. Wightman, and R. Nigbor, 2002b, A pitfall in shallow shear-wave refraction surveying: Journal of Ap-plied Geophysics, 51, no. 1, 1–9, http://dx.doi.org/10.1016/S0926-9851(02)00197-0.
Xia, J., R. D. Miller, C. B. Park, and G. Tian, 2003, Inversion of high frequency surface waves with fundamental and higher modes: Journal of Applied Geophysics, 52, no. 1, 45–57, http://dx.doi.org/10.1016/S0926-9851(02)00239-2.
Xia, J., Y. Xu, R. D. Miller, and J. Ivanov, 2012a, Estimation of near-surface quality factors by constrained inversion of Rayleigh-wave attenuation coeffi cients: Journal of Applied Geophysics, 82, 137–144, http://dx.doi.org/10.1016/j.jappgeo.2012.03.003.
Xia, J., Y. Xu, Y. Luo, R. D. Miller, R. Cakir, and C. Zeng, 2012b, Ad-vantages of using multichannel analysis of Love waves (MALW) to estimate near-surface shear-wave velocity: Surveys in Geophysics, 33, no. 5, 841–860, http://dx.doi.org/10.1007/s10712-012-9174-2.
Yilmaz, O., M. Eser, and M. Berilgen, 2009, Applications of engineer-ing seismology for site characterization: Journal of Earth Science, 20, no. 3, 546–554, http://dx.doi.org/10.1007/s12583-009-0045-9.
Acknowledgements: Th is work is partly supported by the National Natural Science Foundation of China (NSFC), under Grant No. 41274142.
Corresponding author: [email protected]
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Nonreflection seismic and inversion of surface and guided waves
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Estimating deep S-wave velocity structure in the Los Angeles Basin using a passive surface-wave method
This article summarizes a passive surface-wave method that uses only two sensors and its application to the
estimation of deep S-wave velocity structure.Th ree-dimensional S-wave velocity structure to a depth
of several kilometers has a large eff ect on long-period ground motion in tectonic basins, such as the Los Angeles (LA) Ba-sin. Recent studies of long-period ground motion in the LA Basin (e.g., Hatayama and Kalkan, 2012) show that observed ground motion in some areas cannot be explained by the S-wave velocity models in current use. Most studies of basin velocity structure rely on geologic information, surface and borehole geophysical data, and observed earthquake records to deduce or measure seismic velocities. Geophysical data and seismic stations commonly used for velocity analysis are sparsely distributed and most well data are too shallow to characterize deep S-wave velocity structure. To establish more accurate basin velocity structure, there is a need for more densely distributed deep S-wave velocity data.
Active and passive surface-wave methods have been in-creasing in popularity over the last 15 years. Th e passive surface-wave method or microtremor array measurements (Okada, 2003), in which surface waves from ambient noise are used, is particularly attractive to estimate deep S-wave ve-locity structure. Th is is because the method does not require an artifi cial source and the depth of investigation can easily be extended by increasing the size of the array, providing the necessary low-frequency microtremor energy is present.
Large-scale microtremor array measurements have been widely used in the last 15 years in Japan for estimating S-wave velocity structure to a depth of several kilometers. In these investigations, triangle arrays with dimensions of several kilo-meters are used for calculating Rayleigh-wave phase velocity in the frequency range of 0.2–1 Hz.
Many practitioners use the spatial autocorrelation (SPAC) method (Aki, 1957) for calculating phase velocities from am-bient noise data and the method requires at least four sensors placed on the center and vertices of an equilateral triangle. Margaryan et al. (2009) show that SPAC using only two sen-sors yields almost identical phase velocities as triangle arrays with four sensors. Recently, Hayashi and Underwood (2012a, 2012b) and Hayashi et al. (2013) show that S-wave velocity profi les down to a depth of 2–3 km can be determined by using two sensors and SPAC in the San Francisco South Bay area in California and Seattle and Olympia in Washington. SPAC using a small number of sensors (less than four) enables acquisition of microtremor array data much more effi ciently and is, therefore, more cost-eff ective.
To evaluate the applicability of SPAC using two sensors,
KOICHI HAYASHI, GeometricsANTONY MARTIN, GEOVisionKEN HATAYAMA, National Research Institute of Fire and DisasterTAKAYUKI KOBAYASHI, OYO Corporation USA
herein referred to as two-station microtremor array measure-ments (2ST-MAM), we performed measurements at four sites in the southwestern portion of the LA Basin and com-pared resulting S-wave velocity profi les with existing borehole S-wave velocity logs and a 3D seismic velocity model based on geologic structure models and sparse geophysical data.
Data acquisition and processingTwo-station microtremor array measurements (2ST-MAM) were performed at four sites (Carson, South Gate, Willow-brook, and Manhattan Beach) in the southwestern portion of LA Basin (Figure 1). At each site, one sensor was established at a fi xed location with microtremor data acquired at this lo-cation for the duration of the survey. Microtremor data were acquired with a second sensor using variable station separa-tions ranging from 10 to 3430 m, 3802 m, 820 m, and 2645 m at the Carson, South Gate, Willowbrook, and Manhattan Beach sites, respectively. At each measurement location, we recorded microtremor data for several hours per site using intervals of 10 minutes to 1 hour and a 10-ms sample rate. As the separation of sensors increased, the record length of ambient noise was increased (Figure 2). Acquisition of data from smaller arrays (less than 300 m) and larger arrays was performed during the day and night, respectively. Receivers were placed in relatively quiet locations such as in parks or residential areas.
Seismographs used for this study were McSEIS-MT Neo by OYO Corporation. Th e units are self-contained with a single set of three-component accelerometers and a GPS clock. Th e GPS clock allows synchronizing of multiple seis-mographs.
A vertical component of ambient noise is used in pro-cessing. Recorded data were divided into several time blocks with overwraps. Each block consists of 8192 samples with a data length of 81.92 s. Several blocks containing nonstation-ary noise were rejected before processing. Coherence was cal-culated for each block with the real part of coherence for all blocks averaged to obtain the spatial auto correlation (SPAC). A velocity that minimizes the error in between SPAC and a Bessel function (fi rst kind, zero order) can be considered as the phase velocity. An outline of the processing based on SPAC using two sensors is summarized in Figure 3.
Example of spatial autocorrelationFigure 4 shows an example of spatial autocorrelations at the Manhattan Beach site. It is evident that there are clear dis-tinctions between the coherence for various sensor spacings (Figure 4a). Frequency-based coherences (Figure 4b) have
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At the Carson, South Gate, and Manhattan Beach sites, the longest wavelengths associated with observed phase veloc-ities are greater than 5 km and may include information on the S-wave velocity structure to a depth of 2–3 km. Th e maxi-mum Rayleigh-wave phase velocities at these sites are about 2000 m/s, which implies that S-wave velocities at the depth of 2–3 km are greater than 2000 m/s. In general, the longest wavelength obtained applying SPAC is 2–4 times the receiver separation. Th e maximum receiver separations at these sites are 3430 m, 3802 m, and 2645 m, respectively and, therefore, it is reasonable that the maximum wavelengths at these sites are 5–10 km. At the Willowbrook site, the longest wavelength was only about 3 km because of a lack of large sensor spacing
good agreement between the ob-served coherences and the theoretical Bessel functions. Dispersion curves are clearly evident in the frequency range of 0.25–0.5 Hz in the low-fre-quency image and from 0.5 to 20 Hz in the high-frequency image (Figure 4c).
Clear variation of coherence and phase velocity are observed in the frequency range of 0.2–0.4 Hz in the low-frequency chart and from 1 to 5 Hz in the high-frequency chart. Th ese variations correspond to deep (several kilometers) and shallow (sev-eral tens of meters) velocity structure, respectively. Th e results demonstrate the applicability of the 2ST-MAM to both deep and shallow investigations.
Dispersion curvesSmall-scale microtremor array mea-surements and active surface-wave measurements (MASW) were also performed at the Carson site (Dolphin Park). Triangle and linear arrays were used for small-scale passive data acquisition. Th e maximum size of the triangle array was 60 m and the length of the linear array was 115 m. In the triangular array, seven geophones with a natural frequency of 1 Hz were used for data acquisition. In the linear array, 24 geophones with a natural frequency of 4.5 Hz were deployed on 5-m intervals.
Passive surface-wave data were recorded for about 10 minutes with a 2-ms sample rate. Th e confi guration of the linear array was similar to that of a popular passive surface-wave method often referred to as the refraction microtremor method (ReMiTM).
In the active surface-wave method (MASW), 24 geo-phones (4.5 Hz) were spaced along a single line at 1-m inter-vals. A 10-kg sledge hammer was used as the energy source for the MASW testing. Figure 5 shows the comparison of dispersion curves together with corresponding wavelength.
It is clear that a dispersion curve obtained by the 2ST-MAM agrees with that of the small passive arrays (triangle and linear) at the overlapping frequency range of 2–3 Hz. Maximum wavelengths obtained using the 2ST-MAM, the small arrays, and the MASW were about 10 km, 200 m, and 50 m, respectively (Figure 5). As a rule of thumb, 1/2 to 1/3 of the maximum Rayleigh-wave wavelength is indicative of the penetration depth of the surface-wave method. Th e ex-tremely deep penetration capability of the 2ST-MAM is obvi-ous when compared to conventional surface-wave methods, such as MASW or ReMi (Figure 5).
At all sites, phase velocities were obtained at the frequency range from 0.4–13 Hz. We estimated Rayleigh-wave phase velocities down to a frequency of 0.25 Hz, except for the Wil-lowbrook site which had maximum frequencies varying from 13 to 30 Hz (Figure 6).
Figure 2. Example of 2ST-MAM array confi guration (Carson). Only receivers more than 900 m from the fi xed receiver are shown.
Figure 1. Sites of investigation. Note that the velocity log associated with the Manhattan Beach site is several kilometers north of the 2ST-MAM array.
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and, therefore, the surface-wave data likely provide information only on S-wave velocity structure to a depth of about 1 km.
Th e phase velocities at the Manhattan Beach site are lower than those of the other sites over the frequency range from 0.4 to 1.5 Hz. Th e phase velocities at the Willowbrook and the Manhattan Beach sites are much faster than those of other sites across the frequency range of 3–10 Hz.
InversionAn inversion scheme (Suzuki and Yamanaka, 2010) was ap-plied to the observed dispersion curves to develop S-wave
velocity profi les for the four sites. During the inversion, the phase velocities of the dispersion curves were used as obser-vation data with the unknown parameters being layer thick-ness and S-wave velocity. A genetic algorithm (Yamanaka and Ishida, 1995) was used for optimization. Th e search area for the inversion was determined based on initial veloc-ity models created by a simple wavelength transformation in which wavelengths calculated from phase velocity and fre-quency pairs are divided by three and mapped as depth.
Th e theoretical phase velocity was defi ned as an eff ective mode that was generated by calculating the weighted aver-age of the fundamental mode and higher modes (up to the 20th mode) based on the medium response. Th e inversion was based on minimization of diff erences between the ob-served and the eff ective-mode phase velocities. To objectively evaluate the capability of the 2ST-MAM, the inversion was performed without a priori information so that the investiga-tion can be considered a blind test, to a certain degree. In a later section, existing borehole velocity logs and a crustal scale 3D S-wave velocity model will be shown for comparison.
Figure 7 shows an example of a comparison of observed and theoretical dispersion curves. Yellow circles indicate the eff ective mode of theoretical phase velocities as mentioned above. Th e theoretical dispersion curve (eff ective mode) agrees reasonably well with the observed data.
S-wave velocity profi lesAt the Carson, South Gate and Manhattan Beach sites, S-wave velocity profi les were determined to a depth greater than 2.5 km (Figure 8). At the Willowbrook site, an S-wave velocity profi le was determined only to a depth of about 1 km. Th e relatively shallow penetration depth at the Willow-brook site was because of a lack of microtremor measure-ments with a large sensor separation.
At all four sites, there is a near-surface layer with S-wave velocity less than 300 m/s. A shallow stiff sediment layer with S-wave velocity of more than 300 m/s was determined at a depth ranging from 10 to 50 m, depending on the site. Th is layer was relatively deep at the Carson and South Gate sites compared with the other two sites.
An intermediate velocity layer with S-wave velocity great-er than 1000 m/s was calculated for a depth range from 500 to 750 m. Above this layer, S-wave velocity at the Manhattan Beach site in the 250–750 m depth range is clearly lower than the other three sites. Th is lower S-wave velocity at the Man-hattan Beach site is because of relative lower phase velocity in the frequency range of 0.4–1.5 Hz. Th is intermediate velocity bedrock unit corresponds to a thick layer in which S-wave velocity ranges from 1000 to 1500 m/s in the depth range of 750–2500 m.
At the three sites (Carson, South Gate, and Manhattan Beach) where S-wave velocity profi les were estimated to a depth of around 3000 m, the bedrock with S-wave velocity greater than 2000 m/s was estimated to be at a depth of more than 2500 m. Th e deepest bedrock modeled at South Gate posses an S-wave velocity lower than that at the Carson and Manhattan Beach sites.
Figure 3. Outline of processing based on the spatial autocorrelation using two sensors. If f(i,t) and g(i,t) are two traces of the ith block obtained at two sensors (A and B) with separation Δx, then the fast fourier transform (FFT) of these two functions (waveforms) for each block can be expressed in the frequency domain as F(i,ω) and G(i,ω). Th erefore, complex coherence (COH) for ith block is calculated as (1) where CCfg(i,ω) is the crosscorrelation of two traces F(i, ω) and G(i, ω) and Af(i,ω) and Ag(i, ω) are the autocorrelations of F(i, ω) and G(i, ω), respectively. Th e spatial autocorrelation SPAC is defi ned as the real part of the averaged complex coherences:
(2) where n denotes the number of blocks. Coherence (COH) was calculated for each block and then the real parts of all blocks were averaged to obtain the SPAC. Ten to 100 blocks were averaged for calculating fi nal SPAC. If we assume that microtremor propagates in all directions equally, the SPAC forms a Bessel function of the fi rst kind, zero order (Aki, 1957). (3) where, c(ω) is phase velocity at angular frequency ω and J0 is the fi rst kind and zero order of the Bessel function. Th e velocity that minimizes the error in Equation 3 can be considered as the phase velocity at the angular frequency ω .
1957).
1957).
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Th e S-wave velocity profi les ob-tained using 2ST-MAM were com-pared with existing borehole velocity logs. Th e seismic velocity logs at the Carson, South Gate, and Willow-brook sites were acquired as part of the ROSRINE (Resolution of Site Response Issues from the Northridge Earthquake) project and until re-cently were available at http://geoinfo.usc.edu/rosrine. Th e velocity log as-sociated with the Manhattan Beach site was acquired by the USGS (Dan Ponti, written communication) at a location several kilometers north of the 2ST-MAM array.
P- and S-wave velocity borehole measurements were made by OYO Corporation’s suspension PS logging system. Th is system directly deter-mines the average velocity of a 1-m segment of the lithologic column im-mediately surrounding the boring. Th is measurement is the elapsed time between the arrival of a controlled-source wave propagating upward through the rock/soil column and between a pair of receivers separated by rubber isolation tubes. Depths of the velocity logs were 250 m at the South Gate (Downey), Willowbrook, and Manhattan Beach sites and 350 m at the Carson (Dolphin Park) site. Th e approximate locations of the borehole velocity logs are close to the measurement areas, except for the Manhattan Beach site, which is sev-eral kilometers from the area of mea-surements (Figure 1).
Th e S-wave profi les were also compared with a 3D seismic veloc-ity model for Southern California: the Southern California Earthquake Center Community Velocity Model (SCEC 4.0; http://www.data.scec.org/research-tools/3d-velocity.html). Th is velocity model was primarily based on geologic models, empirical rela-tionships between seismic velocity, sediment age and depth of burial, and existing surface and borehole geophysical data.
Comparisons of S-wave velocity profi les in the deep (left) and shallow (right) regions from the 2ST-MAM method with the velocity logs and the SCEC 4.0 model suggest good agree-ment between the various data sets (Figure 9). S-wave velocities obtained using the 2ST-MAM are reasonably matched with the
Figure 4. Example of spatial autocorrelation and phase-velocity images at the Manhattan Beach site. Th e left side of the fi gure shows the low-frequency component which refl ects S-wave velocity structure down to several kilometers, whereas the right side shows the high-frequency component refl ecting velocity structure less than about 100 m. (a) Examples of frequency-dependent coherences comparing larger sensor spacing (left) and smaller spacing (right). (b) Typical coherences as a function of sensor distance with theoretical Bessel functions calculated for phase velocities that yield minimum error between the observed coherences and the theoretical Bessel function. Th e symbols indicate observed coherences and solid lines indicate the theoretical Bessel functions. (c) Error between observed coherences and theoretical Bessel functions with magenta indicating large error and blue indicating small error. Red dots indicate minimum-error phase velocities at each frequency and they can be considered as the observed dispersion curves.
velocity logs when considering the resolution diff erence between surface-wave methods and borehole velocity data. At the Man-hattan Beach site, S-wave velocities recorded on the borehole ve-locity log are slightly faster than that of the 2ST-MAM, which is likely associated with the physical separation between the veloc-ity log and surface-wave measurement area.
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It is not reasonable to establish the accuracy of the 2ST-MAM results by directly comparing them to the SCEC model, which was constructed from interpolating sparsely distributed geophysical and drilling information with geolog-ic information. Here, we compare the 2ST-MAM with the SCEC model from an approximate basin structure perspec-tive. In general, the S-wave velocity profi les developed using 2ST-MAM agree with the SCEC model.
If we compare S-wave velocity at a depth of 3000 m from the SCEC model at the Carson and South Gate sites, the Carson site clearly has higher velocity than the South Gate site. Th is is consistent with the 2ST-MAM velocity models and the fact that the South Gate site is in the deepest part of the LA Basin. At the Manhattan Beach site, S-wave velocity obtained using the 2ST-MAM is clearly lower than that in the SCEC model in the depth range of 300–2500 m. Th e site is along the coast and this diff erence may be because of the inaccuracy of the SCEC model. Th is example implies that the 2ST-MAM can complement large 3D velocity models such as the SCEC model.
ConclusionsTwo-station microtremor array measurements (2ST-MAM) were performed at four sites in the south-western portion of the Los Angeles Basin, California to estimate deep S-wave velocity structure and evalu-ate the applicability of the method to such investigations. Resultant S-wave velocity profi les were favor-ably compared to existing velocity logs and a 3D seismic velocity mod-el. Our investigation results imply that the 2ST-MAM can accurately detect Rayleigh-wave phase veloci-ties down to a frequency of 0.2 Hz and penetrate to a depth of 2–3 km. Th ese results have shown that using
Figure 5. Comparison of dispersion curves obtained by MASW, small arrays, and 2ST-MAM (Carson) together with corresponding wavelength.
Figure 6. Comparison of observed dispersion curves together with corresponding wavelength.
Figure 8. Comparison of the S-wave velocity profi les in both the deep (left) and shallow (right) regions obtained by the inversion.
Figure 7. Comparison of observed and theoretical dispersion curves. Red solid line with white circles indicates observed dispersion curve. Solid and broken lines indicate theoretical dispersion curves and their relative amplitude (medium response) respectively. Yellow circles indicate the eff ective mode of theoretical phase velocities.
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the 2ST-MAM method is applicable to both deep and shallow investiga-tions.
As mentioned earlier, the 2ST-MAM is based on the assumption that microtremors propagate in all directions equally. Investigation re-sults described in this article together with examples in other basins imply that assumption is valid in large tec-tonic basins along the west coast of the United States.
Microtremor measurements using a large number of sensors are much bet-ter than measurements using only two sensors. Th e 2ST-MAM, however, is much more effi cient and cost-eff ective compared to conventional microtrem-or array measurements using many sen-sors. Considering that the demand for precise S-wave velocity models in urban areas is increasing, the method present-ed here can play an important role for such investigations.
ReferencesAki, K., 1957, Space and time spectra of
stationary stochastic waves, with spe-cial reference to microtremors: Bulletin of the Earthquake Research Institute, 35, 415–456.
Hatayama, K. and E. Kalkan, 2012, Long-period (3 to 16 s) ground motions in and around the Los Angeles Basin during the Mw 7.2 El Mayor-Cucapah earthquake of April 4, 2010: Presented at 15th World Conference on Earth-quake Engineering.
Hayashi, K. and D. Underwood, 2012a, Estimating deep S-wave velocity struc-ture using microtremor array mea-surements and three-component mi-crotremor array measurements in San Francisco Bay Area: Proceedings of the Symposium on the Application of Geophysics to Engineering and Envi-ronmental Problems.
Hayashi, K. and D. Underwood, 2012b, Microtremor array measurements and three-component microtremor measure-ments in San Francisco Bay Area: Presented at the 15th World Confer-ence on Earthquake Engineering.
Hayashi, K., R. Cakir, and T. Walsh, 2013, Using two-station mi-crotremor array method to estimate shear-wave velocity profi les in Seattle and Olympia, Washington: Proceedings of the Symposium on the Application of Geophysics to Engineering and Environmen-tal Problems.
Margaryan, S., T. Yokoi, and K. Hayashi, 2009, Experiments on the stability of the spatial autocorrelation method (SPAC) and linear array methods and on the imaginary part of the SPAC coeffi cients as an indicator of data quality: Exploration Geophysics, 40, no. 1,
Figure 9. Comparison of S-wave velocity profi les in deep (left) and shallow (right) regions with the seismic logs and the SCEC 4.0 model.
121–131, http://dx.doi.org/10.1071/EG08101.Okada, H., 2003, Th e microtremor survey method: SEG, http://
dx.doi.org/10.1190/1.9781560801740.Suzuki, H. and H. Yamanaka, 2010, Joint inversion using earthquake
ground motion records and microtremor survey data to S-wave profi le of deep sedimentary layers: Butsuri Tansa, 65, 215–227 (in Japanese).
Yamanaka, H. and J. Ishida, 1995, Phase-velocity inversion using genetic algorithms: Journal of Structural and Construction Engi-neering, 468, 9–17 (in Japanese).
Corresponding author: [email protected]
AREAS OF EXPERTISE
Unconventional Reservoirs
Challenging Environments
Complex Geologies
Basin Exploration
Reservoir Exploitation
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Love waves from local traffi c noise interferometry
Surface-wave interferometry on local scale usually aims at recovering Rayleigh waves. Th is is because of the
predominant use of vertical component geophones in exploration seismology and the fact that Rayleigh waves occur for any given subsurface structure. On the other hand, Love waves are present only in layered media and require horizontal component geophones for their observation. As they depend on shear-wave velocity structure and density only, the analysis of Love waves provides a potentially powerful supplement to Rayleigh wave inversion. Perhaps surprisingly, recent studies show that low-frequency Love waves (0.05–0.1 Hz) excited by the interaction of ocean waves with the ocean fl oor (the Earth’s microseism) can be recovered by interferometry, and that their S/N is high compared to Rayleigh waves (Lin et al., 2008). On a regional scale, Jay et al. (2012) analyzed the ambient noise fi eld in a volcanic region and found that Love waves with frequencies of about 0.3 Hz are observed more clearly than corresponding Rayleigh waves. In this article, we show that Love waves in the frequency band of 1.5 to 5 Hz can be obtained from local noise interferometry, and that they are of comparable S/N as Rayleigh waves. Th us they may also be used to constrain the near-surface structure.
Love waves in a nutshellLove waves are horizontally polar-ized because they result from interac-tion of shear (SH) waves. As opposed to Rayleigh waves, Love waves exist in layered media only. For the one-layer case, the Love wave represents the superposition of multiply, criti-cally refl ected downgoing SH waves from the bottom of the layer (e.g., Stein and Wysession, 2003). Th e lay-er of a thickness H is then considered as a wave guide and the Love-wave velocity cL is inbetween the shear-wave velocities of the layer and the half-space (Figure 1). In contrast, Rayleigh-wave velocities are always less than the layers shear-wave ve-locity. Th e dispersion relation shows that Love-wave velocities at low fre-quencies tend toward the half-space
M. BEHM and R. SNIEDER, Colorado School of Mines
velocity β2, while observations at high frequencies give the layer velocity β1. Equation 1 relates the Love-wave velocity cL to its frequency f, layer thickness H, layer and half-space shear-wave velocities β1, β2, and densities ρ1, ρ2:
(1)
Figure 1. Love-wave phase velocity as a function of frequency and layer thickness H for a layer-over-half-space model (dispersion relation). β and ρ refer to shear-wave velocity and density, and ic is the critical angle. Th e shown raypath is not the actual raypath of the Love wave, but schematically describes critically refl ected SH waves. Th e dashed gray line is the wavefront of the downgoing SH waves which interfere to constitute the Love wave at the surface point P.
Figure 2. Th e deployment of the La Barge Passive Seismic Experiment in southwestern Wyoming. White dots indicate locations of three-component instruments. Th e black line represents the state road contributing dominantly to the ambient noise. Th e Hogsback thrust is the main structural feature and separates carbonate outcrops in the west from siltstones and sandstones in the east.
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densities, also the layer thickness. Eslick et al. (2008) examine the constraints on the subsurface settings and re-cording parameters for successful retrieval of Love-wave dis-persion. In case of several layers, the shear-wave velocities of the layers can be determined analogously to Rayleigh waves (Xia et al., 2009). From a practical point of view, it is inter-esting to note that even for the one-layer case the dispersion
relation is highly nonlinear and a so-lution for Love-wave velocity must be determined numerically. Solu-tions for multilayer-models and later-ally varying layer thicknesses require more computational eff ort (e.g., Ben-Hador and Buchen, 1999). On the other hand, the nonlinearity of the dispersion relation can be employed to impose constraints on shear waves, densities, and layer thickness, pro-vided the Love-wave velocity can be reliably observed over an appreciable frequency range.
Data and interferometric processingTh e La Barge Seismic Experiment is a industry-academia cooperation aiming at evaluating the feasibility of passive seismology for local subsur-face characterization (Saltzer et al., 2011). From November 2008 to June 2009, 55 3C broadband stations were deployed at a spacing of 250 m in an active hydrocarbon production site in southwestern Wyoming (Figure 2). Th e continuous recordings and the small aperture of the array make the data set well suited for local inter-ferometry analyses. Previous investi-gations (Behm et al., accepted) show that both Rayleigh- and Love-wave velocity information are obtained from traffi c noise originating from a state road. We fi rst summarize their approach and their most important fi ndings, and then discuss the Love waves in more detail.
Each of the stations is turned into a virtual source by correlating its am-bient noise recording with the am-bient noise recording of every other station. It turns out that fi ve days of continuous noise data are suffi cient to recover surface waves travelling be-tween the stations up to distances of 5 km. Analysis further shows that traf-fi c activity from the Wyoming state road 235 in the eastern part of the deployment provides the main source
Equation 1 illustrates that, opposed to Rayleigh waves, Love-wave velocities do not depend on compressional-wave veloci-ties. Th e use of Love waves thus reduces the ambiguity inher-ent in inversion for shear-wave structure. If the assumption of the one-layer case is well justifi ed, the dispersion relation potentially enables us to estimate the shear-wave velocities of both the layer and the basement, and by further assuming
Figure 3. Interferograms for virtual source L17 (red star) and stations L42 to L55 (red dots). Th e lack of causal energy results from the receiver stations being closer to the noise source (WY state road 235) than the virtual source. Dashed red lines indicate linear moveouts for velocities of 1500, 2000, and 2500 m/s. Note the higher apparent phase velocity at the transverse component (T) compared to vertical (Z) and radial (R) components. Th e black rectangle depicts the waveform from which the dispersion curve for raypath “A” (Figure 6) is calculated.
Figure 4. Common-off set stacks of Hilbert-transformed interferograms from 530 virtual-source receiver combinations in the central part of the investigated area. Note the overall similarity of the vertical (Z) and radial (R) components, and the higher apparent group velocity of the transverse (T) component data.
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of coherent noise. The location of the road with the respect to the deployment ensures a large number of stationary phase points such that most interferograms feature clear surface-wave arrivals with high S/N. Rayleigh waves are polarized in the plane defined by the vertical and the propagation direc-tion, and Love waves are polarized in the horizontal plane and perpendicular to the propagation direction. By rotating the horizontal components into the radial (virtual source—
Figure 5. Near-surface phase-velocity maps and their ratio as obtained from traveltime tomography of vertical and transverse component interferograms (from Behm et al. accepted, slightly modified). The arrows (A, B) denote the virtual source-receiver pairs for which dispersion curves are shown in Figure 6.
receiver azimuth) and transverse (90° clockwise to the azi-muth) directions, it is possible to separate Love and Rayleigh waves. The slower Rayleigh wave is present on the vertical and radial components, and the faster Love wave appears on the transverse component (Figure 3). Although of overall high S/N, the interferograms are characterized by a limited bandwidth peaking at 2.5 Hz, and subsequently a sometimes ringy wavelet. The calculation of the envelope (modulus of
the Hilbert-transformed interfero-gram) compresses oscillating wavelets and improves the delectability of the onset of the waves. It is important to note that envelope interferograms no longer represent phase velocities, but group velocities instead. All envelope interferograms from the central part of the investigated area are further stacked in offset bins (Figure 4). Al-though lateral velocity variations may degrade the stacks, these results also support the existence of both Ray-leigh and Love waves. Vertical and radial component data appear simi-lar with respect to apparent velocity and maximum offsets (2500–3500 m), while transverse component data feature higher apparent velocity and also more consistent arrivals at large offsets (3500–5000 m).
Dispersion of surface waves en-ables us to invert for shear-wave veloc-ity structure. In exploration seismol-ogy and near-surface investigations, the multichannel analysis of surface waves (MASW) is commonly applied to obtain Rayleigh-wave phase-veloc-ity dispersion (Park et al., 1999). The relatively low central frequency of the obtained traffic noise interferograms in conjunction with the station spac-ing hampers the observation of phase velocity dispersion, while the large number of clear surface-wave arrivals allows inverting picked traveltimes for laterally varying group and phase ve-locities. Vertical component traveltimes provide Rayleigh-wave velocities, and transverse component traveltimes are inverted for Love-wave velocities (Fig-ure 5). With respect to the wavelength, those velocities represent average sur-face-wave velocities from the upper 100–300 m. The results correlate well with the surface geology as the carbon-ates west of the Hogsback thrust are represented by relatively high velocities. Lateral resolution of the velocity maps
Figure 6. Group velocity dispersion curves obtained from frequency-time analysis for two virtual source—receiver pairs (A: L46 > L17; B: L55 > L31). The white curve depicts the maximum amplitude at each frequency. Note the overall similarity of the vertical (Z) and radial (R) components, and the higher velocity and different appearance of the transverse (T) component. The gray bar shows the average phase velocity (Figure 5) along the raypath.
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depends on ray coverage and picking accuracy, and we estimate it to range between 500–1000 m.
Group velocity dispersion of Rayleigh and Love wavesIn contrast to the MASW method, the relatively sparse dis-tribution of broadband instruments on continental scale led to methods to derive group velocity dispersion between station pairs based on the frequency-time analysis (FTAN; Dziewonski et al., 1969; Levshin et al., 1989). With this ap-proach, the surface wave travelling between two stations is reconstructed by interferometry. Th e interferogram is fi ltered in diff erent frequency bands, where each band is defi ned by a Gaussian function of a central frequency and given half-width. After fi ltering, the envelope of the trace is calculated. By knowing the off set between the two stations, the time axis of the trace is converted to velocity and the maximum of the envelope is picked for each central frequency. Th e ob-tained group velocity dispersion curve can then be inverted for a shear-wave velocity-depth function representing the region between the stations. If station coverage is dense, a tomographic approach for a 3D shear-wave velocity model is also feasible. Th is method has been applied successfully to ambient noise from globally distributed earthquakes to delineate crustal and mantle structures (e.g., see the overview given by Bensen et al., 2007). As with MASW, the inversion for shear-wave velocities assumes a layered 1D model. Th e evident lateral variation in the investigated area limits the
general applicability of the FTAN algorithm, but nonethe-less we are able to calculate group velocity dispersion curves for selected receiver pairs (Figure 6). To minimize the con-tribution of spurious energy, we mute the interferograms for apparent velocities larger than 4000 m/s and less than 1000 m/s prior to the dispersion analysis. Th e raypath “A” com-prises the stations L46 (virtual source) and L17 (receiver) in the high-velocity carbonates. As virtual source and receiver are interchangeable, the actual surface wave used for the cal-culation of the dispersion curve is seen in the acausal part of Figure 3. In contrast, the raypath “B” connects the stations L55 (virtual source) and L31 (receiver) in the low-velocity eastern part.
Vertical and radial component dispersion curves appear similar with a gentle tendency of lower velocities toward higher frequencies, while the dispersion of the transverse component with its steep slope toward the low-frequency end resembles typical Love-wave dispersion characteristics (com-pare with Figure 1).
Group velocities UG and phase velocities UP are related by
. (2)
For a given frequency f and realistic velocities, group ve-locities are less than phase velocities if the phase velocities
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decrease with frequency (dUp/df < 0) and vice versa. The mag-nitude of the difference between group and phase velocities is inversely proportional to the phase velocity and to the rate of change of phase velocity with frequency. The observations for ray path “A” qualitatively agree with this general relation of phase and group velocities in that sense that group veloci-ties are less than phase velocities. In case of raypath “B”, the group velocities surprisingly appear higher than the phase velocities. However, it is noted that raypath “B” crosses the region with the strongest lateral variation, representing the westward dipping low-angle Hogsback thrust where a gradu-ally thickening sheet of high-velocity carbonates overthrusts low-velocity sediments. This definitely represents a challenge to the simplifications inherent to both traveltime inversion and dispersion interpretation, and also illustrates limits to surface-wave inversion.
OutlookOur study shows that locally excited Love waves in a typical exploration environment and in the frequency range of 1.5–5 Hz are of comparable, if not higher S/N than Rayleigh waves. This frequency range does not necessitate costly broadband stations as in our test study, but can be well targeted by low-frequency geophones more commonly used in exploration seismology. With more and more industrial applications rely-ing on three-component instruments (e.g., seismic monitor-ing, shear-wave retrieval), the recording and potential use of Love waves for subsurface characterization becomes feasible. In particular passive seismic deployments are well suited, as surface waves can be efficiently recovered from interferom-etry applied to local ambient noise. Love waves are enticing because, compared to Rayleigh waves, they do not depend on P-wave velocity and thus reduce the ambiguity in extracting shear-wave velocity structure. Near-surface shear-wave veloc-ity inversion zones might by quickly mapped by the absence of Love waves. The complementary information of Rayleigh and Love waves provides improved assessment of seismic ve-locities and densities. Combined dispersion measurements of Love and Rayleigh might also be used to constrain lateral variations in Earth structure (Levshin and Ratnikova, 1984) and seismic anisotropy (Montagner and Nafaf, 1986). In case when the near surface is sufficiently described by a one-layer model, the distinct shape of the Love-wave dispersion curve could facilitate to estimate layer and half-space shear-wave velocities and densities simultaneously.
ReferencesBen-Hador, R. and P. Buchen, 1999, Love and Rayleigh waves in
non-uniform media: Geophysical Journal International, 137, no. 2, 521–534, http://dx.doi.org/10.1046/j.1365-246X.1999.00790.x.
Bensen, G. D., M. H. Ritzwoller, M. P. Barmin, A. L. Levshin, F. Lin,
M. P. Moschetti, N. M. Shapiro, and Y. Yang, 2007, Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements: Geophysical Journal Interna-tional, 169, no. 3, 1239–1260, http://dx.doi.org/10.1111/j.1365-246X.2007.03374.x.
Behm, M., R. Snieder, and G. M. Leahy, Retrieval of local surface wave velocities from traffic noise—an example from the LaBarge basin (Wyoming): accepted for publication in Geophysical Pros-pecting.
Dziewonski A. M., S. Bloch, and M. Landisman, 1969, A technique for the analysis of transient seismic signals: Bulletin of the Seismo-logical Society of America, 59, 427–444.
Eslick, R., G. Tsoflias, and D. Steeples, 2008, Field investigation of Love waves in near-surface seismology: Geophysics, 73, no. 3, G1–G6, http://dx.doi.org/10.1190/1.2901215.
Jay, J. A., M. E. Pritchard, M. E. West, D. Christensen, M. Haney, E. Minaya, M. Sunagua, S. R. McNutt, and M. Zabala, 2012, Shal-low seismicity, triggered seismicity, and ambient noise tomography at the long-dormant Uturuncu volcano, Bolivia: Bulletin of Vol-canology, 74, no. 4, 817–837, http://dx.doi.org/10.1007/s00445-011-0568-7.
Levshin, A. L., and L. I. Ratnikova, 1984, Apparent anisotropy in in-homogeneous media: Geophysical Journal of the Royal Astronom-ical Society, 76, no. 1, 65–69, http://dx.doi.org/10.1111/j.1365-246X.1984.tb05022.x.
Levshin, A. L., T. B. Yanovskaya, A. V. Lander, B. G. Bukchin, M. P. Barmin, L. I. Ratnikova, and E. N. Its, 1989, Seismic surface waves in a laterally inhomogeneous Earth: Norwell.
Lin, F., M. P. Moschetti, and M. H. Ritzwoller, 2008, Surface wave tomography of the western United States from ambient seismic noise: Rayleigh and Love wave phase velocity maps: Geophysical Journal International, 169, 1239–1260.
Montagner, J. P. and H. C. Nataf, 1986, On the inversion of the azimuthal anisotropy of surface waves: Journal of Geophysi-cal Research, 91, B1, 511–520, http://dx.doi.org/10.1029/JB091iB01p00511.
Park, C. B., R. D. Miller, and J. Xia, 1999, Multichannel analysis of surface waves: Geophysics, 64, no. 3, 800–808, http://dx.doi.org/10.1190/1.1444590.
Saltzer, R., G. M. Leahy, J. Schmedes, J. Roth, and E. Rumpfhuber, 2011, Earthquakes—A naturally occurring source of low frequen-cy data: 81st Annual International Meeting, SEG, Expanded Ab-stracts, 3689–3693, http://dx.doi.org/10.1190/1.3627967.
Stein S. and M. Wyssesion, 2003: An introduction to seismology, earthquakes, and earth structure: Blackwell publishing.
Xia, J., R. Cakir, R. D. Miller, C. Zeng, and Y. Luo, 2009, Estima-tion of near-surface shear-wave velocity by inversion of Love waves: 79th Annual International Meeting, SEG, Expanded Abstracts, 1390–1394, http://dx.doi.org/10.1190/1.3255109.
Acknowledgments: This work was funded by ExxonMobil. We thank Matt Haney for reviewing the manuscript. IRIS DMC was used to access the waveform data.
Corresponding author: [email protected]
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Surface-wave observations after integrating active and passive source data
Empirical Green’s function (EGF) retrieval and turning ambient noise into useful signal by crosscorrelation or
seismic interferometry (Curtis et al., 2006) has been a popular topic in recent years in the seismological community. Many have discussed how the interstation distance or its equivalent affects the accuracy of the Green’s function that can be retrieved by crosscorrelation of long-range noise between two stations (e.g., Snieder, 2004; Bensen et al., 2007; Halliday and Curtis, 2008; Tsai, 2009; Kimman and Trampert, 2010).
It is generally accepted that the necessary or optimum in-terstation distance strongly depends on source distribution, length of records (and, hence, is naturally related to source distribution), and the duration of the Green’s function to be retrieved. Noise generated by a surface source can be efficiently used to reconstruct the Green’s function of surface waves if we focus on the accuracy of the phase, and not be too concerned with the amplitude accuracy of the retrieved Green’s function (Halliday and Curtis, 2008; Kimman and Trampert, 2010).
Free-space theory for surface source distributionNoise sources that contribute to the retrieved Green’s func-tion resulting from the crosscorrelation of two stations, A and B, in the xoy plane that are extracted from an acoustic free space with a velocity c, must distribute on a hyperbola (Figure 1) (Roux et al., 2005).
The acute angle θ formed by the asymptote with the x-axis in the first quadrant is
(1)
It is obvious that d → D if θ → 0 . The angle γ formed by connecting any point S on the hyperbola to the origin will be less than θ.
Constructive interference will occur between records of receivers A and B when
, n ∈N
as defined by the first Fresnel zone, where λ denotes wave-length of a plane wave with angle frequency ω for a random source S. On the other hand, from Equation 1, the following inequality always holds in the first quadrant,
(2)
Yixian xu, Baolong Zhang, Yinhe luo, and Jianghai xia, China University of Geosciences
Figure 1. Hyperbola defined by Equation 1 with foci A and B. The acute angle θ formed by the asymptote of a hyperbola with the x-axis in the first quadrant.
Figure 2. Coverage angle versus minimum interstation distance. Mask (75.6˚, 1) means the interstation distance is at least one wavelength when the noise coverage angle approaches 75.6˚.
Equation 2 states that the minimum interstation distance D is determined by the coverage angle γ (and, hence, its upper limit θ) and the wavelength λ. The smaller the angle γ, the smaller the interstation distance D could be (Figure 2). Based on a rigorous analysis for realistic medium by Tsai (2009), the far-field plane wave or first term approximation (Equation 12 in that paper) of the measured delay time τ between two stations
tan cos
cos cos cos
cos
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will be related to coverage angle and interstation distance as
. (3)
If the delay-time τ between two stations is measurable only if τ is greater than a quarter of the period T, (i.e., D cos γ / c ≥ T / 4), Equation 2 has value.
As suggested by Bensen et al. (2007), the empirical minimum interstation distance must be larger than three
Figure 3. Dispersion images derived from the same shot gather: (a) spread length of 24 m (≈ one wavelength at 10 Hz); (b) spread length of 12 m (≈ half wavelength at 10 Hz); (c) spread length of 6 m (≈ one-fourth wavelength at 10 Hz). The model is a top layer with thickness 10 m underlying a half-space. The S-wave velocities of the top layer and the half-space are 200 m/s and 800 m/s, respectively. The wavelet peak frequency is 10 Hz. The dispersion image is calculated from a shot gather with 0.5-m receiver interval by the phase-shift method (Park et al., 1999) and the energy is normalized for every frequency. The dot line is the analytical result calculated by Schwab and Knopoff’s scheme (Schwab and Knopoff, 1972).
times the wavelength. This is related to the coverage angle of random noise and its general distribution everywhere in real world (Figure 1 and Figure 2).
Extension to multichannel analysis of surface waves (MASW)An interesting case results when all the sources are located at x axis (i.e., D = d) and γ = θ = 0. This source orientation is applicable to the multichannel analysis of surface waves (MASW) method when using an active source (Xia et al., 1999; Park et al., 1999; Xu et al., 2006). The minimum inter-station distance for an MASW spread will be roughly equiv-alent to λ / 4 (Equation 2). To demonstrate this relationship, a minimum spread necessary for extracting 10-Hz dispersive energy with 200 m/s phase velocity is 5 m. Please note that this result is for an inline, long-offset, random source that is independent of the source energy, attenuation, and degree of heterogeneity of the media that the surface wave passes through.
The resolution of the MASW dispersion image depends on the geophone spread not the geophone interval (Forbriger 2003; Xia et al., 2006), and in the frequency-velocity do-main, resolution increases with increasing geophone spread length (Figure 3). In spite of a drop in resolution in frequency versus phase velocity image as the spread length decreases, phase velocities can be accurately estimated at frequencies lower than the source peak frequency as long as the spread length is greater than one fourth wavelength. This is analo-gous to improvements in surface-wave EGF extracted from crosscorrelation of noise wavefields (Tsai, 2009) (Figure 6) obtained by increasing interstation distance. It is noted again
Figure 4. Shot gather (band-filtered within 1–30 Hz) generated seismic interferometry for a virtual source at the seventh trace.
cos
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dispersive energy image is actually sensitive to the spatially averaged properties of subsurface. This confirms the previ-ous statement that spatial resolution can be improved by us-ing narrow, denser arrays. Using a rough estimate of a 5-Hz Rayleigh-wave phase velocity of 1600 m/s at the base rock of the tunnel produces a wavelength is 320 m. It is clear that we can also get a good estimate of the dispersion-curve image even when the spread length approaches half a wavelength of the passive seismic energy.
Seventeen shot gathers were constructed from the entire data set. We used the total dispersion curve generated for each of the 17 shot gathers when inverting for each 1D S-wave velocity profile used to generate the 2D velocity cross section (Figure 6b). Two low-velocity layers (LVL) were revealed. The top LVL is continuously distributed from the midpoint of receivers 15 and 16, 60 m deep to the right up to about
Figure 5. Dispersion-energy images calculated by the phase-shift method from traces (a) 1–40 traces, (b) 1–23, and (c) 18–40.
that the spread length is consistent with interstation distance as defined in this case.
Increasing the spread length or interstation distance will decrease the spatial resolution of the MASW or EGF output. From the previous analysis, the upper limit of the horizontal spatial resolution (determined to be half the interstation dis-tance for surfaces waves) was one-eighth wavelength for the EGF extraction. This is true when all possible random noise sources are distributed inline with at least two stations (a con-straint also true for MASW). For MASW, incrementally mov-ing the effective receiver spread less than a spread length after every source recording (analogous to the multifold technique used in reflection seismic) allows spatial sampling points on sub-spread-length intervals. This process effectively improves spatial resolution by inverting dispersion curves derived from the intraspread records. Some good examples from the real world can be found in Miller et al. (1999).
An example of seismic interferometryFrom the passive seismic perspective, we might expect denser one- or two-dimensional seismic arrays comparatively im-prove spatial resolution by inverting dispersion curves from the extracted EGF for a profile or an area. This scheme should benefit from the partial duplication of information content within the subsurface. It is possible to recover the same in-formation content by inverting spatially denser dispersion curves. We hence report an example of ambient noise-based seismic interferometry.
We deployed a dense array consisting of 47 Texans 125A seismic recorders with 4.5-Hz vertical receivers across the base of a tunnel structure near Badong, a town on the southern side of the Yangtze River in the first section of Three Gorges. The tunnel was excavated through the core of Huangtuling landslide by China University of Geosciences to monitor any movement along the slide plane (Figure 6a). The array was 368 m in length with 8-m receiver intervals. The record length was consistently 26 hours. We used the standard seis-mic interferometry approach (e.g., Curtis et al., 2006) to ob-tain the shot gather generated by the virtual source.
We know from independent measurements that the S-wave velocity of the rock exposed at the base of the tunnel is between 1000 and 2000 m/s. The dominant Rayleigh waves evident in the shot gather have a frequency range from 3 to 8 Hz (Figure 4). Using the tau-p transform (McMechan and Yedlin, 1981), we generated an image of the dispersive en-ergy. For the shot gather depicted in Figure 4, the energy im-ages are calculated for traces 1-40 (Figure 5a), 1–23 (Figure 5b), and 18–40 (Figure 5c), corresponding spread lengths of 312, 176, and 176 m, respectively.
Consistent with previous observations, the resolution of the energy images decreases with decreasing the spread lengths. Energy images displayed in Figure 5b and Figure 5c are obviously different because the number of traces (ex-cept traces 18–23 traces) used to generate each is different. However, it is interesting to note that the energy image in Figure 5a (40 traces) can be constructed by simply averaging Figure 5b and Figure 5c. This result demonstrates that the
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20 m and potentially linked to the slide plane exposed at the tunnel base of receiver position 25. The second LVL can be found at the middle of the cross section approaching 100 m deep to the right endpoint up to 50 m deep. These LVLs of the S-wave are interpreted as the potential slide planes of the Huangtuling landslide.
ReferencesBensen, G., M. Ritzwoller, M. Barmin, A. Levshin, F. Lin, M. Mos-
chetti, N. Shapiro, and Y. Yang, 2007, Processing seismic am-bient noise data to obtain reliable broad-band surface wave dispersion measurements: Geophysical Journal International, 169, no. 3, 1239–1260, http://dx.doi.org/10.1111/j.1365-246X.2007.03374.x.
Curtis, A., P. Gerstoft, H. Sato, R. Snieder, and K. Wape-naar, 2006, Seismic interferometry-turn noise into signal: The Leading Edge, 25, no. 9, 1082–1092, http://dx.doi.org/10.1190/1.2349814.
Forbriger, T., 2003, Inversion of shallow-seismic wavefields: I. Wave-field transformation: Geophysical Journal International, 153, 720–734.
Halliday, D. and A. Curtis, 2008, Seismic interferometry, surface waves, and source distribution: Geophysical Journal Interna-tional, 175, no. 3, 1067–1087, http://dx.doi.org/10.1111/j.1365-246X.2008.03918.x.
Kimman, W. P. and J. Trampert, 2010, Approximations in seismic in-terferometry and their effects on surface waves: Geophysical Jour-nal International, 182, 461–476.
McMechan, G. A. and M. J. Yedlin, 1981, Analysis of dispersive waves by wave field transformation: Geophysics, 46, 869–874, http://dx.doi.org/10.1190/1.1441225.
Miller, R. D., J. Xia, C. B. Park, and J. Ivanov, 1999, Multichannel analysis of surface waves to map bedrock: The Leading Edge, 18, no. 12, 1392–1396, http://dx.doi.org/10.1190/1.1438226.
Park, C. B., R. D. Miller, and J. Xia, 1999, Multichannel analysis of surface waves: Geophysics, 64, no. 3, 800–808, http://dx.doi.org/10.1190/1.1444590.
Roux, P., K. G. Sabra, W. A. Kuperman, and A. Roux, 2005, Ambi-ent noise cross correlation in free space: theoretical approach: The Journal of the Acoustical Society of America, 117, no. 1, 79–84, http://dx.doi.org/10.1121/1.1830673.
Schwab, F. A. and L. Knopoff, 1972, Fast surface wave and free mode computations, in B. A. Bolt ed., Methods in Computational Phys-ics, Academic Press, 87–180.
Snieder, R., 2004, Extracting the Green’s function from the cor-relation of coda waves: a derivation based on stationary phase: Physical review. E, Statistical, nonlinear, and soft matter physics, 69, no. 4, 046610, http://dx.doi.org/10.1103/Phys-RevE.69.046610.
Tsai, V., 2009, On establishing the accuracy of noise tomography trav-el-time measurements in a realistic medium: Geophysical Journal International, 178, no. 3, 1555–1564, http://dx.doi.org/10.1111/j.1365-246X.2009.04239.x.
Figure 6. The tunnel location marked on the Google image (a), where the points mark receivers and the shaded section corresponds to the cross section and the inverted 1D S-wave velocity cross section along the tunnel base of Huangtuling landslide (b). The origin of the cross section locates at the midpoint of the receivers 15 and 16.
Acknowledgments: Y. Xu is supported by the National Natural Sci-ence Foundation of China (NSFC) under grant no. 40974079. Y. Luo is supported by the Special Fund for Basic Scientific Research of Central Colleges, China University of Geosciences (Wuhan) (#CUGL100402). And J. Xia is partly supported by the National Natural Science Foundation of China (NSFC), under grant no. 41274142.
Corresponding author: [email protected]
Nonreflection seismic and inversion of surface and guided waves
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Surface- and guided-wave inversion for near-surface modelingin land and shallow marine seismic data
In several domains of applied geophysics, surface, and guided waves are considered as a source of information
for characterizing the near surface, which in a marine environment includes the seabed. By contrast, in exploration seismic surveys, these waves have traditionally been regarded as coherent noise that should be filtered out as soon as possible. The authors consider that surface and guided waves are not noise but a signal that can be lifted from the seismic record and exploited for a variety of well-established geophysical solutions. Surface and guided waves constitute a large part of the recorded energy and with proper acquisition, analysis, and inversion they can be used to characterize the near surface with surprisingly high resolution. In this role, they can provide valuable information for tasks such as perturbation correction—adjustment for near-surface traveltime distortions. They can also be used for velocity and geological modeling. In this article, the authors discuss a workflow for the analysis and joint inversion of surface and guided waves in both land and offshore seismic data.
IntroductionThere is no satisfactory definition of “near-surface” in the context of ex-ploration seismic. It is often consid-ered as the shallow part of the sub-surface whose properties, although not directly of interest, can distort or otherwise degrade the observed response of deeper targets. It is fre-quently the part of the subsurface in which seismic coherent noise propa-gates. In this view, the near surface can be generally described as a lay-ered waveguide in which the upper boundary is the free surface and the lower boundary is the bottom of the weathering layer. A large part of the wavefield recorded in surface seismic consists of energy trapped in this waveguide, which manifests itself in the form of surface and guided waves.
Surface and guided waves con-sist of several modes of Rayleigh waves (Scholte waves in shallow water environments), Lamb waves (when strong velocity inversions are present), Love waves (on horizontal components when properly excited), Stoneley waves (that typically propa-gate along a solid-fluid interface, and, more rarely, a solid-solid interface),
Daniele Boiero, eDwarD wiarDa, and Peter Vermeer, WesternGeco
and guided P- and S-waves. In many cases, some of these modes may be present simultaneously and are superimposed on each other.
Analysis of surface waves is widely adopted for build-ing near-surface S-wave velocity models (Socco et al., 2010) and the method is under continuous and rapid evolution for exploration seismology applications (Strobbia et al., 2011); however, the use of guided waves is not yet considered a com-mon practice.
Although, from a theoretical point of view, “guided waves” is a general term that includes surface waves, the terminology adopted in the seismic exploration community refers to guid-ed waves as those events generated by multiple reflections in the near surface that propagate horizontally from the source and are recognized usually by a characteristic high-amplitude interference pattern that is often called shingling.
In exploration seismology, guided P- and S-waves are of-ten observed in surface seismic data (Muyzert, 2007). Figure 1a shows a characteristic land shot record. The wavefield is
Figure 1. (a) Example land shot gather with Rayleigh waves (A) and guided P- and S-waves (B). (b) Example shallow water OBC receiver gather with Scholte waves (C) and guided P-waves (D). (c) Example shallow water towed-streamer shot gather with Scholte waves (E) and guided P-waves (F). (d) f-k transform along the same line as (a). Event 1 is a Rayleigh-wave mode. Event 2 is a guided-wave mode. (e) f-k transform along the same line as (b). Events in the inset are Scholte-wave modes. Other events are guided wave modes. (f ) f-k transform along the same line as (c). Events in the inset are Scholte-wave mode. Other events are guided-wave modes.
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near-surface model can be used for short- to long-wavelength static and perturbation corrections, for velocity model build-ing, and for geotechnical applications.
Surface-wave analysisTh e common physical principle of diff erent surface-wave characterization methods is related to the fact that their propagation depends on their wavelengths, which in turn is responsible for the geometric dispersion—diff erent fre-quencies have diff erent phase velocities. Th e dispersion is strictly related to the site properties and can be inverted to a near-surface velocity model (Socco et al., 2010; Strobbia et al., 2011). For this reason, we analyze guided waves by looking at their main property: geometric dispersion. Sev-eral approaches have been developed for the analysis of sur-face waves in diff erent fi elds of application depending on the available data, ranging from sparse 3D networks for earth-quake seismology to small-scale linear arrays of geophones in geotechnical characterization (Socco et al., 2010). For the applications of interest in the refl ection seismic industry, a robust and fl exible implementation of surface-wave analysis techniques is needed (Strobbia et al., 2011).
Th e objective of the analysis is the extraction of the local wavenumber as a function of frequency, k(f ), for the diff er-ent modes. It can be observed that in a laterally gently vary-ing medium the gradient of the modal phase is essentially a surface-consistent parameter. If the waveform is obviously af-fected by the full propagation path, the kinematic properties of the surface wave, when excluding the near fi eld, can be ex-pressed in terms of local properties (Vignoli et al., 2011). At this stage, each location is considered one-dimensional and the local phase velocity can be inverted to obtain the vertical distribution of the near-surface velocities.
Estimation of phase velocities is done here following the approach proposed by Strobbia et al. (2011), which is based on the use of high-resolution, unevenly spaced f-k transforms (Figures 1d, e, and f ) to estimate the local properties of sur-face and guided waves within a patch of receivers. Th e analysis
complex because of the interference of refl ected and refracted multiples of P- and S-waves and of converted waves. Guided P-waves are disper-sive events that appear with relatively high phase velocities, which can ap-proach the moveout of refl ections at large off sets. Guided S-waves have slower phase velocities than guided P-waves and overlap the ground-roll cone; they are usually called higher-order Rayleigh modes on vertical component data. From a theoreti-cal point of view, Roth et al. (1998) showed that the Rayleigh waves are the superposition of normal modes and that shingled guided P-waves are the superposition of leaking modes (Haddon, 1984).
A shallow marine environment supports guided P-waves in the water layer. Th ey usually display a number of char-acteristic features: their dispersion patterns have a resonant frequency-tuned appearance; they have relatively high cut-off frequencies; and their phase velocities exceed the velocity of the water (Shtivelman, 2004). Figures 1b and 1c show ex-amples of an ocean-bottom cable (OBC) receiver gather and a towed-streamer shot gather that display two distinct groups of waves: the fi rst consists of low-frequency, low-velocity nor-mal modes (Scholte waves), and the second consists of leak-ing modes (guided P-waves) that have higher velocities and frequencies. When the subwater layers are composed of rela-tively soft saturated rocks with high Poisson’s ratio, the leak-ing modes can be approximated by guided acoustic waves.
Guided waves, jointly with the other types of surface waves mentioned above, can be used to obtain a near-surface velocity model for P- and S-waves as their propagation prop-erties depend directly upon the elastic properties of the near-surface (Roth and Holliger, 1999; Ritzwoller and Levshin, 2002; Klein et al., 2005; Muyzert 2007). In particular, guid-ed P-waves can provide constraints on the P-velocities where-as guided S-waves appear as higher surface-wave modes and are used for the estimation of S-velocities (Shtivelman, 2004; Boiero et al., 2009).
Diff erent dispersive modes can be analyzed together to build a reliable near-surface velocity model according to the following workfl ow:
1) Obtain a high-resolution spatial distribution of the modes’ properties, in particular the velocity
2) Invert the modes’ properties to a near-surface model
A surface-wave analysis and inversion method is proposed. It exploits the diff erent modes, including guided waves. Th is changes the perspective of coherent noise in the context of refl ection seismic; whereby, surface-wave analysis becomes part of the data processing workfl ow and surface and guided waves are considered as signal and not noise. Th e obtained
Figure 2. (a) Rayleigh-wave phase-velocity distribution along the line (event 1 in Figure 1d). (b) Guided-wave phase-velocity distribution along the line (event 2 in Figure 1d). Th e black contour line indicates the boundary between guided P-waves (below) and guided S-waves (above).
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workfl ow aims at the extraction of the local properties of the linear event of interest (surface- or guided-wave modes) and makes use of redundancy in the data to remove the eff ect of the propagation path from the source to the analysis point via extraction of the local average-phase gradient. Th e analysis can be run on source and receiver lines for typical 3D ac-
quisition geometries and results are merged into a volume representing the surface-wave properties within a survey. An example of continuous profi ling along a 12-km receiver line is shown in Figure 2. Th e main two modes identifi ed in Figure 1c (labeled 1 and 2) are extracted along the seismic line and shown in Figures 2a and 2b respectively. In this representa-tion, each vertical line represents the dispersion curve for that location.
Surface-wave inversionInversion of phase velocities at each location provides access to the medium velocities. Th ree challenges can be identifi ed with dispersion curve inversion, especially when looking at guided waves:
1) Curve inversion traditionally needs identifi cation of the order (nature) of the modes. Mode identifi cation is mainly limited by the bandwidth of recorded data, in particular at low frequencies. Furthermore, guided P-waves do not have a characteristic reference mode like the fundamental Rayleigh mode for S-waves.
2) Guided P-waves can interfere with converted-wave modes and guided S-waves. In this case, mode identifi cation is diffi cult because the dispersion curves split into short pieces.
3) Surface waves propagate as normal modes defi ned by the real-valued roots of the dispersion equation whereas guid-ed waves—especially P-guided waves—in many condi-tions propagate as leaking modes defi ned by the complex-valued roots of the dispersion equation (Aki and Richards, 1980), which can be diffi cult to deal with.
To address these challenges, we follow the approach pro-posed by Ernst (2007), which involves minimizing the de-terminant of the stiff ness matrix: an implicit function whose zeros are the solution of the secular function and correspond to modal curves. In particular, we consider the misfi t function proposed by Maraschini et al. (2010) based on the Haskell-Th omson matrix method adapted to take into account leak-ing modes (Boiero et al., 2009). Th is misfi t function allows surface and guided modes to be inverted without the need to associate experimental data points to a specifi c mode, thus avoiding mode misidentifi cation errors in the retrieved veloc-ity profi les.
Figure 3 schematically shows the inversion scheme. On its left hand side, black asterisks represent the dispersive events estimated for a synthetic example at a certain location. Sev-eral modes of Rayleigh waves and guided P-waves can be recognized: the two events with higher-phase velocities. Th e background orange shadows represent the real and complex solutions of the secular function for the velocity model on the right hand side.
Th e inversion algorithm modifi es S- and P-velocities in order to match the estimated dispersive events with the secu-lar function solutions. Th is approach does not need to de-scribe leaking modes as acoustic or pseudo-acoustic modes (Roth and Holliger, 1999; Shtivelman, 2004), which is a
Figure 3. Schematic representation of the inversion scheme. On the left side, black asterisks represent the dispersive events estimated for a synthetic example at a certain location. Orange shadows in the background are the real and complex solutions of the secular function for the velocity model on the right side. From top to bottom, the inversion algorithm modifi es the S- and P-velocities to match the estimated dispersive events with the secular function solutions.
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reasonable approximation when deal-ing with soft saturated rocks with high Poisson’s ratio but may not be appropriate in other cases (Roth and Holliger, 1999). Forbriger (2003) also describes a robust procedure that accounts for normal and leaky modes with their amplitudes without the need of approximations, but this approach could not be used here be-cause it does not allow for using the redundancy of the data to remove the eff ect of the propagation path from the source to the analysis point via extraction of the local average phase gradient.
It is worth noticing that diff er-ent modes (surface or guided) may be sensitive to diff erent parameters at diff erent depths of the near-surface velocity model (Liner 2012). Th is also reminds us that the parameter-ization, along with the regularization in the vertical and horizontal direc-tions and the use of a-priori informa-tion, is an important aspect of the inverse problem and should be evalu-ated for each particular case (Taran-tola, 2005). However, we shall not discuss this here.
In the following, we describe the possible surface and guided modes that can be found in land environ-ments and OBC and towed-streamer data from shallow marine environments; how to analyze them measuring their phase velocities; and how to invert them to build near-surface P- and S-velocity models.
Near surface in a land environmentTh e example data set was acquired using point receivers. Th e receiver line spans a distance of approximately 12 km. Figure 1a shows the complexity of the wavefi eld, including interfer-ence of refl ected and refracted multiples of P- and S-waves and of converted waves. Th e guided P-waves are the disper-sive events that appear with relatively high phase velocities (event B in Figure 1a) while the guided S-waves have slower phase velocities and overlap the ground-roll cone (mainly composed by Rayleigh waves indicated as event A). By ana-lyzing the local properties of surface and guided waves along the receiver line (Figure 2), we can immediately observe the high lateral variability of the phase velocities with an abrupt transition at a distance of 9000 m. By observing the Rayleigh-wave phase velocity behavior in Figure 2a, we can predict a velocity inversion in the right side of the line. Th is means that on the left we are dealing with a waveguide that is mainly supporting Rayleigh modes and guided P-waves propagating as leaking modes (Roth et al., 1998) while on
the right we are mainly observing the fl exural (Figure 2a) and extensional (Figure 2b) waves that characterize the propagation of pseudo-Lamb waves in stiff layers overlaying a weaker one (Ryden and Park, 2006).
Figure 4 shows the S-wave (4a) and P-wave (4b) velocity model along the receiver line. Th e two sections are inferred by inverting the phase velocities (Rayleigh and guided waves) es-timated at each location in Figure 2. Figure 4c shows the VP/VS ratio with values oscillating around 2—slightly higher than 2.5 for soft materials and lower than 2 for stiff layers—which is in agreement with published data (Ivanov et al., 2006). Figure 4d shows the solution of the secular function for two diff erent locations compared with the estimated phase veloci-ties. A good match exists, indicating that the depth velocity models are able to explain the phase velocity of the measured modes.
Th e near-surface model obtained by means of the inver-sion process gives geometric information about the near-sur-face layers and the geology and velocity distribution down to the investigation depth. Th e results of the inversion show rapid changes of weathering, compaction, and lithology in the near surface even within a single receiver line. Th ese varia-tions can be retrieved thanks to the high lateral resolution of surface and guided waves.
Figure 4. (a) Near-surface model obtained from phase velocities in Figure 2: P-wave. (b) Near-surface model obtained from phase velocities in Figure 2: S-wave. (c) VP/VS ratio. (d) Solutions of the secular function for two diff erent locations (719 m and 10,656 m distance, respectively) compared with the estimated phase velocities.
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Seabed in a shallow marine environmentSeismic data acquired in shallow waters often display well-de-fi ned dispersion patterns related to surface and guided waves. Figures 1b and 1c show minimally preprocessed hydrophone-component data from an OBC and towed-streamer sail line, respectively. Th e waves propagating as normal modes are rep-resented by a low-velocity, low-frequency wavetrain identi-fi ed with Scholte waves (events C and E). Th e high-frequency part of Scholte waves consists mainly of Stoneley waves local-ized in the vicinity of the liquid/solid interface, whereas at lower frequencies they consist of Rayleigh waves propagat-ing in the layers below the seabed (Shtivelman 2004). As the interface waves attenuate rapidly with increasing distance to the liquid-solid interface, the receivers have to be as close as possible to the seabed: OBC cables can easily record Scholte waves as well as towed streamers in shallow water. Numerous examples obtained in areas with various geological condi-tions using diff erent acquisition geometries show that most of the energy of the waves is localized within a narrow range of low frequencies limited to 2–20 Hz (Shtivelman, 2004). Th e phase velocities of Scholte waves are related to the shear-wave velocities below the water-bottom and can be inverted to estimate VS in the subwater layers.
Th e guided waves propagating as leaking modes are com-posed mostly of multiply refl ected P-waves, whereas their resonant character is because of the leaking of S-waves out-wards from the upper layers (events D and F in Figures 1b and c). By inverting the guided-wave dispersion curves, the vertical distribution of the P-wave velocity (VP) in the shal-low sub-water layers can be estimated.
Figure 5 shows an example of continuous profi ling along an OBC cable. Th e phase velocities for the two Scholte-wave modes and fi ve guided P-wave modes labeled in Figure 1e are extracted along the line and shown in Figures 5a and 5b re-spectively. In this representation, each vertical line represents the dispersion curve for that location.
Figure 6 shows the P-wave (Figure 6a) and S-wave (Fig-ure 6b) velocity model along the OBC cable. Th e two sec-tions are inferred by inverting the phase velocities estimated at each location in Figure 5. Figure 6c shows the VP/VS ratio with values ranging from 2.5 up to 9 in the shallower lay-ers, which are in agreement with published data (Ritzwoller and Levshin, 2002). Figure 6d shows the stack of the shal-low section superimposed with results from acoustic full-waveform inversion (FWI). Th e velocity models are in good agreement, highlighting the capability of guided P-waves for retrieving and characterizing lateral variations in the shallow sediments. From the P- and S- wave velocity models inferred from guided and surface waves, we can observe a penetra-tion depth for surface-wave inversion of about 200 m below the seabed. Th is could be used for velocity inversion imag-ing and the characterization of laterally variant complexities within this layer. Figure 7 shows the solution of the secular function for three diff erent locations compared with the esti-mated phase velocities. A good match exists, indicating that the depth model is able to explain the phase velocity of the measured modes.
Figure 5. (a) Scholte-wave phase velocity distribution for two diff erent modes along the line (events in the inset in Figure 1e). (b) Guided-wave phase-velocity distribution for fi ve diff erent modes along the line (events in Figure 1e).
Figure 8 shows an example of continuous profi ling along a towed-streamer line following the same approach as described for OBC data. Th e phase velocities for the one Scholte-wave mode and three guided P-wave modes labeled in Figure 1f are extracted along the line and shown in Figures 8a and 8b, respectively. Figure 9 shows the P-wave (Figure 9a) and S-wave (Figure 9b) velocity models. Th e two sec-tions are inferred by inverting the phase velocities estimated at each location in Figure 8. Figure 9c shows the VP/VS ratio with values ranging from 2 up to 8 in the shallower layers, which are in agreement with the previous example. Also in this case the solution of the secular function for three diff er-ent locations is compared with the estimated phase velocities (Figure 10). Th e depth model is able to identify the lateral variations that refl ect the variations of phase velocities of the measured modes.
ConclusionsIn exploration seismic surveys, surface and guided waves, which constitute a large part of the radiation of seismic sourc-es at the surface, can be analyzed and inverted to character-ize the near surface. Th e size and quality of modern seismic
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Figure 6. (a) Sea-bottom model obtained from phase velocities in Figure 5: P-wave. (b) Sea-bottom model obtained from phase velocities in Figure 5: S-wave. (c) VP / VS ratio. (d) Shallow stack and P-wave velocity model from acoustic FWI. Th e white line is a fi ducial marker and is not related to any interpretation.
Figure 7. Solutions of the secular function for the sea-bottom velocity model in Figure 6 at three diff erent locations (1347 m, 2847 m, and 4843 m distance, respectively) compared with the estimated phase velocities.
data sets enable the implementation of a robust workfl ow for the pro-cessing of surface waves, providing accurate estimates of their propaga-tion properties with high lateral and vertical resolution. By combining diff erent processing techniques, the lateral resolution can be adapted to the characterization objectives, from small-scale perturbation correction to long wavelength statics and ve-locity modeling. A proper inversion scheme can then infer S- and P-wave velocities from the propagation prop-erties of diff erent type of surface and guided waves that can be found in land data and shallow marine en-vironments, whether recorded by OBC, ocean-bottom nodes (OBN) or towed streamer.
Beside the use of the inverted velocity models to support static estimation and correction, mainly for land environments, surface- and guided-wave inversion has the capa-bility to enhance conventional shal-low velocity model building, depth imaging and FWI effi ciency and results. Th e assumptions typically underpinning current acoustic FWI methods are generally not justifi ed in the elastic near-surface environment, and surface-wave inversion may pro-vide P-wave velocity models that can be incorporated into the FWI initial model. In practice, the shal-lowest tens of meters are notoriously diffi cult to update using FWI. FWI often uses Gardner’s rule to obtain a density model, which is invalid in the near surface for unconsolidated sediments. Obtaining both P- and S-wave velocity models in the near surface through guided- and Scholte-wave inversion, respectively, may provide essential constraints on the wet bulk density of unconsolidated sediments, particularly in shallow marine environments. It is also note-worthy that surface-wave inversion is more cost-eff ective and less computationally intensive than FWI.
For multicomponent data, a further benefi t of accurate near-surface P- and S-wave velocity models is their poten-tial contribution to improved PP-PS matching, imaging and (joint) inversion. Generation of an accurate near-surface model enables imaging processes to more accurately account for diff erences in traveltimes and paths rather that reliance on
1D time statics. Th is will result in more accurate depth im-ages for both PS and PP data, better amplitude handling in off set/angle gathers, and hence greater confi dence in match-ing PS-PP events in migrated stacks and joint inversion using the results of amplitude versus off set (AVO) analysis of the gathers.
Th e inferred near-surface properties can also be used to design and optimize the coherent noise-fi ltering workfl ow,
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and can be used for local-adaptive fi lters. Model-based noise genera-tion can be used to predict the co-herent noise even beyond aliasing. Finally, interpretation of the inver-sion results can provide a robust geological, structural, and lithologi-cal model of the near surface from which geotechnical parameters and drilling hazards may be identifi ed.
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Figure 8. (a) Scholte-wave phase-velocity distribution along the line (events in the inset in Figure 1f ). (b) Guided-wave phase-velocity distribution for three diff erent modes along the line (events in Figure 1f ).
Figure 9. (a) Sea-bottom model obtained from phase velocities in Figure 8: P-wave. (b) Sea-bottom model obtained from phase velocities in Figure 8: S-wave. (c) VP/VS ratio.
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Figure 10. Solutions of the secular function for the sea-bottom velocity model in Figure 9 at three different locations (700 m, 2700 m, and 4850 m distance, respectively) compared with the estimated phase velocities.
Strobbia, C., A. Laake, P. Vermeer, and A. Glushchenko, 2011, Surface waves: use them then lose them. Surface-wave analysis, inversion and attenuation in land reflection seismic surveying: Near Surface Geophysics, 9, 503–514.
Tarantola, A., 2005, Inverse problem theory and methods for model parameter estimation: Society for industrial and applied math-ematics.
Vignoli, G., C. Strobbia, G. Cassiani, and P. Vermeer, 2011, Statisti-cal multioffset phase analysis for surface-wave processing in later-ally varying media: Geophysics, 76, no. 2, U1–U11, http://dx.doi.org/10.1190/1.3542076.
Xia, J., R. D. Miller, and C. B. Park, 1999, Estimation of near-surface shear-wave velocity by inversion of Rayleigh wave: Geophysics, 64, no. 3, 691–700, http://dx.doi.org/10.1190/1.1444578.
Acknowledgments: The authors thank Statoil and WesternGeco for permission to publish the data and WesternGeco for permission to publish this work. Thanks also to our former colleague Claudio Strobbia for valuable discussions and to Ayman Zaghloul for his help in processing part of the data.
Corresponding author: [email protected]
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Nonreflection seismic and inversion of surface and guided waves
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Exploring nonlinearity and nonuniqueness in surface-wave inversion for near-surface velocity estimation
With the rapid increase in prospecting for unconventional oil and gas, a large part of which remains on land,
the demand for land seismic data processing has increased substantially and is expected to further increase in the future. It is known that land seismic data are often of much poorer quality than marine seismic data. This is to a large extent caused by the presence of unconsolidated rock in the near surface with often complex velocity structure, which is absent in many marine settings. Such near-surface variations cause the wavefield to scatter or even lose its coherence as it propagates. This makes it difficult to accurately image the deeper-lying targets in land seismic data.
Moreover, the data contain surface waves that have sub-stantially larger amplitudes than the reflected body waves and as such are considered noise that hides part of the reflections needed for accurate imaging. Besides attempts to increase the data quality from a data-acquisition point of view, one can try to improve the imaging with a data-processing point of view and use the surface waves to invert for the near-surface (shear-) wave velocity structure (e.g., Xia et al., 1999). Once this velocity structure is known, it can be used to model the surface waves and subtract them from the data to increase the signal-to-noise ratio or, e.g., to calculate shear-wave receiver statics for converted-wave seismic imaging. In that way, the hampered imaging of the deeper lying targets can be improved.
Surface waves are sensitive to the Earth’s properties up to a depth of roughly one wavelength. Therefore, with observed fre-quencies typically somewhere between 3 and 30 Hz, assuming typically observed velocities, they can be used to invert for the
Huub Douma, ION Geophysical/GXT Imaging SolutionsmattHew m. Haney, U.S. Geological Survey
(shear-wave) velocity up to depths of 100–150 m (e.g., Ivanov et al., 2006; Muyzert, 2007). Recently Haney and Douma (2012) inverted group- and phase-velocity Rayleigh-wave dispersion curves for the near-surface shear-wave velocity using a pertur-bational approach applied to the forward method known as the thin-layer method (Lysmer, 1970; Kausel, 1999).
The forward problem of modeling dispersion curves for surface waves, however, remains nonlinear. In linearized in-versions, this inherent nonlinearity is evident because the sensitivity kernels are dependent on the model parameters, making the inversion dependent on the starting model. Through the mere acceptance of uncertainties in the data by fitting the data up to a certain tolerance, the problem also be-comes inherently nonunique. Even though linearized inver-sions are in the daily practice of exploration geophysics often the only practical option, they cannot deal with nonlinearity and nonuniqueness. To get a feeling for the nonlinearity and nonuniqueness of surface-wave inversion in an exploration geophysical context, we compare the results obtained from a linearized inversion with that of a nonlinear search technique. For this article, we illustrate our methods and results by in-verting a single dispersion curve obtained from field data. We focus on the inversion of the phase velocity only.
DataFigure 1a shows a receiver gather that was band-pass filtered between 2 and 15 Hz, with offsets between 50 and 300 m. By limiting the offsets to a narrow region around the receiver gather, spatial averaging or path effects are minimized, such
Figure 1. Band-pass filtered (2–15 Hz) common-receiver gather from a land data set with offsets between 50 and 300 m showing a fundamental-mode Rayleigh wave (a), and its associated phase-velocity spectrum (b). The white dotted line in (b) is the picked dispersion curve. Good signal in the phase-velocity spectrum was obtained only between 4 and 8.25 Hz.
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that the final inverted shear-wave velocity profile is indeed mostly associated with the local area around the receiver gather. The surface (Rayleigh) wave is clearly visible and can be used in a slant-stacking (or beamforming) procedure as a function of frequency to obtain the phase-velocity spectrum (Van der Kruk, 2007). Because of the irregular distribution of offsets and azimuths, the surface wave has a somewhat “ragged” appearance.
Figure 1b shows the associated velocity spectrum and the picked dispersion curve (white line). The dispersion curve was picked through finding the global maximum in the velocity spectrum and following the maximum in a search window in the direction of both increasing and decreasing frequency. In this way, mode jumping can be minimized, although this receiver gather does not contain higher modes beyond the fundamental mode. A good quality dispersion curve could be obtained only over a narrow bandwidth (here 4–8.25 Hz). The dispersion curve was picked at a frequency interval of 0.25 Hz.
Linearized inversion resultsFor the inversion, we chose a layer thickness for the forward modeling of 3 m. With a maximum frequency of 8.25 Hz in the dispersion curve and minimum observed phase velocity of about 350 m/s, this layer thickness is less than a tenth of the estimated short-est wavelength of 350/8.25 = 42 m. This ensures the accuracy of the thin-layer method used in the forward mod-eling (Kausel, 1999). We imposed an exponential smoothness constraint on the inversion using a model-covariance matrix with a 1/e length of 9 m (about a quarter of the smallest wavelength) and a model standard deviation of 34 m/s. The data standard deviation was set to 10 m/s for all frequencies.
The Rayleigh-wave phase-velocity is primarily sensitive to the shear-wave velocity. Therefore, in practice, only the shear-wave velocity can be reliably estimated in phase-velocity disper-sion-curve inversion. The sensitivity to the P-wave velocity and the density are taken into account by assuming a constant VP/VS ratio and using Gard-ner’s relation to relate density to the P-wave velocity.
To set up a linearized inversion based on the thin-layer method, a linearized perturbation analysis is used. Because the matrix in the obtained linear inverse rela-tion is sparse, we use the LSQR algorithm of Paige and Saunders (1982). Conver-gence is established when χ2 < 1.
We used two different starting models for our linearized inversion: a linearly increasing shear-wave velocity with depth (Figures 2a and 2b), and a smoothed version of a low-velocity layer on top of a half-space (Figures 2c and 2d). As can be seen in Figure 2e, both final models fit the data well (within one standard deviation because convergence was found for both models with χ2 < 1). When comparing both models (Figure 2f ), we note that the models differ mostly in the up-per 20 m and below 50 m. When looking at the update of the models as a function of iteration (Figures 2b and 2d), we notice that below 70–80 m both final models are not much different from their respective initial models.
Figure 3 shows the mode shapes for the fundamental-mode Rayleigh wave for both the horizontal and vertical par-ticle velocities, using the linear initial velocity model. It is clear from Figure 3 that below 70–80 m there is little sensitivity
Figure 2. Phase-velocity spectrum with superimposed modeled dispersion curves as a function of the iteration number for a linearized inversion using a linear starting model (a) and the related shear-wave velocity models (b). The starting model is indicated by the black dashed line in (b) and the associated dispersion curve is indicated by the black dashed line in (a). The different model updates are shown from the first iteration (dark green) to the final model in bright green. The associated dispersion curves in (a) have the same colors as the models in (b). (c) and (d) are similar to (a) and (b) but for a starting model that is a smoothed version of a low-velocity layer on top of a half-space. (f ) and (e) compare the final models obtained using both starting models and their associated dispersion curves, respectively. The white dashed line in (a), (c), and (e) is the measured dispersion curve.
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to the model parameters. This lack of sensitivity is caused by the absence of low frequencies (< 4 Hz).
The results from this linearized inversion emphasize the nonlinear character of the surface-wave disper-sion-curve inversion; the inversion can get stuck in a local minimum of the objective function, making the final model dependent on the start-ing model. Without knowledge of the uncertainties in the model pa-rameters, there is no way of knowing which model to choose, as both mod-els fit the data equally well (within one standard deviation, χ2 < 1). To try and get an idea of the uncertainty in the model parameters, we employed a nonlinear search method to search the model space for an ensemble of models that fit the data equally well.
Nonlinear search and the neigh-borhood algorithmThere are many different methods to search a model space, amongst which are genetic algorithms, neural net-works, and simulated annealing, to name but a few. Here we chose the neighborhood algorithm (NA) by Sambridge (1999). The choice of this algorithm was motivated by its sim-plicity in both parameterization and implementation as well as its previous use in surface-wave dispersion-curve inversion (e.g., Huang et al., 2010).
Figure 4 explains how the NA works for the simple case of a two-pa-rameter model space. The algorithm is initialized by a random distribu-tion of initial models in the model space (10 in this case). For each ini-tial model, the algorithm performs the forward modeling and calculates the associated misfit. Subsequently, it chooses the (user-defined) Nr models with the lowest misfits (two in Figure 4b, indicated by the green regions) and repopulates each nearest neigh-borhood region of these models with a (user-defined) number Ns of new models (one in Figure 4c, indicated by the red dots). For each new model, the associated misfit is calculated and again the Nr models with the low-est misfit are chosen (the orange re-gions in Figure 4d) and their nearest
Figure 4. Cartoon illustration of the neighborhood algorithm for a two-parameter model space. The algorithm is initialized with 10 random models (a), and Nr = 2 regions of lowest misfit are selected as shown by the green regions in (b) and each repopulated with Ns = 1 new model as shown by the red dots in (c). The algorithm proceeds by calculating the misfits of the newly added models and continues to again select the Nr = 2 regions of lowest misfit as shown by the orange regions in (d) and repopulate each of these regions with Ns = 1 new model as shown by the green dots in (d). The algorithm proceeds in the same fashion until a (user-defined) maximum number of iterations is reached.
Figure 3. Mode shapes of the fundamental-mode Rayleigh wave for the horizontal particle velocity (a) and the vertical particle velocity (b) for one of the starting models. It is clear that there is little sensitivity to the model parameters below about 70 m.
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neighborhood regions repopulated with Ns new models each. The algorithm proceeds onward in the same fashion until a maximum number of iterations is reached.
The NA thus zooms in on the regions of model space that most reduce the misfit. It is able to avoid getting trapped in a local minimum because it resamples multiple nearest neigh-borhood regions with the lowest misfits. As such, it has the po-tential to find a global minimum, although convergence to this minimum, as with most search algorithms, is not guaranteed.
To reduce the number of parameters, we enforced a regular-ized inversion grid where three consecutive layers have the same shear-wave velocity (and thus the same density and compres-sional-wave velocity). This gives an effective layer thickness of 3 × 3 = 9 m which is about 1/4 of the smallest (approximate) wavelength of 42 m. In this way, we reduce the size of the model space considerably while maintaining the necessary resolution. The forward modelling grid still has a smallest layer thickness of 3 m, ensuring the accuracy of the thin-layer method.
The NA was initialized with 2500 uniformly distributed random models. At each iteration, we decided to repopulate the Nr =100 best nearest neighborhood regions with two ad-ditional models, leading to a total of 200 added models per iteration. The total number of iterations was set to 50. This resulted in 12,500 models, shown by the gray lines in Figure 5c. The bounds of the search space are clearly visible. Figure 5a shows the velocity spectrum and the measured dispersion curve (white dashed line), as well as all the dispersion curves (gray lines) associated with each model in the total ensemble. It is clear that most models have associated dispersion curves that do not fit the data. For comparison, the models and their associated dispersion curves resulting from both linearized in-versions are shown.
We chose a subset of the generated ensemble that best fit the data. This resulted in an ensemble of 75 models shown by the black lines in Figure 5c. Their associated dispersion curves are shown in Figure 5b, and it is clear that these models fit
Figure 5. Ensemble of shear-wave velocity models (gray lines) generated by the NA (c) and their associated dispersion curves as shown by the gray lines in (a), and the ensemble of best 75 models as shown by the black lines in (c) and their associated dispersion curves as shown by the black lines in (b). Note that the ensemble of best shear-wave velocity models fit the data well (b). The white dashed line in (a) and (b) indicates the measured dispersion curve. For comparison, both models from the linearized inversion and their associated dispersion curves are shown by the blue and magenta curves in (b) and (c).
Figure 6. Cartoon illustrating that the variation of models in an ensemble (green ellipse) is largest in the direction of the long axis of the ellipse (a), and smallest in the direction of the short axis (b). To obtain the most robust features in the ensemble (i.e., the ones that vary the least), the ensemble needs to be projected onto the short axis of the ellipse (c).
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robust pattern that the models have in common is defined by the short axis of the ellipse. Hence to extract these patterns from the ensemble we must project the ensemble onto the short axis of the ellipse.
The major axes of the ellipse are found by the eigenvectors of the covari-ance matrix of the models, where the shear-wave velocities as a function of depth are treated as the independent parameters and the different model re-alizations as different measurements of these parameters. The eigenvector with the largest eigenvalue is then related to the long axis of the ellipse, while the ei-genvector with the smallest eigenvalue is related to the short axis. The eigenval-ue itself is a direct measure of the vari-ability of the models in the direction of the associated eigenvector.
In the simple case of the cartoon in Figure 6, it is easy to determine which eigenvector to keep because there are only two such vectors; one with a bigger eigenvalue and one with a smaller value. We would keep the small eigenvalue and project the en-semble onto its associated eigenvector only. In case we have more than two parameters, such as in our inversion case, we need to look at the eigenval-ue spectrum and see if we can identify a knee-point or a break in the eigen-value spectrum, above which the ei-genvalues are substantially larger or
become larger more rapidly as a function of the eigenvector index. In that case we project the ensemble onto the eigenvec-tors with eigenvalues below the knee-point.
We can perform this ensemble inference on both the whole ensemble generated using the NA (gray lines in Figure 5c) and on the ensemble of best models only (black lines in Figure 5c). At first sight, doing this for the whole ensemble might not seem meaningful, because this ensemble contains mostly models that do not fit the data (Figure 5a). However, knowing that the NA focuses its model space sampling on those regions that reduce the misfit the most, it follows that the most robust features in this ensemble are related to the parameters that are best resolved by the data.
Plotting the eigenvalues of the covariance matrix of the whole ensemble identifies a knee-point in the eigenvalue spec-trum (Figure 7a) around eigenvector index 17 or 19. Project-ing the whole ensemble onto these first 17 or 19 eigenvectors gives the projected ensemble shown in blue in Figure 7b and Figure 7d, respectively. The projected ensembles indicate that the shear-wave velocity from 15 to 75 m is best resolved by the data. From the linearized inversion, we already knew that
Figure 7. Eigenvalue spectra, (a) and (c), of the eigenvectors of the covariance matrix for the whole ensemble of all models generated using the NA as shown by the gray lines in (b) and (d). Projecting the ensemble on the first 17 (a) or 19 (c) eigenvectors gives the filtered ensembles shown by the blue lines in (b) and (d). Clearly the most robust information in the ensemble is that the shear-wave velocity between about 15–75 m is best resolved. The deeper (> 75 m) and shallow (< 15 m) shear-wave velocity appears to be poorly resolved because of the lack of low (< 4 Hz) and high (> 8.25 Hz) frequencies in the dispersion measurement.
the data well (cf. the dispersion curves related to both models obtained using the linearized inversion shown by the magenta and blue curves in Figure 5b). The individual models exhibit a trade-off relation between velocity and depth because no a-priori smoothness constraint was imposed on the models, as opposed to the linearized-inversion case. Note that both models obtained using the linearized inversion appear to be con-tained within the bounds of the ensemble of 75 best models.
Ensemble inferenceOnce we have an ensemble of models that fit the data to a pre-scribed tolerance, we can ask what all these models have in com-mon. Hence, instead of looking for an optimum solution as in the linearized inversion case, we now ask what robust information we can infer from the ensemble. To do so, we proceed in the same way as Douma et al. (1996).
Suppose that, for the sake of argument, we can depict our ensemble of models as an ellipse in a two-dimensional model space (Figure 6). In that case, it is clear that the models vary the most in the direction of the long axis of the ellipse, and the least in the direction of the short axis. That is, the most
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there was little resolution below 70–80 m, but the ensemble inference applied to the whole ensemble generated using the NA search method seems to further reveal that the shallow shear-wave velocity is poorly constrained by the data.
To confirm this somewhat surprising observation in the context of the linearized inversion, we calculated the resolu-tion matrices for the linearized inversions for both final mod-els (Figure 8). If resolution would be perfect, the resolution
matrix would be the identity matrix. For both models, how-ever, it is clear that at shallow depths (< 20 m) the resolution matrices contain substantial side lobes away from the diago-nal. This is consistent with the above conclusion drawn from the ensemble inference on the whole ensemble of models (Figure 7) and explains why for the linearized inversion the models obtained with different starting models differ consid-erably at shallow depths (Figure 8c).
Figure 8. Resolution matrices for the linearized inversion with both the smoothed layer over a half-space initial model (a) and the linear initial model (b). Above 20 m, the resolution matrices contain strong side lobes away from the diagonal and below 70 m the matrices are near zero. At the depths between 20 and 70 m, the resolution matrices are reasonably diagonal, indicating good resolution at this depth range only. The final models obtained using the linearized inversion for both starting models are shown in (c) for comparison.
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We attribute the lack of resolution at the shallow depths (< 20 m) to the maximum frequency in our dispersion curve data being limited to 8.25 Hz. It appears that without higher-frequency surface waves the shallow shear-wave velocity can-not be accurately determined. This is important, since the majority of the problems with the near surface often stem from the complexity of the Earth in the first 10–20 m. There-fore, when using dispersion-curve surface-wave inversion to try and find this shallow complexity, it is important to focus on methods of dispersion-curve estimation that maximize the largest usable frequency in the data. Possible measurement and inversion of other physical quantities (such as the H/V ratio) could be considered when inverting for the shallow shear-wave velocity.
By applying the ensemble inference to the ensemble of 75 best models, we obtain the eigenvalue spectrum shown in Figure 9a. The only clear break in the eigenvalue spectrum appears to be at eigenvector index 23 (Figure 9c). By pro-jecting the ensemble on the first 23 eigenvectors, we get the projected ensemble shown by the green lines in Figure 9d. This projection reduces the model parameter uncertainty for depths shallower than about 70 m when comparing the range
of values of the shear-wave velocity in the projected ensemble (green lines) with the original ensemble of best 75 models (black lines) but not much for depths below 70 m. When, using in-stead the first 14 eigenvectors for the projection (Figure 9b), we see that the uncertainty is further reduced, while the final models obtained from the linearized inversion remain con-tained within the bounds of the pro-jected ensemble for the well-resolved depth range of 15–75 m. Hence, both models obtained from the lin-earized inversion seem to be consis-tent with the robust information in the ensemble. This confirms that the linearized inversion seems to perform well in the well-resolved depth range of 15–75 m. As such, it seems both models are both good estimates of the shear-wave velocity at those depths.
We observe that in the well-resolved part of the model space, for the projections in Figure 9b and Figure 9d, the models resulting from the linearized inversion tend to be on the low end of the shear-wave veloc-ity range indicated by the projected ensemble. This is likely caused by the smoothness constraint that was im-posed on the linearized inversion, be-cause this constraint is known to in-clude a minimization of the norm of the model (Yanovskaya and Ditmar,
1990). This causes the linearized inversion to tend toward the model with the minimum shear-wave velocity. No smooth-ness constraint was imposed on the nonlinear search.
The filtered ensemble shows a range of possible shear-wave velocities at each depth. Therefore, the filtered ensemble can be used to estimate the uncertainty of the shear-wave velocity. However, the range of estimate shear-wave veloci-ties depends on the number of eigenvectors used to filter the ensemble. When there is a clear knee-point or break in the ei-genvalue spectrum, it is easy to find the number of eigenvec-tors on which to project. However, when no clear knee-point or break is present, it becomes harder to determine how many eigenvectors to use for the projection. To avoid ambiguity in choosing the number of eigenvectors to use and thus deter-mining the uncertainty in the model parameters, a more ob-jective criterion would be desirable.
ConclusionWe have explored the nonlinearity and nonuniqueness of the inversion of fundamental-mode Rayleigh-wave dispersion curves by comparing linearized inversion results based on the finite-element thin-layer method to the results obtained from
Figure 9. Eigenvalue spectra (a) and (c) of the eigenvectors of the covariance matrix for the ensemble of best 75 models shown by the black lines in (b) and (d). Projecting the ensemble on the first 14 (a) or 23 (c) eigenvectors gives the filtered ensembles shown by the green lines in (b) and (d). The final models from both linearized inversion are shown for comparison as shown on the blue and magenta lines in (b) and (d) as well as their associated initial models (the dashed blue and magenta lines).
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ensemble inference of an ensemble of models generated through a nonlinear search method (the neighborhood al-gorithm). The ensemble inference applied to the whole en-semble highlighted the depths that were well resolved, and emphasized the need for high frequency dispersion mea-surements to be able to resolve the near-surface (<10–20 m) shear-wave velocity. In the well-resolved depth-range, the results from two linearized inversions with different starting models compared well with the results obtained from the nonlinear search.
ReferencesDouma, H., R. Snieder, and A. Lomax, 1996, Ensemble inference
in terms of empirical orthogonal functions: Geophysical Jour-nal International, 127, no. 2, 363–378. Yanovskaya, T. and P. Ditmar, 1990, Smoothness criteria in surface wave tomography: Geophysical Journal International, 102, no. 1, 63–72.
Haney, M. and H. Douma, 2012, Rayleigh wave tomography at Coronation Field, Canada: The topography effect: The Leading Edge, 31, no. 1, 54–61, http://dx.doi.org/10.1190/1.3679328.
Huang, H., H. Yao, and R. van der Hilst, 2010, Radial anisotropy in the crust of SE Tibet and SW China from ambient-noise interferometry: Geophysical Research Letters, 37, no. 2, n/a, http://dx.doi.org/10.1029/2010GL044981.
Ivanov, J., R. Miller, P. Lacombe, C. Johnson, and J. Lane Jr., 2006, Delineating a shallow fault zone and dipping bedrock strata using multichannel analysis of surface waves with a land streamer: Geophysics, 71, no. 5, A39–A42, http://dx.doi.org/10.1190/1.2227521.
Kausel, E., 1999, Dynamic point sources in laminated media via the thin-layer method: International Journal of Solids and Struc-tures, 36, no. 31–32, 4725–4742, http://dx.doi.org/10.1016/S0020-7683(98)00262-5.
Lysmer, J., 1970, Lumped mass method for Rayleigh waves: Bul-letin of the Seismological Society of America, 60, no. 1, 89–104.
Muyzert, E., 2007, Seabed property estimation from ambient-noise recordings: Part 2—Scholte-wave spectral-ratio in-version: Geophysics, 72, no. 4, U47–U53, http://dx.doi.org/10.1190/1.2719062.
Paige, C., and M. Saunders, 1982, Algorithm 583, LSQR: Sparse linear equations and least-squares problems: ACM Transactions on Mathematical Software, 8, no. 2, 195–209, http://dx.doi.org/10.1145/355993.356000.
Paige, C., and M. Saunders, 1982, LSQR: an algorithm for sparse linear equations and sparse least squares: ACM Transactions on Mathematical Software, 8, no. 1, 43–71.
Sambridge, M., 1999, Geophysical inversion with a neighborhood algorithm—I. Searching a parameter space: Geophysical Jour-nal International, 138, 479–494.
Van der Kruk, J., S. Arcone, and L. Liu, 2007, Fundamental and higher mode inversion of dispersed GPR waves propagating in an ice layer: IEEE Transactions on Geoscience and Remote Sensing, 45, no. 8, 2483–2491, http://dx.doi.org/10.1109/TGRS.2007.900685.
Xia, J., R. Miller, and C. Park, 1999, Estimation of near-surface shear-wave velocity by inversion of Rayleigh waves: Geophysics, 64, no. 3, 691–700, http://dx.doi.org/10.1190/1.1444578.
Acknowledgments: We thank ION Geophysical/GXT Imaging solutions for permission to publish these results.Corresponding author: [email protected]
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Nonreflection seismic and inversion of surface and guided waves
MASW for geotechnical site investigation
Multichannel anaylsis of surface waves (MASW) is a seismic surface-wave technique developed specifi cally
for near-surface applications at depths usually shallower than a few tens of meters (Park et al., 1999). Since its introduction in the late 1990s, use of the technique has rapidly increased for two reasons: (1) it provides the shear-wave velocity (VS) of ground materials, which is one of the most important geotechnical parameters in civil engineering, and (2) it is easier to use than other common seismic approaches (e.g., refraction, refl ection, and surface-wave surveys).
Elastic moduli are commonly used in geotechnical en-gineering to describe the behavior of Earth materials under stress, which is ultimately related to such tasks as properly designing earthworks and structural foundations, risk assess-ment under specifi c site conditions, and monitoring various types of existing infrastructures for public safety. Among three primary types of modulus—Young’s (E), shear (μ), and bulk (κ) moduli—the fi rst two are most commonly used because of what they represent. Young’s modulus simply describes the deforma-tion tendency along the axis of stress, whereas the shear modulus describes the tendency of shape deformation (“shearing”) that, in turn, is related to the viscosity of material.
Young’s and shear moduli are determined from the pa-rameters of density (ρ), shear-wave velocity (VS), and Pois-son’s ratio (σ) (Figure 1). From the two defi ning equations shown in the fi gure, it is obvious that VS plays the most im-portant role as it is included as squared terms. In addition, VS in reality changes through a broader range than density and Poisson’s ratio. Th erefore, accurate evaluation of VS can be ex-tremely valuable in geotechnical engineering. As shown in the equations, the shear modulus can be determined fairly accu-rately once VS is known. On the other hand, Young’s modulus requires Poisson’s ratio to obtain a comparable accuracy.
MASW provides VS information of ground materials by processing Rayleigh-type surface waves that are dispersive when travelling through a layered media (diff erent frequen-cies travel at diff erent speeds). Th is dispersion property is de-termined from a material’s shear-wave velocity (VS) (by more than 95%), P-wave velocity (VP) (≤ 3%), and density (ρ) (≤ 2%). By analyzing dispersion properties, we can therefore de-termine VS fairly accurately by assuming some realistic values for VP and ρ. Th e accurate evaluation of the dispersion prop-erty is most important with any surface-wave method in this sense.
By using a 2D wavefi eld transformation (for example, f-k transformation), the MASW method converts raw fi eld data in a time-off set (t-x) domain directly into a frequency-phase velocity (f-v) domain in which dispersion patterns are evident through the wavefi eld maxima. Th e remaining procedure ex-tracts a dispersion curve of, usually, fundamental mode that will be used in a subsequent process in search for the one-dimensional (1D) VS profi le. An accurate dispersion analysis is obviously an important part of data processing, and this is
CHOON PARK, Park Seismic LLC
Figure 1. Defi ning equations for Young’s and shear moduli showing relationship with shear-wave velocity ( VS ) and other parameters.
Figure 2. A diagram showing the relative amplitude change with off set among surface waves, body waves, and ambient noise indicating that the most commonly used off set range for MASW data acquisition is usually shorter than 100 m. Th is almost always falls into the “optimum off set” due to the strong energy of surface waves.
Site Class S-velocity (VVSS)(ft/s)
S-velocity (VS)(m/s)
A(Hard rock)
> 5000 > 1500
B(Rock)
2500−5000 760−1500
C(Very dense soil and
soft rock)
1200−2500 360−760
D(Stiff soil)
600−1200 180−360
E(Soft clay soil)
< 600 < 180
F(Soils requiring
additional response)
< 600, and meeting some additional
conditions
< 180, and meeting some additional
conditions
Table 1. NEHRP seismic site classifi cation based on shear-wave velocity ( VS ) ranges.
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as liquefaction evaluation, is related to the elastic property of VS that is closely linked to the viscosity of material; the lower the VS, the more viscous is the material. On the other hand, ground amplification for a given earthquake mag-nitude, which causes most earthquake-related damages, changes with ground stiffness at relatively shallow depths. Based on the premise established from empirical studies that the top 30 m influences the most, and also from the fact that the shear-wave velocity (VS) is the best indicator of stiffness, the average VS in the top 30 m (usually denoted as Vs30m) is used as an important criterion in the design of building structures. In general, a site with a lower Vs30m would be subject to a greater ground amplification (and suffer more damage from an earthquake).
The National Earthquake Hazard Reduction Program (NEHRP) established by the U.S. Congress in 1977 adopts this criterion and classifies a site into one of several categories (Table 1). The International Building Code (IBC) published the same classification designations in 2000 as one of the pa-rameters that should be accounted for in structural design.
Calculation of the average VS for a certain depth range (for example, the top 30 m) can be accomplished in two ways: (1) based on relative thickness-contribution of each layer (method 1 in Figure 3), and (2) based on the defini-tion of velocity—total distance (∑di) divided by total travel time (∑ti) that is calculated by summation of thickness (di) divided by velocity (Vsi) of each layer (method 2 in Figure 3). Both methods can yield significantly different results for the same VS profile as illustrated by using a simple two-layer VS profile. Vs30m as defined in International Building Code (IBC 2000 and later editions) uses the second method, which tends to put a heavier weight on the lower VS:
Vs30m = ∑di / ∑ti = 30 / ∑(di/Vsi) (m/s) (1)
One of the most demanding applications for Vs30m evaluation occurs in wind-turbine site characterization (Park
and Miller, 2005). In this case, the VS value provided by MASW is important to account in the foundation design not only for the potential earthquake hazard, but also for the continuous and prolonged vibration of the ground pro-duced by rotating blades. Vs30m values and corresponding site classes presented in Figure 4 are selected from sites at sev-eral different wind farms in the midwest and the northeast. They are presented in the typical format to deliver the results to the engineers.
Site characterization of a potential nuclear power plantAnother example of the application of MASW for 1D (depth) site character-ization comes from the seismic hazard assessment of potential nuclear power
why it is often the signal-to-noise ratio (SNR) of dispersion image that directly influences reliability of MASW results.
A high SNR is required in all types of wave-based tech-niques to achieve highly accurate results. The surface-wave method utilizes Rayleigh waves as signal—the most trouble-some source-generated noise in the history of exploration seismology, commonly known as ground roll. Surface waves provide the highest SNR possible in any type of seismic ap-proach. As a consequence, the field operation for data acqui-sition and subsequent data analysis become extremely simple and effective, almost always ensuring the most reliable results.
MASW is the most advanced surface-wave method be-cause of its full adaptation of the multichannel principles long used in seismic exploration for natural resources. Figure 2 illustrates the tolerance in data acquisition with MASW by showing that the common range of source-receiver offsets re-quired for most geotechnical projects—usually shorter than 100 m—is optimal within which a high SNR is almost always guaranteed. The area too close to the source (for example, ≤ 5 m) is usually avoided because of the near-field effects that pre-vent full development of surface waves. On the other hand, an excessively far offset (for example, ≥ 100 m) is also avoided because of far-field effects that can make the energy level of surface waves drop below that of ambient noise.
Because shear-wave velocity (VS) information is a good indicator of the material stiffness, MASW is often applied in civil engineering to deal with mechanical aspects of ground materials (for example, assessment of load-bearing capacity, ground behavior under continuous and prolonged vibration, and ground amplification and liquefaction potential under earthquake). MASW also finds application in mapping the soil/bedrock interface, which is often more usefully and re-alistically defined from the stiffness concept than any other characteristics (Miller et al., 1999).
Seismic site classification-Vs30mOne application of MASW in earthquake engineering, such
Figure 3. Two possible ways to calculate an average shear-wave velocity ( VS ). The second method used for Vs30m tends to put a heavier weight on the lower VS .
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plants that are routinely subjected to machinery vibration and potential ground motion from earthquake. An instance recently implemented at Th yspunt, South Africa, is presented.
To meet the increasing demands for electricity generation, the gov-ernment of South Africa is commit-ted to the construction of several new nuclear power plants, with the coastal site at Th yspunt, west of Port Elizabeth, being considered as one of the sites for characterization (Figure 5) (Bommer et al., 2013). Although South Africa is not a region of elevat-ed seismicity, destructive earthquakes have occurred. Th e most recent had a magnitude 6.2 and occurred in 1969. Commissioned by the state-owned energy utility (Eskom), the Council for Geoscience (CGS), one of the National Science Councils of South Africa, conducted a seismic hazard analysis following the most stringent international standards. Th e MASW survey, adopted as one of the several approaches for this comprehensive analysis, was conducted at six diff er-ent locations in the area (Figure 5). Th e purpose was to evaluate VS struc-ture to depths as deep as possible, preferably down to 100 m.
Because of the unusually deep in-vestigation depth being sought, both active and passive surveys were con-ducted using a 48-channel seismic acquisition system and 4.5-Hz geo-phones as receivers. Since all the sites were in remote coastal areas without strong vibration sources available, such as traffi c, passive surveys relied on ocean activities for lower frequen-cy surface waves (for example, 10 Hz or lower). In addition, two diff erent active surveys were conducted at each site: one with relatively short receiver spacing (dx) of 1 m and a 5-kg sledge hammer source, and another with a longer dx of 4 m and a rock-drop source facilitated by a tracked hoe (Figure 6). Th e former survey setup was designed to investigate relatively shallow depths (for example, ≤ 30 m) and the latter was designed to investigate deeper depths (≤ 50 m).
Most sites had soft sandy overburden of varying thick-nesses, thereby attenuating surface waves quite rapidly, especially in the short-spread surveys using a sledge hammer source. Th e long spread surveys with the rock-drop source
generated suffi cient energy at frequencies as low as 10 Hz and lower at some sites (Figure 7). Th e passive survey adopted a two-dimensional cross-receiver array with a 10-m separation between receivers (Figure 6).
Dispersion imaging results from these passive surveys also showed remarkable energy at the lowest frequencies in the range of 4 −20 Hz (Figure 7). Th e results from the long-spread active surveys were quite similar, with diff erences mainly in
Figure 5. Site map of a potential nuclear power plant in Th yspunt, South Africa, that shows six MASW sites and deep borehole sites.
Figure 4. Shear-wave velocity (VS ) profi les selected from fi ve diff erent wind-turbine sites that fall into each diff erent class in seismic site classifi cation (as defi ned in Table 1).
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the higher-frequency content and resolution. All three types of dispersion images were stacked on top of each other to extend the usable bandwidth and increase the overall SNR of images. Th is stacking also enhanced higher-mode patterns that existed in diff erent frequency bands on diff erent images (Figure 7).
Th e inversion process to produce a 1D VS profi le at each site consisted of two phases. Th e fi rst phase used only the fundamental-mode (M0) curve to produce the fi rst approxi-mation of the velocity profi le. Th en, using this as an initial model in the second phase, fundamental and higher-mode dispersion curves were generated through the forward model-ing process. Th ese multimodal dispersion curves were then examined against observed patterns in the stacked dispersion image. Th is second phase of multimodal inversion was car-ried out and repeated after manually changing the velocity (VS) and thickness models until satisfactory matches were found. Figure 8 shows the fi nal VS profi les at all six sites obtained through this two-phase inversion approach. Th eo-retical bounds for 50% change in dispersion curves are also indicated in the profi le. Borehole data from PS-suspension logging are also presented in Figure 9 with their locations marked on the map in Figure 5.
No borehole sites were close enough to any MASW site to allow a meaningful direct comparison. Nonetheless, borehole data can show possible VS ranges of overburden and bedrock in the area. Th ey show bedrock depths change signifi cantly from one site to another in an unpredictable manner. Th ey indicate VS of overburden at about 200 m/s and that of bed-rock at about 1500−3500 m/s with fl uctuations between the two. MASW results also show VS values of bedrock in a similar but slightly lower range and almost the same VS of overburden (Figure 8). Depths of bedrock are also observed changing without any predictable pattern.
Underground mine investigationAnother common application area of MASW is mapping bedrock in depth and relative competence related to stress
Figure 6. A costal view (left) from an MASW site at the potential nuclear power-plant location in Th yspunt, South Africa. Ocean activities generated surface waves for passive surveys that used a 2D receiver array (center). A rock-drop source using a tracked hoe (right) was used for the active survey.
Figure 7. Dispersion images obtained from passive and active data sets acquired at site 1 in Th yspunt, South Africa. Th e image created from combining (stacking) the two images is shown at the bottom.
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these newly constructed highway segments as a means to monitor the general condition of the bedrock. MASW sur-veys were conducted as one of the approaches at the four target locations marked on the map in Figure 10. Th e main purpose of the MASW surveying was to map the general to-pography of bedrock and any other noteworthy subsurface features that could be linked to potential progression of bed-rock weakening or vertical migration of collapse structures.
To simultaneously survey two 12-ft wide lanes (both driv-ing and passing), a specially built double land streamer was
from overburden and cultural activities. Th e interface be-tween overburden and underlying bedrock can be a sharp boundary such as soil over competent basement rock, or a gradational transition such as the buried bedrock infl uenced by a severe weathering process with no physically distinct boundary. From a perspective of elastic property, the inter-face is also a sharp boundary in the former case, whereas it is a gradational change in the latter case because the weathered top portion would consist of varying degree of rock stiff ness. Th is suggests the stiff ness mapping by MASW would show the interface from a highly realistic standpoint.
MASW is known to provide highly eff ective and accurate informa-tion about bedrock depth, especially at depths shallower than 20 m or so. Th is is because surface-wave disper-sion properties are highly sensitive to change in this depth range. Although the shear-wave velocity (VS) of over-burden can be accurately estimated, VS of the bedrock tends to be slightly underestimated as depth increases be-yond the most sensitive range of 20 m unless special care is taken during the initial model creation at the be-ginning of inversion process.
Naturally, a common application would be the bedrock mapping in as-sociation with public safety where a potential hazard of bedrock collapse exists due to man-made or natu-ral causes in the subsurface such as mining and karst sinkhole develop-ment. Mapping bedrock topography can delineate the collapsed features, whereas a zone of bedrock with sig-nifi cantly lower VS than adjacent ar-eas may indicate a potential for verti-cal migration of a void.
In 2009, the Minnesota Depart-ment of Transportation (MnDOT) built a special type of pavement called CRCP (continuously reinforced con-crete pavement) along several seg-ments of Trunk Highway (TH) 169 in Chisholm, Minnesota (Figure 10). Th is construction followed several surface collapse features in the area near TH169 that were deemed to be related to previous mining activities for more than 100 years that left a subsurface maze of abandoned mine shafts and tunnels (Figure 10).
In 2011, the Offi ce of MnDOT Materials launched a project that in-cluded geophysical approaches to in-vestigate subsurface conditions below
Figure 9. Deep borehole data from PS-suspension logging at six locations in Th yspunt, South Africa.
Figure 8. MASW results of fi ve-layer shear-wave velocity ( VS ) profi les at six sites in Th yspunt, South Africa.
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used to collect surface-wave data by using a 48-channel ac-quisition system with each land streamer equipped with 24 4.5-Hz geophones installed at 1-m spacing (Figure 11). Th e left- and right-side land streamers (facing from source) were connected to channels 1−24 and 25−48 of the seismograph, surveying on driving and passing lanes, respectively. A pow-erful weight-drop source specially designed and built at the University of Saskatchewan in Canada was used to generate surface waves 6 m ahead and at the midpoint between the two streamers (Figure 11). To minimize traffi c control and to avoid traffi c-generated noise as much as possible, surveying took place during the night.
Figure 12 shows typical fi eld records from each land streamer and corresponding images of fundamental-mode dispersion patterns that possess an almost ideal SNR (i.e., 100% signal) in a broad frequency band of approximately 5−40 Hz. Figure 13 shows analyzed 2D shear-wave velocity
(VS) maps for the longest survey line on the eastbound lanes (line 3) that were obtained with a maximum analysis depth of 25 m.
Th e bedrock surface is denoted by a relatively sharp tran-sition boundary of velocities from approximately 200 m/s to 500 m/s. Th e bedrock depth is shown to gradually increase from about 7 m on the western end to the maximum depth of about 20 m on the eastern end, and this general trend con-formed to the boring results from several locations along or near the surveyed line. Interoverburden layers of higher veloc-ity materials are probably lenses of gravels and boulders. Th ey can be identifi ed on both maps of driving and passing lanes, appearing as localized lenses and continuous layers. Th is in-terpretation is consistent with the general geology of the area as confi rmed from borings and other sources.
Although the two maps from each lane look identical at a regional scale, diff erences are noticeable when examined from a local perspective. For example, bedrock is slightly deeper on the eastern half of the passing-lane map, and interoverburden layers have slightly diff erent depths and lateral extent. Con-sidering the identical and consistent acquisition conditions
Figure 10. Aerial map showing locations of four MASW survey lines on Trunk Highway (TH) 169 near Chisholm, Minnesota. Locations of mine properties and workings are also shown.
Figure 11. Th e double land streamers (24-channel acquisition each) built at the Minnesota Department of Transportation (MnDOT) for the simultaneous MASW surveys over two (driving and passing) lanes on TH169. Th e weight-drop source shown in the inset used a polyethylene impact plate that tends to increase surface-wave energy at lower frequencies. To minimize traffi c-generated noise and the burden of traffi c control, surveys took place during the night.
Figure 12. Typical fi eld records from MASW surveys on TH169, and their corresponding dispersion images from each land streamer that show almost ideal signal-to-noise ratio of 100% signal.
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Figure 13. MASW results of 2D shear-wave velocity (VS ) maps for line 3 from the surveys on TH169. Results from left (channels 1–24, driving lane) and right (25–48, passing lane) land streamers are shown.
the two land streamers were subjected to, it is reasonable to attribute these diff erences to subtle subsurface realities.
ReferencesBommer, J. J., K. J. Coppersmith, E. Hattingh, and A. P. Nel, 2013,
An application of the SSHAC level 3 process to the probabilistic seismic hazard assessment for the Th yspunt nuclear site in South Africa: Proceedings, 22nd International Conference on Structural Mechanics in Reactor Technology (SMiRT22).
Miller, R. D., J. Xia, C. B. Park, and J. M. Ivanov, 1999, Multichannel analysis of surface waves to map bedrock: Th e Leading Edge, 18, no. 12, 1392–1396, http://dx.doi.org/10.1190/1.1438226.
Park, C. B., R. D. Miller, and J. Xia, 1999, Multichannel analysis of surface waves: Geophysics, 64, no. 3, 800–808, http://dx.doi.org/10.1190/1.1444590.
Park, C. B. and R. D. Miller, 2005, Seismic characterization of wind turbine sites near Lawton, Oklahoma, by the MASW method: Kansas Geological Survey Open-fi le Report 2005-22.
Acknowledgments: I thank offi cials at Eskom in South Africa for permission to use the data sets in this article. Julian J. Bommer at Imperial College, London, UK, and Artur Cichowicz at the Coun-cil for Geosciences (CGS) in South Africa played critical roles in getting permissions. I also acknowledge all those actively involved in the fi eld operation during the MASW surveys at the Th yspunt nuclear site. Cichowicz and Denver Birch from CGS made major contributions to the MASW work. Henni de Beer of ESKOM facilitated access to the site and provided assistance in clearing the MASW test locations. Vincent Jele, Robert Kometsi, and Leonard Tabane of the CGS assisted Birch and two TI team members, Ellen Rathje and Adrian Rodriguez-Marek, with the MASW fi eld work. Wits University provided some equipment for use in the active MASW testing. Institute of Mining Seismology (IMS) performed the passive MASW experiments. Special thanks to Jason Richter at Minnesota Department of Transportation (MnDOT) for the
generosity in allowing TH169 data to be used for this publication as well as sharing other related information.
Corresponding author: [email protected]
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Nonreflection seismic and inversion of surface and guided waves
664 The Leading Edge June 2013
Nonreflection seismic and inversion of surface and guided waves
Bedrock mapping in shallow environments usingsurface-wave analysis
R ayleigh-wave analysis is nowadays a standard tool for retrieving near-surface S-wave velocity models. Th e
method, usually based on the inversion of surface-wave dispersion curves adopting a 1D forward operator, is most often applied to laterally varying sites and often on long and continuous seismic lines. Th e processing is performed using one of many available wavefi eld-transform techniques and results in several local dispersion curves estimated along the survey line. Th e dispersion curves are inverted to provide local S-wave velocity models which are merged to reconstruct 2D/3D structures.
If all stages of the survey are carefully planned, from ac-quisition to inversion, the surface-wave analysis can provide a signifi cant piece of information that, particularly if merged with other geophysical and borehole data, can represent a key in site characterization. In several engineering problems rang-ing from seismic site-response studies to geotechnical charac-terization, S-wave velocity is in fact an important parameter that it is often diffi cult to estimate through surface-based body-wave techniques.
Th is article presents two fi eld cases from Scandinavia that represent successful examples of application of surface-wave analysis to two engineering problems. Th e fi rst case is for water resource mapping in Lolland, Denmark, where the near-surface S-wave velocity model retrieved from surface-wave analysis is used together with high-resolution P-wave refl ection seismic to map the uppermost portion of the bed-rock (cretaceous chalk) in an area where the shallow depth of bedrock (~20 m) make it a diffi cult target for seismic re-fl ection. Th e second case from Helsingborg, Sweden, is for a geotechnical site characterization for a shallow railway tunnel in a complex sedimentary rock environment with signifi cant tectonics. Near-surface S- and P-wave velocity models, as well as structural information, are retrieved from surface-wave, refraction, and refl ection analysis of high-resolution P-wave data. In both cases, data has been collected on roads in an effi cient manner using a small vibrator and a land streamer.
Th e surface-wave surveys were performed according to the following scheme:
1) Seismic data acquisition along several seismic lines2) Estimation of a dense series of dispersion curves along the
lines3) Dispersion curve QC4) Monte Carlo inversion (MCI) of the dispersion curves to
set the reference model5) Laterally constrained inversion (LCI) of the dispersion
curves to supply a pseudo-2D velocity model
DANIELE BOIERO, Politecnico di Torino, presently WesternGeco LAURA VALENTINA SOCCO, Politecnico di TorinoSTEFANO STOCCO, Gamut S.r.l.ROGER WISÉN, Rambøll Danmark A/S
6) Comparison of the obtained velocity models with other geophysical investigation results and borehole data
Th e surface-wave data acquisition was performed with a land-streamer setup, designed by Rambøll Danmark A/S, utilizing geophones on stainless steel sledges with variable spacing and a 3.5-ton vibrating source (Figure 1). Th e ac-quisition was optimized to reach the desired investigation depth and data quality control was performed during the ac-quisition stage. Th e land streamer, specifi cally designed for surface-wave acquisition, consists of 77 4.5-Hz geophones, with 1.25-m spacing for the fi rst 47 geophones and 2.5-m for the last 30 geophones. Th e streamer has a total active length of 133.75 m. Th e distance between the vibrator and the fi rst geophone is 6.25 m. For the Lolland case, which was the fi rst with this specifi c setup, the vibrator was complemented with a 40-kg accelerated weight drop. A 15-s nonlinear sweep in the frequency band of 6–80 Hz was used, with useful energy being transmitted from around 8 Hz depending on site con-ditions. Two separate data sets with diff erent receiver spacing are extracted for surface-wave processing from each record, one with 48 receivers and 1.25-m interval and one with 55 receivers and 2.5-m interval.
A dense series of dispersion curves was then retrieved through a processing tool (Gamut S.r.l.) developed to handle the data acquired by the specifi c acquisition setup. Th e dispersion curves are estimated at each position of the streamer by picking the energy maxima on the f-k spectrum
Figure 1. Th e Rambøll land-streamer setup consists of an IVI minivib; a pulling vehicle equipped with vibrator controller, positioning system and data QC facilities; and a streamer with geophones on steel sledges with variable spacing. Th e streamer comes in two diff erent versions, one for low-frequency surface-wave surveys and one for higher-frequency high-resolution P-wave refl ection surveys.
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in which each model is linked to an experimental dispersion curve. Moreover, neighboring 1D models are linked by lateral constraints that impose similarity between neighboring mod-el parameters of the same type. Th e diff erence with respect to individual inversions of the retrieved dispersion curves is that, in LCI, all the dispersion curves along the line are inverted simultaneously, minimizing a common objective function. Th e number of output models is equal to the number of dis-persion curves along the seismic line, but the models are not independent and the pseudo-2D model that is obtained by merging them is internally consistent.
Th e data and the constraints act in a complementary man-ner: where model parameters are poorly resolved by the data, their values will be more strongly infl uenced by constraints and, hence, by the values of the same parameters in a position
and by processing the two diff erently spaced confi gurations separately. Consequently, for each shot gather, we estimated two diff erent dispersion curves that are spatially located at the center of the relevant receiver spread. In this way, the dispersion-curve data set to be inverted is constituted by a series of dispersion curves estimated along the seismic line. A thorough quality control has been applied to the dispersion curves before inversion with the aim of selecting high-quality fundamental-mode curves along the whole line.
Th e inversion process of the surface-wave dispersion curves suff ers from solution nonuniqueness and it is hence strongly biased by the initial model. To mitigate this problem, the inversion is based on a two-step process. It starts from a broadband exploration of the model parameter space through MCI and focuses on the high probability density (low-misfi t) model space regions with a refi nement performed through LCI. Both the inversion algorithms use Haskell-Th omson for-ward modeling (Th omson, 1950; Haskell, 1953; Herrmann, 1996). Th e unknown parameters are the S-wave velocity and the thickness of the layers. Th e densities of the layers and the Poisson’s ratios (or P-wave velocity) are assumed a priori.
Th e MCI algorithm (Socco and Boiero, 2008) uses scale properties of Rayleigh-wave propagation to reduce the re-quired number of simulations and effi ciently explore the model parameter space Th e misfi t function accounts for problem dimensionality (number of unknown parameters with respect to available data points) and data uncertainty. Th e misfi t is evaluated for each profi le of the population and used to select acceptable models according to a statistical test that selects all profi les that are “equivalent” according to a giv-en level of confi dence. Th e selected models, hence, represent a set of possible solutions which may be considered equally probable, given the experimental data and their uncertainty, the chosen parameterization and the level of confi dence. It can be shown that the solutions of deterministic inversions performed with diff erent initial models fall in a wider model region with respect to the models selected by the Monte Car-lo inversion. Hence, the region where the selected profi les fall can be used as a reference for the defi nition of a consistent initial model for LCI. If low-velocity layers or stiff inclusions are present, MCI allows them to be evidenced and accounted for in the parameterization for LCI.
LCI, fi rst presented for the interpretation of resistivity data (Auken and Christiansen, 2004), is a deterministic inver-sion in which each 1D model is linked to its neighbors with a mutual constraint to provide a single pseudo-2D model. Th e lateral constraints can be considered as a priori information on the geologic variability in the investigated area: the smaller the expected variation of a model parameter, the more rigid the constraint. Moreover, it is possible to use any available a priori information, (e.g., from drilling), to further constrain the inversion. LCI has been validated through several applica-tions to resistivity and electromagnetic data and has also been successfully applied to surface-wave data (Socco et al., 2009).
Th e local dispersion curves extracted along the line are inverted through a least squares LCI algorithm to get a fi nal pseudo-2D result. Th e inversion result is a set of 1D models,
Figure 2. Lolland survey. (a) Site map and the seismic line with CMPs for the refl ection line. (b) A seismic gather with sensor spacing of 1.25 m. (c) Picked maxima and selection of the fundamental mode on the f-k spectrum (normalized) of the seismogram shown above.
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where the data contain greater information. Th e strength of the constraints can be tuned according to a priori information on the geologic variability in the area or selected with several tests as the highest possible value that does not produce an increase in the misfi t with respect to unconstrained inversion. In other words, constraints should not get in contrast with the compliance with the data and allow lateral variations to be depicted where present (Boiero and Socco, 2010).
Where a priori information is available from borehole and other geophysical investigations, it can be either included in the inversion as constraints on the initial model based on this a priori information or used as a posteriori benchmark to validate the results obtained by surface-wave analysis. In this study, we have not included the a priori information in the initial model.
LollandIn 2009, Rambøll performed a refl ection and surface-wave seismic survey for the Danish Environmental Agency at Lolland in the south of Denmark. Intensive groundwater mapping is performed in Denmark as part of the Danish environmental agency’s work toward providing naturally clean, untreated high-quality drinking water, a task that is part of the Danish implementation of the European Union’s Water Framework Directive. Within this work, geophysical surveys play an important role to cover a large enough part of the Danish territory and are used together with several other methods (e.g., drilling, geophysical borehole logging, geochemical and hydrogeological mapping, and geological modeling).
Th e application of shallow refl ection seismic methods has become an important tool and complements the electric and electromagnetic methods, used successfully for cost-eff ective mapping of shallow geology, with mapping of deeply (up to several hundreds of meters) buried Quaternary valleys and deep Tertiary aquifers as well as faults. Because the cost for carrying out manual shallow onshore refl ection seismic sur-veys acts as a limiting factor toward the use of seismic, the development of land-streamer technology was started at the shift of the century (Vangkilde-Pedersen et al., 2006). Th e land streamers developed at Rambøll signifi cantly improved the production rate and by now several generations of land-streamer systems have been used successfully and more than 1000 km of seismic have been surveyed, both within the groundwater mapping program and in other applications (e.g., oil or gas prospecting and infrastructure). Land-stream-er technology has recently also been used for dedicated low-frequency measurements for surface-wave analysis. Together with P-wave velocity models from refraction analysis of the P-wave refl ection data, where applicable, the S- wave models from surface-wave analysis provide important information for static correction, interpretation of shallow features, and qual-ity control of the near-surface parts of the refl ection results.
Th e survey in Lolland consisted of 10.7 km of refl ection seismic and 10.4 km of surface-wave profi ling. Th e purpose of the survey was to increase the overall understanding of the geologic structures in the area; the results from the seismic
survey are used for building the geologic and hydrogeologic models. In particular, the top of the PreQuarternary unit, that consists of chalk, is shallow (~20 m) in the entire area and the aim of surface-wave analysis is to map the surface of the bedrock. Th e data set consists of 507 common-shot gath-ers. Each record contains 48 receivers (1.25-m spacing) and
Figure 3. (a) All fundamental-mode dispersion curves extracted along the line shown in Figure 2. (b) Th eir representation in the distance-λ/2.5 domain.
Figure 4. Solution for the dispersion curve estimated from shot gather 20630 (2.5-m spacing). Th e blue models on the left represent the lower misfi t models with their relative dispersion curves (right upper panel). Th e red model will be used as the reference model. Th e blue asterisks on the right bottom panel correspond to the best-fi tting model.
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55 receivers (2.5-m spacing). Th e SW seismic line is shown in Figure 2a.
Th e seismic data have been processed using a tool devel-oped by Gamut to handle the shot gathers acquired with the used land streamer. An example of shot gather is shown in Figure 2b.
Th e application of the f-k wavefi eld transform (Figure 2c) on the seismogram of Figure 2b allows the energy maxima, that correspond to the main energetic events in the seismo-gram (Rayleigh waves), to be identifi ed and picked (Figure 2c). Once the maxima have been picked, the outliers can be removed either in the f-k or f-v domain. Th e last step of the processing consists in selecting the maxima corresponding to the Rayleigh-wave fundamental mode (Figure 2c) that will be used during the inversion. Th e choice of the data points to be associated to the fundamental mode has been done with a conservative approach: modes are quite clearly separated in the high-frequency band while, at low frequency, where smooth passage in a higher mode would be possible, points presenting a steep pattern have been disregarded during the picking.
All estimated dispersion curves are shown in Figure 3a (dif-ferent colors correspond to diff erent locations along the line). Th e curves are smooth and continuous with good quality and a wide frequency band (6–60 Hz) along the whole seismic
Figure 6. (a) 3D visualization of the inversion result using medium regularization for thickness and strong regularization for velocities. Boreholes are also included in (b), see Table 2 for description of colors. (c) Superposition of the results after depth-to-time conversion on the seismic refl ection section (wiggle image).
Figure 5. (a) Collection of the 1D S-wave velocity models obtained by the Monte Carlo inversion. (b) S-wave initial velocity model for LCI obtained by averaging the models in (a).
Figure 7. Helsingborg survey. (a) Site location and the acquired seismic lines; blue dots represent the position of extracted dispersion curves and red dots represent borehole positions. (b) Th e f-k spectrum with picked dispersion curve and selection of fundamental-mode data points.
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line. Even though the pattern of the curves is quite similar, it can be noticed that the velocity varies along the line. Th e fundamental-mode curves are shown in the distance-λ/2.5 domain (Figure 3c). Th is plot provides a rough preliminary
evaluation of the investigation depths. Th e color represents the Rayleigh-wave phase velocity. Th is representation shows that data provide information down to about 30 m.
Inversion was then performed, starting with MCI. Before
Figure 8. Fundamental-mode dispersion curves extracted along the lines shown in Figure 7 transformed in the distance-λ /2.5 domain. (a) Hel3. (b) Hel4. (c) Hel5. (d) Hel10. (e) Hel16. (f ) Hel18.
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Layer number Th ickness[m]
S-wave velocity[m/s]
Poisson’s ratio Density[kg/dm3]
1 2.7 184 0.3 1.8
2 6.5 387 0.4 1.9
3 10.7 533 0.4 2
Half-space ∞ 904 0.3 2.1
Table 1. Reference model at Lolland retrieved by MCI.
inversion, the dispersion curves have been undersampled in the wavelength domain, keeping a point every 0.5 m to reduce the computational cost in the misfi t calculation. Th e model parameter space is randomly sampled with 10 thousand mod-els and the fundamental mode dispersion curve associated to each 1D S-wave velocity model is scaled and compared with the fundamental mode of each dispersion curve along the
line. Th e misfi t is computed and, at each dispersion curve, the Fisher test is applied to select a set of accepted models. In this way, it is possible to have a picture of the solution for each position along the line and to set up a consistent refer-ence model for LCI.
MCI results (Figure 6) are reported using a representation based on the relative misfi t. Th e darkest color always corre-sponds to the models whose theoretical dispersion curve has the lower misfi t with respect to the experimental dispersion curves. Th e same color is used to represent the S-wave ve-locity model and its associated dispersion curve. Th e choice of the reference model can be made by selecting the best-fi tting model or manually picking the high probability den-sity regions (Figure 4). Th e best model is selected for each dispersion curve along the line and the set of models shown in Figure 5a is found. Th e result of MCI (Figure 5a) is used to defi ne a 1D reference model for LCI by averaging all the model parameters along the line (Figure 5b). Th e values of the model parameters are shown in Table 1. Th e reference model shown in Figure 5b is then used as the starting model for LCI whose results are shown in Figure 6.
Diff erent levels of regularization (spatial constraints strength) have been tested and are not shown here. Th e eff ect of increasing the strength of the spatial constraints is evaluated by analyzing the normalized residu-als at the last iteration. Th e residuals are almost the same for the diff erent levels of constraints which have been tested. For this reason, we consid-ered the result, obtained using the strongest regularization (medium for thickness and strong for velocities).
In Figure 6a, we show the 3D im-age of the obtained velocity model. Th e inversion results have been com-pared with the information from boreholes available from the Geologi-cal Survey of Denmark and Green-land, GEUS, (http://jupiter.geus.dk/). Th e position of the available bore-holes is shown in Figure 2. Th e bore-holes (for the legend see Table 2) are superimposed on the inversion results in Figure 6b. Because the boreholes do not lie exactly on the seismic line, the information from each borehole is associated with the closest disper-sion curve. Th e depths at which the chalk has been found in the diff erent boreholes are in agreement with the S-wave velocity models.
Th e S-wave velocity model is then compared with the high-res-olution seismic refl ection section along the same line (Figure 6c). For
Figure 9. (a) LCI results for all lines. Lines (b) Hel3, (c) Hel4, and (d) Hel5 compared with borehole results.
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Figure 10. (a) Refl ection CSP gather from line Hel3; the refl ection from the bedrock is clearly visible. (b) Superposition of the results for lines Hel3, Hel4, Hel5, and Hel16.
the depth-time conversion of the Vs model, we used a P-wave velocity obtained by setting Vp/Vs equal to 2 for the unsatu-rated soil and Vp equal to 1500 m/s for the saturated soil. As a rough approximation, we consider the fi rst layer as unsatu-rated soil, and the second and the third as saturated soil.
Th e chalk bedrock has a smoothly undulating pattern with a syncline in the northern half of the profi le, around off set 6250–7600 m, that is clearly seen in both SW and re-fl ection results. Th e syncline is interpreted as a broad eroded Quaternary meltwater channel or lake. A drilling in the deep-er part of this valley showed sand and gravel directly on top of the chalk, most likely meltwater deposits, overlain only by clay.
HelsingborgIn 2009, an extensive seismic campaign was performed by Rambøll as part of the site investigations for a railway tun-nel from Helsingborg central station and southward through the city. Th e seismic campaign consisted of about 11 km of refl ection seismic and 5 km of surface-wave seismic profi ling. Th e main aim of the investigations was to obtain detailed knowledge from refl ection seismic on the many faults within the uppermost part of the subsurface and thereby improve the geologic model of the bedrock below the center of Hel-singborg. One intention of the near-surface S-wave velocity model from surface-wave seismic was to make it possible to track faults all the way to the surface. Th e S-wave model has also, together with the corresponding P-wave velocity sec-tions from refraction analysis of the refl ection data, been used to create a continuous section along the tunnel align-ment to be used as part of the detailed geotechnical investi-gation.
Th e data set consists of 207 common-shot gathers. Each record contains 48 receivers (1.25-m spacing) and 55 receiv-ers (2.5-m spacing). Th e seismic lines are shown in Figure 7a. Similarly to Lolland case, f-k analysis was performed and fundamental-mode dispersion curves were extracted. As for the Lolland case, the selection of data points to be attrib-uted to the fundamental mode was carefully performed by disregarding those points whose attribution was ambiguous. In this case, the fundamental mode appears clearly dominant in the whole frequency band and well separated from higher modes. In Figure 7b, we show an example of processing. Sev-eral dispersion curves were extracted along the diff erent lines. In Figure 8, the fundamental modes are shown in form of a pseudo-section (phase velocity × 1.1 versus wavelength/2.5). Contrary to the Lolland case, the dispersion curves present several gaps. Th e velocity increases with pseudo-depth and, because it is possible to use a simplifi ed geologic model for the Quaternary deposits and the task of retrieving the bed-rock depth, a simple reference model with one soft layer over a stiff half-space was selected. Th e S-wave velocities and the thick-ness of the interface were estimated through MCI using the same approach adopted at Lolland. Th e obtained initial model for LCI is shown in Table 3.
LCI was performed, starting from weak spatial con-straints and increasing them until the normalized residuals
Material Color
Sand Light BlueClay GrayClay till MagentaLimestone Red
Table 2. Description of colors in Lolland boreholes.
Layer number
Th ickness[m]
S-wave velocity [m/s]
Poisson’s ratio
Density[kg/dm3]
1 4.68 193 0.3 1.8
Half-space ∞ 799 0.3 2.1
Table 3. Reference model at Helsingborg retrieved by MCI.
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started to increase with respect to unconstrained inversion. Th e fi nal selected level of constraints was medium constraints on interface depth and strong spatial constraints on the ve-locities. Figure 9a shows the LCI results for all lines; Figures 9b–d compares some selected lines with borehole results. Th e boreholes confi rm that the simple two-layer model adopted for inversion is suitable for describing the geology of the site. Th e bedrock depth is well and accurately retrieved also by this simple parameterization.
In Figure 10, the LCI results are compared with P-wave seismic refl ection results along some selected lines. An exam-ple of CSP gather from the refl ection data set is also included to show the strong refl ection from the bedrock and the con-sistency of stacked sections with raw data. Th e comparison of seismic stacked sections and surface-wave analysis is per-formed by transforming the depth axis on the Vs model to a time axis following the same approach applied at Lolland. In this case, based on geologic information, the water table is assumed at 0 m. Th e agreement with both borehole results and seismic refl ection is good and the bedrock top is well depicted.
ConclusionsBoth presented fi eld cases represent successful examples of the application of surface waves for near-surface character-ization aimed at providing important information for en-gineering and hydrogeologic problems. Th e results were com-pared with other data (boreholes and seismic refl ection) and had good agreement, confi rming the potential of surface-wave applications for these problems. No quantitative evaluation of the agreement with boreholes was done because the borehole position was not coincident with the seismic line. Th e acquisi-tion system allowed fast and eff ective gathering of dense data sets and the processing tool provided a dense series of disper-sion curves. Th e two-step inversion approach reduces the risk solution nonuniqueness (e.g., producing local minima) thanks to the preliminary Monte Carlo inversion, and the LCI provides laterally varying internally consistent velocity models that are able to depict smooth lateral variations.
ReferencesAuken, E. and A. V. Christiansen, 2004, Layered and laterally con-
strained 2D inversion of resistivity data: Geophysics, 69, no. 3, 752–761, http://dx.doi.org/10.1190/1.1759461.
Boiero, D. and L. V. Socco, 2010, Retrieving lateral variations from surface-wave dispersion curves analysis: Geophysical Pros-pecting, 58, no. 6, 977–996, http://dx.doi.org/10.1111/j.1365-2478.2010.00877.x.
Haskell, N., 1953, Th e dispersion of surface waves on multilayered media: Bulletin of the Seismological Society of America, 43, no. 1, 17–34.
Herrmann, R. B., 1996, Computer programs in seismology: an over-view on synthetic seismogram computation: St. Louis University.
Socco, L. V. and D. Boiero, 2008, Improved Monte Carlo inversion of surface-wave data: Geophysical Prospecting, 56, no. 3, 357–371, http://dx.doi.org/10.1111/j.1365-2478.2007.00678.x.
Socco, L. V., D. Boiero, S. Foti, and R. Wisén, 2009, Laterally con-strained inversion of ground roll from seismic refl ection records: Geo-physics, 74, no. 6, G35–G45, http://dx.doi.org/10.1190/1.3223636.
Th omson, W. T., 1950, Transmission of elastic waves through a strati-fi ed solid medium: Journal of Applied Physics, 21, no. 89.
Vangkilde-Pedersen, T., J. F. Dahl, and J. Ringgaard, 2006, Five years of experience with landstreamer vibroseis and comparison with con-ventional seismic data acquisition: Proceedings of 19th Annual SA-GEEP Symposium on the Application of Geophysics to Engineering and Environmental Problems, 1086–1093.
Acknowledgments: Processing, inversion, and quality control of the surface-wave data in the two fi eld examples were collaborations between Rambøll Danmark A/S and Gamut S.r.l. (Politecnico di Torino spin-off ). We thank Rambøll Danmark A/S for permission to show data produced by its in-house-developed land streamer; the Danish Environmental Agency and Helsingborg city for permission to present the data; Anders Almholt, Rune Bert Jørgensen, Uff e Torben Nielsen, Jørgen Ringgaard, and Corrado Calzoni for their collaboration.
Corresponding author: [email protected]
Table 4. Description of colors at Helsingborg boreholes.
Material Color
Soil Light BlueFine sand GrayGravelly sand MagentaBedrock Red
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MASW surveys in landfi ll sites in Australia
Multichannel analysis of surface waves (Park et al., 1999), commonly called MASW, is a seismic
technique used to map the near-surface S-wave velocity structure. It has been applied to a range of geotechnical engineering problems, such as detection of cavities (Miller et al., 1999), the search for bedrock structure (Carnevale et al., 2005), examining water seepage (Ivanov et al., 2006), and monitoring ground improvement (Burke and Schofi eld, 2008).
As a by-product of urban devel-opment, industrial and domestic ref-uge is amassed and deposited in vari-ous places, ranging from naturally low ground to abandoned quarries. Th ese places are called landfi ll sites. As urban development progresses, the landfi ll sites reach their capacity and become unsuitable for further fi lling. At that point, a variety of approaches are con-sidered as a means of increasing the capacity. When housing developments creep up to the fi ll site, more refuse is not welcomed and the land use is re-considered.
Landfi ll sites may be rezoned and redeveloped for a variety of purpos-es including residential, industrial, commercial, and recreational (parks and athletic fi elds).
Development of a fi ll site is rath-er problematic. Th e original topogra-phy of the fi ll site is rarely surveyed before the start of fi lling. So the thickness of the fi ll material is usu-ally unknown. In most cases, the fi ll material is neither documented nor quantifi ed. Even when the fi ll mate-rial is documented, the suitability of the land for further development is unpredictable. Th erefore, the eleva-tion of the original natural ground and load-bearing capacity of the fi ll site are often investigated prior to es-tablishing the fi tness of the site for further development. Some prob-lems may occur even after develop-ment and a similar investigation is required for monitoring and reme-diation.
Th is article shows four case his-tories detailing the use of MASW in landfi ll areas in Australia:
KOYA SUTO, Terra Australis Geophysica Pty. Ltd.
1) planning for further fi ll at an operating fi ll site (Suto and Lacey, 2011)
2) an area planned for industrial development (Suto and Scott, 2009)
3) a site with a hard fi ll material being considered for retail development (Suto and Cenic, 2012)
4) a football fi eld where a sinkhole developed in a droughtData acquisition for the MASW surveys used two source
Figure 1. (a) An S-wave velocity section of an MASW survey line in the Toowoomba Waste Management Centre. (b) Th e “piggyback” expansion plan based on the MASW survey.
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people live within the boundaries of the regional council. Th e Toowoomba Waste Management Centre is licensed to accept between 100,000 and 200,000 tons of domestic waste per year. After more than 30 years of accumulating un-controlled fi ll material, mainly domestic and industrial waste, the landfi ll site is approaching capacity. In 2011, the possibility of developing the site via a “piggyback” expansion was considered. A “piggyback” expansion is a method whereby the previously limited vertical capac-ity of a fi ll site is increased via the installation of a new liner over the existing (old) fi ll. Ensuring the integrity of the liner and preserving drainage paths are important to this method of expansion. Th us, the site requires realistic
types: a 50-kg weight-drop system or a 12-lb sledge hammer. Th e seismic energy was recorded by 24 geophones with a natural frequency 4.5 Hz, spaced at 1-m intervals along a purpose-built landstreamer. Station intervals in all fi gures in this article are 1 m. Th e seismograph is a Seistronix RAS-24 controlled by a portable PC.
Th e processing was performed with SurfSeis software de-veloped at the Kansas Geological Survey. Th e software used for the plan view maps is Surfer 10 by Golden Software.
Case 1: Investigation for further fi llToowoomba, a large inland regional city in Queensland, Australia is 125 km west of Brisbane. Approximately 165,000
Figure 2. (a) An S-wave velocity section from the MASW survey in Tweed Heads. (b) A series of depth slices of S-wave velocity structure. (c) S-wave velocity distribution 1 m below ground.
Figure 3. Th e tree root excavated from the “anomaly” area.
Figure 4. A football fi eld and a sinkhole.
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characterization to assess the expected magnitude of diff erential settlement once additional load is placed upon the existing landfi ll site.
Figure 1a shows the S-wave velocity section of one survey line. Th e colors are blocked every 50 m/s. A borehole identi-fi ed the top of the natural ground at a depth of about 18 m, which corresponds to about 225 m/s. Th e profi le of the natural surface is interpreted according to this velocity (blue line in the fi gure). Th e thickness of the landfi ll was also estimated from the historical level survey (red line). Discrepancies in both vertical and lateral directions are found between the interpreted natural profi les using old elevation data and the recent drilling and MASW surveys. Th ese were later found to be the result of diff erences in the datum used in the elevation and location surveys. Without
the MASW survey, this discrepancy would have passed unnoticed.
Figure 1b shows cross sections in two directions of the expansion plan of the landfi ll area. Th is design is based on the depth profi le of the natural surface and distribution of the strength of the fi ll material derived from the S-wave velocities.
Case 2: Investigation for industrial developmentAn MASW survey was carried out at an industrial devel-opment site in Tweed Head, New South Wales, to moni-tor the compaction and uniformity of the fi ll material after compaction using a roller. An additional goal of the survey was to map the spatial distribution of areas with insuffi cient compaction. Th e key to quantifying compaction based on shear velocity was calibrating the survey with geotechnical test results. Th is secondary objective was intended to deter-mine where further ground improvement or excavation and replacement were required.
Th e 1.5-hectare site typically consisted of 1.5–2 m of un-controlled fi ll including building rubble, old trees, and large quantities of other organic matter. Th e land had been left un-developed for many years because of the daunting geotechni-cal challenges posed by the fi ll material, but was now consid-ered economically worthy of characterization, remediation, and development.
Eleven parallel lines about 120 m in length and separated by 12 m were surveyed in the site. Th e MASW analysis was
Figure 5. (a) S-wave velocity section over the sinkhole. (b) Depth slices of S-wave velocity structure every 0.5 m from the ground surface.
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Figure 6. Th e old fi ll site now used as a car park. Th e landstreamer is on the driveway, and a chimney of the heritage-listed kiln of the brickworks is in the background.
carried out at 12-m increments along the lines. Th e low-velocity layer (blue) is clearly identifi ed at a depth of 2 m (Figure 2). Th is is consistent with the eff ective compaction depth (about 1.5 m) of the ground by the impact roller. Th e compaction was laterally inconsistent and thereby did not ef-fectively reach to the edge of the site, because of the size and turning circle of the machine.
Because data points were suffi ciently dense, S-wave ve-locities could be interpolated horizontally and vertically, with the 3D distribution of S-wave velocity displayed as a series of maps in plan view (Figure 2b). Th is display helped engineers plan further improvement of the site.
Along with the MASW survey, the geotechnical study in-cluded dynamic cone-penetration (DCP) tests. Th is geotech-nical test tracks the number of strikes of a specifi cally shaped rod, constructed of a certain material, by a uniform weight dropped from a predetermined height. Th e number of blows necessary to penetrate a specifi c thickness of material is an indication of the hardness of the ground material. Th rough an empirical equation, Young’s modulus is estimated from the number of strikes. In Figure 2b, black dots show the points where the DCP test estimated Young’s modulus (E) higher than 100 MPa; white dots show areas with lower Young’s modulus. Th ey are superimposed on the S-wave velocity map 1 m below ground surface.
In general, an S-wave velocity of about 220 m/s (yellow-green boundary) corresponds to E = 100 MPa. One area, the blue oval in the fi gure labeled “Anomaly”, highlights a distinct dis-agreement between S-wave veloc-ity and DCP tests results. Th at area was excavated and an old tree root unearthed. Th e soil around it was relatively compacted and its S-wave velocity was high on average over the range of the traces used in the MASW analysis. But the point measurement of the DCP test showed otherwise.
Case 3: Investigation for weakened groundFrom 2007–2010, Brisbane was hit by a severe drought. Th e parks and grounds became dry and a football fi eld built on a landfi ll started to de-velop sinkholes because of shrinkage of the soil and fi ll material (Figure 4). An MASW survey was carried out to assess the depth and extent of the problem around one sinkhole. Sur-vey results contributed to planning the ground-improvement operation which was to involve stripping away and replacing the ground material.
Figure 5a shows the S-wave ve-locity sections of the survey line over the sinkhole. Th e slow layer from the Figure 7. S-wave velocity (a) and interpreted section (b) of the car park.
ground surface to a depth of about 0.5 m extends over the whole line. By the time the survey was carried out, the hole had been fi lled and the surrounding area had been compact-ed. Th e S-wave velocity section clearly illustrates the eff ect of that remediation.
Th e depth-slice map of the S-wave velocity structure is shown in Figure 5b. Th is leads to the conclusion that the western part
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of the grounds is generally softer than the eastern side, and ground improvement is needed at least through the top 1 m.
Case 4: Investigation for redevelopment (in a site with hard fi ll material)Near the City of Adelaide, capital of South Australia, there was an old brickworks plant which closed in 1975. Th e fi ll site adjacent to the brick plant has been used as a car park for a local market (Figure 6). A plan to redevelop this site included a large shopping complex. An MASW survey was carried out to examine the strength of the ground for this building project.
Th e area is in a fl ood plain close to a river. It was anticipat-ed that unconsolidated river sediments were present beneath the landfi ll material. Th e landfi ll material was not controlled or documented. But because of its vicinity to the brickworks, it is not hard to imagine that some waste from brick-baking process and broken bricks was dumped at this site. Th erefore, unique to this study in relation to the others in this article, a velocity reversal is expected as the basal contact of the landfi ll.
Figure 7 shows an S-wave section of one line of the survey. A number of boreholes were drilled around the site and the contact between base of the fi ll and top of the natural surface was identifi ed at these locations. Th e borehole data are posted in Figure 7b, in which the yellow column indicates the land-fi ll and light blue indicates the natural ground. At many bore-hole locations, the boundary between the fi ll material and natural ground corresponds to the 300 m/s velocity contour with the faster layer above and slower layer below. Deeper in the section where the S-wave velocity reaches 300 m/s, a second time is estimated to be the bedrock surface. Th e color scheme was adjusted to clearly show these boundaries. On this section, the white line indicates the interpreted base of the fi ll. Note that this horizon could not be interpreted con-sistently across the entire section. Th is is perhaps because of inconsistency of the fi ll material; some part was identifi ed as fi lled with “usual” industrial and domestic refuge and clearly inconsistent with the refuge from the brickworks.
Closing remarksTh e MASW method was applied to landfi ll sites at various locations around Australia being considered for various types of developments, improvements, or remediation. In each case, the MASW survey suggested a reliable and ground-truth-verifi ed solution to a geotechnical engineering ques-tion. MASW surveying can provide continuous profi les with information related to the strength of the ground which can be compared directly with drilling and essential for confi -dent use by geotechnical engineers.
Because an MASW survey takes place strictly on the ground, it does not disturb the near-surface environment or the materials and fl ow paths that many times are the primary target of the investigation. In some situations, such as parks and athletic fi elds, this is an important consideration.
Although S-wave velocity, obtained by the MASW meth-od, is an elastic parameter and therefore a physical property useful to engineering application, it is not yet suffi ciently
recognized or generally appreciated by geotechnical engineers. Accepted conventional engineering parameters are often ob-tained through lab measurement or in-situ measurement like the dynamic cone-penetration (DCP) test and cone-penetra-tion test (CPT). Th ey are measured under specifi c defi ned conditions and are therefore comparable from sample-to-sam-ple. However, these measurements do not produce measured properties with a physical dimension; relationships between parameters are often given only by empirical correlations. Yet, these parameters are well established and widely used. S-wave velocity overcomes all these limitations and should someday be more widely embraced by engineers and used for engi-neering projects as a standard indicator of the nature of the ground.
ReferencesBurke, R. W. and N. B. Schofi eld, 2008, Th e multichannel analysis
of surface waves (MASW) method as a tool for ground improve-ment certifi cation: Proceedings of the Symposium on the Applica-tion of Geophysics to Engineering and Environmental Problems, 1041–1055.
Carnevale, M., J. Hager, J. W. Brinkmann, and B. R. Jones, 2005, MASW and GPR survey to delineate depth to bedrock and crystal cavities for mineral exploration, Hiddenite, North Carolina: Pro-ceedings of the Symposium on the Application of Geophysics to Engineering and Environmental Problems, 1051–1060.
Ivanov, J., R. D. Miller, N. Stimac, R. F. Ballard Jr., J. D. Dunbar, and S. Smullen, 2006, Time-lapse seismic study of levees in southern New Mexico: 76th Annual International Meeting, SEG, Expand-ed Abstracts, 3255–3259, http://dx.doi.org/10.1190/1.2370207.
Miller, R., J. Xia, and C. B. Park, 1999, MASW to investigate subsid-ence in the Tampa, Florida area: Kansas Geological Survey Open-fi le Report No. 99–33.
Park, C. B., R. D. Miller, and J. Xia, 1999, Multichannel analysis of surface waves: Geophysics, 64, no. 3, 800–808, http://dx.doi.org/10.1190/1.1444590.
Suto, K., 2012, MASW surveys in fi ll sites in Australia: Proceedings of the 1st Symposium of Korean Society of Exploration Geophysi-cists.
Suto, K. and I. Cenic, 2012, An MASW survey at a site with high-velocity uncontrolled fi ll: A case history: 74th Conference and Ex-hibition, EAGE.
Suto, K. and D. Lacey, 2011, An application of multichannel analysis of surface waves (MASW) to a landfi ll site: A case history: Pro-ceedings of the 10th SEGJ International Symposium.
Suto, K. and B. Scott, 2009, 3D treatment of MASW data for moni-toring ground improvement at an uncontrolled fi ll site: Proceed-ings of 20th ASEG Conference and Exhibition.
Acknowledgments: Th e case histories described here have been presented at ASEG, SEGJ, EAGE, and KSEG meetings. Th e original presentations were given under the permission of the Au-recon Australia Pty. Ltd. and URS Australia Pty. Ltd. Th e author acknowledges these companies. Particular thanks are due to the coauthors of the original presentations: Brendan Scott of URS (now of the University of Adelaide), David Lacey (SKM), and Ivana Cenic (Aurecon). Th anks also to Peter Mitchell and Maria Pham of Aurecon Australia for their assistance.
Corresponding author: [email protected]
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Nonreflection seismic and inversion of surface and guided waves
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Resolving complex structure in near-surface refraction seismologywith common-offset gathers
Common-off set gathers (COG) have been usefully employed in the processing of seismic refl ection data
(Fulton and Darr, 1984). COG methods have also been employed to a lesser extent with the processing of seismic refraction data (Coppens, 1985), but their wider application has yet to be fully exploited.
Th is study describes novel COG adaptations of the gen-eralized reciprocal method (GRM) (Palmer, 1981, 2010b). Initially, the COG GRM methods (Palmer, 2012) were de-veloped as simple and convenient methods for the quality assurance of the large volumes of refraction data, which are characteristic of statics computations for high-fold CMP re-fl ection data processing. However, their application to a va-riety of sets of data has demonstrated a number of additional unanticipated benefi ts.
Perhaps the most useful benefi t is the convenient deter-mination of the crossover distance, which provides a use-ful measure of the range of possible seismic velocities in the weathered zone. Th e crossover distance facilitates the valida-tion of vertical velocity gradients and, in turn, diving waves, which are commonly assumed with many implementations of refraction tomography (Palmer, 2013).
Another unanticipated benefi t, which is the subject of this study, is the improved resolution of the base of the weathering in regions where the structural complexity results in more than a single refracting interface. In particular, this study demonstrates that the COG GRM time model can sig-nifi cantly enhance the delineation of low-angle thrusts.
COG GRM gathers are generated by implementing the algorithms with a systematic increase in the source-to-source distance, with the objective of progressively delineating in-creasingly deeper interfaces. Th e COG GRM gathers at each station are then stacked which, in this study, corresponds with the generation of histograms of the scalar COG GRM attributes at each station.
Conceptually, the COG GRM stacks can be viewed as analogous to the tau-p or slant stack (Barton and Jones, 2003). By contrast, the COG GRM stacks can delineate de-tailed spatially varying parameters through the explicit iden-tifi cation of forward and reverse traveltimes in the algorithms.
Th e methodology is applied to regional seismic refl ec-tion data recorded across part of the Palaeozoic Lachlan Fold Belt in southeastern Australia (Jones and Drummond, 2001; Barton and Jones, 2003). Th e data are taken from traverse 99AGS-L1, which is ~47 km in length. Th e analysis focuses on the ~57,000 traveltimes, which cover a 12-km section be-tween stations 1750 and 2050. Th is section crosses the fl ood plain of the Lachlan River, for which the weathered layer consists of unconsolidated Tertiary alluvium. Th e section also crosses the Marsden Th rust, which is a major structural fea-ture, in the vicinity of station 1800.
DERECKE PALMER, University of New South Wales
Th is study demonstrates that the wavepath eikonal traveltime (WET) refraction tomograms (Schuster and Quin-tus-Bosz, 1993), generated with the default starting model consisting of smooth vertical velocity gradients, have misfi t errors which are comparable to those for a variety of WET tomograms generated with the GRM and the COG GRM, using both uniform velocities and vertical velocity gradients. Th ese results challenge the usefulness of simplistic compari-sons of misfi t errors for diff erentiating acceptable tomograms. Furthermore, they illustrate the ubiquity of nonuniqueness and, in turn, the necessity for explicitly validating the starting model.
COG GRM time model algorithm Th e COG GRM time model algorithm, which is shown in Equation 1, using the symbols in Figure 1, is essentially the same as the common reciprocal method (also known as the ABC, plus/minus and Hagiwara’s methods). Th e diff erence is that only a single value is computed with each source separa-tion for the receiver midway between the two sources, that is FG = GR.
FRGRGFG tttt −+= 2
1 ( ) (1)
With most high-fold CMP data, it is possible to average all traveltimes, such as the traveltime from the source at F to the receiver at G, with the traveltime from the source at G to the receiver at F. To a reasonable approximation, this use of reciprocity can generate useful results with single-ended data, as are usually acquired with land and marine streamers, or where source points are missed occasionally.
No corrections are made to accommodate the eff ects of extended receiver arrays or any off sets or uncertainties in the source location, which can often occur with surface sources. Furthermore, no corrections are made to accommodate any crooked-line geometry.
Figure 1. Schematic raypaths used to compute the COG GRM time model.
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Th e Lachlan data were acquired with a source separation of 40 m. However, the increment in source separation for the COG GRM time model (distance FR in Figure 1) is in-creased in multiples of twice the station spacing, that is, 80 m. From the minimum source-to-source off set of 80 m to the maximum source-to-source off set of 4800 m, a maximum of 60 COG GRM time model profi les can be generated.
Figure 2 presents the unstacked COG GRM time model gathers for the source separations, ΔVP of 80 m to 1200 m in increments of 80 m, and then to 4400 m in increments of 400 m. Also, the mode for the stacked time models in Figure 3 is shown. Th e edited range of time model profi les in Figure 2 has been selected because the inclusion of all the computed values can be confusing with this style of presentation. Fig-ure 3, which presents the stacked COG GRM time model obtained with the full range of source separations of 80 m to 4800 m, is much clearer.
For small source separations of 80–320 m, the time mod-el of ~5–20 ms between stations 1800 and 2050 is represen-tative of the water table in the unconsolidated Tertiary allu-vium. Th is is supported by the COG GRM seismic velocities of ~1650 m/s for source separations of 240 m and 320 m, as will be discussed below. Between stations 1750 and 1800, the weathered layer is composed of in-situ weathered Palaeozoic meta-sediments.
As the source separation increases, the traveltimes are representative of deeper refractors, such as the subweathered zone. However, between stations 1800 and 2050, there is an intermediate source range of between 320 m and 1200 m where the traveltimes are from diff erent layers and the com-puted values are meaningless. Th ey can be recognized as the low-count values intermediate between the true time models of the diff erent layers in Figures 2 and 3.
Th e clustered time model values, which range from ap-proximately 8 ms to 120 ms, are representative of the base of the weathering. Th ey have been computed with source
Figure 2. Unstacked COG GRM time model gathers for a selected range of source separations.
Figure 4. COG GRM time models and seismic velocities averaged over limited ranges of source-to-source separations.
Figure 3. COG GRM time model and seismic velocity histograms.
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separations generally greater than approximately 1200 m. Th e average standard deviation at each station is ~4–5 ms, but it increases to ~10 ms in the vicinity of station 1900, where there are two distinct populations. Elsewhere along the ~47-km traverse, the standard deviation is commonly less than 2 ms where the weathered layer consists of weathered Palaeozoic meta-sediments.
Figure 4 presents the COR GRM time model averaged over fi ve ranges of source separations. It demonstrates that the shallower time model between stations 1860 and 1910 has been computed with near-trace traveltimes, whereas the deeper time model has been computed with far-trace trav-eltimes. All starting models for the COR GRM and GRM WET tomograms have been computed with the time models computed with the near-trace traveltimes.
COG GRM velocity analysis algorithmTh e COG GRM refractor velocity analysis algorithm, which is shown in Equation 2 using the symbols in Figure 5, employs a novel four-term modifi cation of the standard GRM veloc-ity analysis algorithm. As with the COG GRM time model algorithm, only a single value is computed with each source separation for the receiver at G, which is midway between the four source locations.
CDBCABwherettttBCGV
CDABADBC==−−+= 2( () ) (2)
Th e accuracy of individual times can be improved
through averaging with the appropriate reciprocal values. Furthermore, the algorithm is also eff ective with single-ended traveltime data. In contrast to the COG GRM time model algorithm, however, the COG GRM velocity analysis algo-rithm is computed with a total source separation AD which increases in increments of three times the station spacing.
Figure 3 presents the histogram at each station of all the COR GRM refractor velocities for total source separations from 120 m to 4800 m. For small source separations of 240 m and 360 m between stations 1800 and 2050, the seismic velocities are ~1650 m/s, which is representative of the water table in the unconsolidated Tertiary alluvium.
With the increasing source separation, there is an increase in the depth of investigation and eventually, the seismic ve-locities are representative of the subweathered zone. However,
there is an intermediate range where the computed seismic velocities are meaningless, because the arrivals are from dif-ferent layers. Th ese values are recognized by the low counts in Figure 3.
Figure 3 shows that the base of the weathering occurs where the seismic velocities are approximately 4500±100 m/s, for the range of source separations of 1080 m to 4800 m. Figure 4 shows that the lower seismic velocities, especially those at stations 1820 and 1990, have been computed with the near-trace traveltimes. All starting models for the COR GRM WET tomograms have been computed with the seis-mic velocities computed with the near trace traveltimes.
Default WET tomograms—smooth vertical velocity gradientsTh e default WET tomogram is presented in Figure 6 for 5, 20, and 50 iterations. Figure 7 shows that the rms mis-fi t errors achieve a minimum value after about 20 iterations, which is the default.
Th e 4200 m/s contour is inferred to represent the base of the weathering at a depth of approximately 300 m, on the ba-sis of the COG GRM seismic velocities in Figure 3. Th e con-tours of the seismic velocities in the weathered layer, which indicate a vertical velocity gradient of ~10 m/s per meter, are essentially parallel to the base of the weathering, suggesting
Figure 5. Schematic raypaths used to compute the COG GRM seismic velocity.
Figure 6. Default WET tomograms, which employ smooth vertical velocity gradients for the starting model, after 5, 20, and 50 iterations.
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a weathering profi le. If the contours of the seismic velocities represented the normal compaction of unconsolidated clastic sediments, then they would be parallel to the surface topog-raphy. In that case, the vertical velocity gradients would be more moderate and vary as the one sixth power of depth (Z1/6) (Palmer, 2001b). Dobrin (1976) quotes an increase of 1 m/s per metre for Gulf Coast sediments. Barton and Jones (2003) quote depths to slightly weathered bedrock of up to 150 m.
Figure 8 is an expanded section of the WET tomogram for the 4-km interval between stations 1800 and 1900. Th e Marsden Th rust occurs in the vicinity of station 1825 and an adjacent low angle thrust surface, as is indicated in Figures 3 and 4, occurs at station 1865.
COG GRM WET tomogramsFigure 9 presents the stacked seismic refl ection section. It shows strong refl ections dipping to the right at an angle of approximately 15°. Accordingly, COG GRM and GRM starting models for WET tomography have been computed with both vertical and dipping interfaces in the subweathered region.
In general, the lateral resolution of the seismic velocities in the subweathered region with the COG GRM WET to-mograms is largely independent of whether vertical or dip-ping interfaces are employed in the subweathered region. For that reason, only the WET tomograms computed with dip-ping interfaces are presented here.
Figure 10 presents the COG GRM WET tomogram computed with uniform seismic velocities of ~1650 m/s in the weathered layer, after 5, 20, and 50 iterations. Th e au-thor’s preference is for the tomogram generated after fi ve it-erations because it exhibits the best resolution of the seismic velocities in the subweathered region.
Nevertheless, it is recognized that this may not necessarily be a widely held position, given that precision, that is, any measure of the misfi t errors, is often considered to be more important than accuracy, that is, the validity of the model. For that reason, the COG GRM tomogram generated af-ter 20 iterations is presented because the misfi t errors have
stabilized and they are comparable with those achieved after considerably more iterations.
Furthermore, the WET tomogram after 50 iterations demonstrates that excessive numbers of iterations can signifi -cantly reduce the resolution to a minimum that is compara-ble to the highest resolution achieved with the default WET
Figure 7. Th e rms errors for all WET tomograms computed in this study.
Figure 8. Detail of the default WET tomograms, which employ smooth vertical velocity gradients for the starting model, after 5, 20, and 50 iterations.
Figure 9. Stacked refl ection section.
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Figure 10. COG GRM WET tomograms, which employ uniform seismic velocities in the weathering for the starting model, after 5, 20, and 50 iterations.
Figure 11. Detail of COG GRM WET tomograms, which employ uniform seismic velocities in the weathering for the starting model, after 5, 20, and 50 iterations.
Figure 12. COG GRM WET tomograms, which employ hyperbolic velocity gradients in the weathering for the starting model, after 5, 20, and 50 iterations.
Figure 13. GRM WET tomograms, which employ uniform seismic velocities in the weathering for the starting model, after 5, 20, and 50 iterations.
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tomogram in Figure 6. It represents the tomogram with the minimum achievable resolution.
Th e WET tomograms generated after 20 and 50 iterations show the systematic imposition of vertical velocity gradients in the weathered region. Th ese vertical velocity gradients are ap-proximately an order of magnitude greater than those which are representative of saturated unconsolidated clastic sediments.
Figure 11 is an expanded section of the WET tomogram for the 4-km interval between stations 1800 and 1900. Th e Marsden Th rust, which occurs in the vicinity of station 1825, exhibits a reduced seismic velocity. Th e adjacent thrust sur-face, which occurs at station 1865, as is indicated in Figures 3 and 4, exhibits both a steep off set in depth of the base of the weathering at station 1865 and a decrease in the seismic velocities. It supports the interpretation that the deeper time model between stations 1860 and 1910 in Figures 3 and 4 represents arrivals refracted from the higher-velocity dipping interface, whereas the shallower time model represents arriv-als refracted from the base of the weathering.
Figure 12 presents the COG GRM WET tomograms in which the seismic velocities in the weathered layer have been modelled with the hyperbolic velocity function (Palmer, 2010a, 2012). Th e hyperbolic velocity function represents the maximum vertical velocity gradient which is consistent
with linear traveltime graphs. It is similar in magnitude to the vertical velocity gradient assumed in the default starting model in Figures 6 and 8. Figure 12 demonstrates that there is no signifi cant variation in the seismic velocities in the sub-weathered region, when compared with the uniform velocity model in Figure 10.
GRM WET tomogramsFigure 13 presents the GRM WET tomogram with uniform seismic velocities in the weathered layer after 5, 20, and 50 iterations. As might be anticipated, there is some improve-ment in the spatial resolution of the seismic velocities in the subweathered regions, such as in the vicinity of stations 1770 and 1870. Th is is confi rmed with the expanded section in Figure 14.
Nevertheless, the resolution of the GRM WET tomo-grams in Figures 13 and 14 is somewhat underwhelming, especially because a more detailed starting model of the seis-mic velocities was generated with a multichord algorithm (Palmer, 2010b). Given that the distances involved are of the order of several kilometers, and that the average thickness or “wavelength” of the individual geological units is of the order of ~100+ m, it is reasonable to anticipate a signifi cant increase in resolution with the GRM WET tomograms.
Figure 14. Detail of GRM WET tomograms, which employ uniform seismic velocities in the weathering for the starting model, after 5, 20, and 50 iterations.
Figure 15. Detail of GRM WET tomograms, which employ uniform seismic velocities in the weathering for the starting model and vertical interfaces in the subweathering, after 5, 20, and 50 iterations.
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In addition to the fi rst-break arrivals, Figure 16 exhib-its several dipping events which originate at the base of the weathering and which are consistent with dipping events in the stacked refl ection section in Figure 9. Note that the verti-cal scales in Figures 16–18 are one-way time, whereas it is two-way time in Figure 9.
Figure 17 presents the stacked refraction convolution sec-tion (RCS) (Palmer, 2001a, 2001b, 2009) for the far traces, together with the multifold GRM time model derived from all of the traveltime data. Th e essential process for the gen-eration of the time model in Equation 1 is the addition of the two scalar traveltimes, tFG and tRG. By contrast, the RCS achieves the equivalent process through the convolution of the corresponding seismic traces, because convolution adds phase, that is traveltimes, and multiplies amplitudes. Stacking with the RCS enhances any genuine events including later arrivals, it attenuates any “cross-convolution” artifacts (de Franco, 2005), and it improves signal-to-noise ratios prior to picking any fi rst-break traveltimes.
In this case, the GRM has generated an average time model in the region between stations 1850 and 1950, where the COG GRM time model in Figure 3 indicates that there are two refracting interfaces. In contrast, the RCS shows a
Figure 16. Full waveform common-off set section employing traces with a 60-station off set.
Figure 15 shows an expanded section of the GRM WET tomogram in which vertical interfaces in the subwe-athered region have been employed. Th e spatial resolution is considerably better than is the case for the GRM WET tomograms which employ dipping interfaces. It can be concluded that WET tomograms with vertical boundar-ies might usefully be generated where the detailed spatial resolution of the seismic velocities in the subweathered region is an important objective, even where the known geological structure indicates dipping interfaces. Th e stacked refl ection section in Figure 9 suggests that vertical interfaces might be more appropriate for station numbers greater than approximately 1950.
Full waveform refraction sectionsFigure 16 shows a full waveform COG for a 60-station (2400-m) off set. It has been obtained with a novel algorithm which essentially generates the equivalent of the time model obtained with the COG GRM or the standard GRM. Th is algorithm facilitates stacking of multiple COG sections, which can be useful in areas where the signal-to-noise ratios are poor, such over sand dunes or where the weathered layer is saturated with gas (Palmer, 2010c).
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Figure 18. Near-trace stacked refraction convolution section employing traces with 20–25 station off sets.
Figure 17. Far-trace stacked refraction convolution section employing traces with 30–90 station off sets, and multifold GRM time model.
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small off set, representative of the low-angle thrust, at station 1880. Figure 17 also exhibits dipping events below the base of the weathering, as well as the ubiquitous multiples which commonly affl ict most seismic data.
Figure 18 presents the stacked RCS for the near traces. Th e dipping events within the subweathered region can still be recognized, although the signal-to-noise is relatively poor because of the low fold. More importantly, the off set in the base of the weathering at station 1880 is more pronounced.
Th e resolution of seismic refraction data can be described in terms of a Fresnel zone, equivalent to that commonly employed with seismic refl ection data. Kvasnička and Červený (1996) show that the penetration D, within the subweathering is given by:
LLTVD n λ2
121 ≈≈ (3)
where Vn is the seismic velocity in the subweathering, T is the period, λ is the wavelength, and L is the length of the ray-path in the subweathering.
Using representative values of 5000 m/s for the seismic velocity in the subweathering, a dominant frequency of 25 Hz, and a source-to receiver distance of 100 stations, that is, 4000 m, the depth of penetration with Equation 3 is approxi-mately 450 m. Th is depth represents a time of approximately 0.1 at the seismic velocity in the subweathering, and in turn,
a time of 0.1 in the RCS, because the RCS represents a one way time model.
Th erefore, it can be concluded that the raypaths of the later arrivals within the subweathering have suffi cient pene-tration to refl ect any variations of a signifi cant vertical extent. However, it does not necessarily support the occurrence of fi rst arrivals consisting of diving waves.
ConclusionsCOG adaptations of the GRM can provide an extremely rapid and convenient method for assessing the large volumes of refraction data which are characteristic of high fold seismic refl ection data. To a large extent, the COG GRM provides a simple approach to parameterizing the traveltime data into the various layers, a process which often can be daunting to inexperienced refraction seismologists.
In general, the time model of the base of the weathering is usually as precise as those derived with more computationally intensive inversion methods, together with one application of residual statics (Palmer, 2009, 2012). Furthermore, the COG GRM time model is reasonably insensitive to even moderate departures from straight line geometry.
Conceptually, the COG GRM can be viewed as an alter-native to the standard tau-p methods. By contrast, the COG GRM is more resilient to most spatial variations in depths and seismic velocities.
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In areas of moderate structural complexity, such as the area examined in this study, the COG GRM can be useful in identi-fying unusual refraction arrivals, such as those associated with the low-angle thrust surface in the vicinity of station 1880. Both the COG GRM time models and the seismic velocities improved the resolution of the WET tomograms.
Furthermore, the introduction of dipping interfaces in the subweathering enhances the geological verisimilitude of the WET tomograms. However, it can result in a loss of spa-tial resolution where detailed starting models are generated with the GRM.
Th e determination of representative dip values for any boundaries in the subweathered region can be derived from a number of sources. It can include known geology, or even a processed seismic refl ection section, as was the case in this study. Where later events occur, this study and Palmer (2010c) demonstrate the usefulness of full waveform refrac-tion methods, such as common-off set sections and the stacked RCS. In fact, it can be concluded that an alternative inversion strategy might involve the application of standard refl ection processing operations, such as migration or imaging, to the full waveform refraction sections, using the GRM and COG GRM derived velocity models.
Th ere is a common expectation that refraction tomogra-phy will improve the resolution of the starting model. Th at occurred with the low-resolution default starting model con-sisting of smooth vertical velocity gradients. However, the resolution is considerably less than the maximum resolution achieved with either the COG GRM or the standard GRM. In fact, the maximum resolution achieved with the default start-ing model is comparable to the minimum resolution achieved with the GRM alternatives after excessive processing. It can be concluded that a detailed starting model is essential where the survey objectives specify a detailed tomogram.
Although the study area consists of meta-sediments, the relatively high seismic velocities are comparable to those characteristic of carbonates and evaporates. Accordingly, the methods described in this study might be usefully applied in North Africa and the Middle East, where such lithologies are common.
Common-off set traveltime and full waveform methods constitute convenient and eff ective approaches for the rou-tine processing and analysis near-surface seismic refraction data. Th e results constitute useful models of the near surface in their own right. Furthermore, they can be used as starting models for further detailed tomographic inversion.
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regolith structure: 16th Geophysical Conference and Exhibition, ASEG, Extended Abstract.
Coppens, F., 1985, First arrival picking on common-off set trace col-lections for automatic estimation of static corrections: Geophysical Prospecting, 33, 1212–1231.
De Franco, R., 2005, Multi-refractor imaging with stacked refraction convolution section: Geophysical Prospecting, 53, 335–348.
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Palmer, D., 2009, Integrating short and long wavelength time and amplitude statics: First Break, 27, no. 6, 57–65.
Palmer, D., 2010a, Non-uniqueness with refraction inversion—a syn-clinal model study: Geophysical Prospecting, 58, 203–218.
Palmer, D., 2010b, Non-uniqueness with refraction inversion—the Mt Bulga shear zone: Geophysical Prospecting, 58, 561–575.
Palmer, D., 2010c, Imaging the base of the weathering by stacking shot records: 21st Conference and Exhibition, ASEG, Extended Abstract.
Palmer, D., 2012, Generating starting models for seismic refraction tomography with common off set stacks: Exploration Geophysics, 43, 242–254.
Palmer, D., 2013, Validating vertical velocity gradients in near-surface refraction seismology: 26th Conference and Exhibition, SAGEEP, Extended Abstract.
Schuster, G. T. and Quintus-Bosz, A., 1993, Wavepath eikonal travel-time inversion: theory: Geophysics, 58, 1314–132.
Acknowledgements: I thank Leonie Jones from Geoscience Australia for the processed seismic refl ection section in Figure 9.
Corresponding author: [email protected]
2013 Near Surface Honorary Lecturer
Surface wave analysis for near-surface characterization: Introduction, theme and variations Presented by Valentina SoccoPolitecnico di TorinoTurin, Italy
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The joint analysis of refractions with surface waves (JARS) method for fi nding solutions to the inverse refraction problem
The joint analysis of refractions with surface waves (JARS) method off ers an approach for fi nding solutions to the
nonunique inverse refraction problem but, more specifi cally, to the inverse fi rst-arrival traveltime problem (IFATP) because it includes the direct wave and excludes refractions that are not fi rst arrivals.
Th e inverse refraction problem is well known and clearly established to be nonunique (Slichter, 1932; Healy, 1963; Ackermann et al., 1986; Burger, 1992; Lay and Wallace, 1995). However, it wasn’t until Ivanov et al. (2005) exam-ined nonuniqueness from the perspective of solving inverse problems that it became clear that the objective function (the one minimizing the diff erence between the observed and the modeled data) did not have a global minimum (i.e., a unique solution), or only a few global minima, but a continuous range of minima (i.e., a valley of possible solutions). Insight into the signifi cance of the problem was gained from experiments that maintained a constant number of parameters when solving the inverse problem (Ivanov et al., 2005). Fur-thermore, these observations were shown to apply even when dealing with a simple (very few parameters) three-layer model.
Ivanov et al. (2005) used a two-layer model, used also by Burger (1992), and showed that if the fi rst layer was split into two parts, and the velocity of the new second layer was changed, there will be a correspond-ing thickness such that the fi rst arriv-als will remain analytically the same (Figure 1a). While retaining this fi rst-arrival pattern for both models is conceivable with the addition of a low-velocity layer (LVL) (Figure 1d), it does not seem intuitively feasible when the new second layer’s velocity increases from that of the fi rst, while still remaining lower than the third layer (Figure 1b). In this new three-layer model, there will be refractions from this second layer but they will not appear as fi rst arrivals (Figure 1a and Figure 1b). Th e refractions from the second layer will be hidden among other interfering waves that are typically observed on near-surface seismograms (such as guided waves,
JULIAN IVANOV, J. TYLER SCHWENK, and SHELBY L. PETERIE, Kansas Geological SurveyJIANGHAI XIA, China University of Geosciences
surface waves, etc.). As a result, this layer will remain “hidden.”Th ese two possibilities have been known since the fi rst
investigations of refraction nonuniqueness. Still, a new aspect of this phenomenon is the fact that the new second layer can be contracted or expanded while adjusting its velocity such that the fi rst arrivals remain unchanged. From an inversion perspective, this means that even the simplest IFATP will not have a global minimum but a valley (Figure 2) of global minima (i.e., equally possible solutions). Th is is the case even when dealing with infi nite, exact data and exact models. In comparison, fi nite data, and errors in the data and the model, are the widely perceived sources of nonuniqueness (Backus and Gilbert, 1967; Backus and Gilbert, 1968; Backus and Gilbert, 1970).
Th ese nonuniqueness valleys shed new light on the vari-ability in refraction and refraction tomography solutions for a
Figure 1. A simple, refraction nonuniqueness problem demonstrated using refraction traveltimes from a set of receivers and a common source. (a) Th ree diff erent three-layer models can generate the same fi rst arrivals. Layers 1 and 3 have the same thickness and velocity parameters in each model, while the parameters vary for layer 2. (b) Layer 2 is a high-velocity layer. (c) Layer 2 has the same velocity as layer 1. (d) Layer 2 is a low-velocity layer. Th e fi gure is derived from Burger (1992). Th e second layer thickness is rounded for better display. Adapted from Ivanov et al. (2006).
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possible IFATP solutions were found without the benefi t of the reference model derived from VS. One solution used the GRM refraction method (Palmer, 1981). Th e other two were refraction-tomography solutions that used the GRM solution as an initial model. One of these refrac-tion-tomography solutions used second-degree smoothing regularization (Delprat-Jannaud and Lailly, 1993); the other used fi rst-degree smoothing regularization (the most common type) and was obtained with FathTomo software (software from Green Mountain Geophysics). All three tomography solutions converged to an almost identical rms error.
All acquired solutions were visually examined and evaluat-ed based on how well they matched the geologic expectations
single model. It has been shown that each inversion algorithm is guided to a solution by selecting a location in the nonu-niqueness valley based on its intrinsic assumptions, settings, and regularization parameters. For example, when analyzing fi rst-arrival data with only two apparent velocity slopes, con-ventional refraction algorithms (e.g., Palmer, 1981) will most likely chose a two-layer model. Refraction tomography algo-rithms that use multicell models need to use regularization to deal with inversion issues such as indeterminacy, instability, etc. (Constable et al., 1987; Meju, 1994). Th e most popular regularization is smoothing but there are other types of regu-larization, such as minimal-gradient support (Portniaguine and Zhdanov, 1999), each of which would eff ectively pick a diff erent location in the IFATP valley.
Th e JARS methodTh e shear-wave velocity (VS) model estimated from surface-wave analysis can be used to select a location in the nonu-niqueness valley when there are no hard data (e.g., well logs) pertaining to the Earth model of a site (which is commonly the case). We suggest having the inversion progress from a model based on the evaluation of real data (i.e., surface waves) is more desirable than making a generalized assump-tion (i.e., vertical gradient, several-layer model). When ap-plying the JARS method for the study of compressional-wave fi rst arrivals, a pseudo compressional-wave velocity (VP) model can be derived from a VS model estimated from surface-wave analysis.
We use the multichannel analysis of surface waves (MASW) method (Song et al., 1989; Miller et al., 1999; Park et al., 1999; Xia et al., 1999) to obtain a VS model of the sub-surface. VS functions estimated using MASW have reliably and consistently correlated with drill data (Xia et al., 2000). Th is VS model is then used by the JARS algorithm to defi ne an initial model that also serves as a reference to constrain the solution of the inverse problem.
Th e initial pseudo VP model may be scaled from the 2D VS model using several (Ivanov et al., 2010) approaches (the best when have a-priori information about the approximate VP/VS trend at a site) with fi rst arrivals that roughly match the real data. Th e pseudo VP model, obtained in this fashion, can also be employed as a reference model (Figure 2), which limits the solution range and is controlled by weighting dur-ing the inversion process (Ivanov et al., 2006).
ApplicationsSeismic data collected in the Sonora Desert, Arizona, USA, proved an eff ective test for the JARS method (Ivanov, et al., 2006). Th e entire data set was recorded using a fi xed spread of 240 receivers with each receiver spaced at 1.2 m. First arrivals were characterized by two apparent slopes. A JARS tomography solution was ob-tained. For comparison, three other
Figure 2. A 2D map of the mismatch error (aka objective) function. Conceptual continuous nonuniqueness visualized in 2D (i.e., using two parameters, p1 and p2), which can be viewed as a valley in the 2D map. In the absence of approximate information about p1 or p2 to be able to fi nd the true solution Strue, an inaccurate reference parameter, S0, can be used to fi nd a point in the valley that is closest Sest. Adapted from Ivanov et al. (2006).
Figure 3. First-arrival picks from a compressional-wave seismic-shot record recorded at the levee crest in southern New Mexico. Adapted from Ivanov et al. (2010).
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for the site. Th e JARS solution appeared most geologically realistic, possessing channel-like features not observed on the other solutions. Also, the VP/VS ratio map, derived from the JARS solution, was most realistic relative to other data from the site. In the absence of well logs or other hard data, this qualitative assessment is plausible because all the IFATP so-lutions can be considered equally possible from a numerical perspective. Th e various solutions can be viewed as points along a multidimensional (multiparameter) nonuniqueness valley, similar to the simple one (with two parameters only) from Figure 2.
Th e intriguing JARS results prompted the application of this method to other sites, including several levee sites along the Rio Grande in the San Juan quadrangle of Texas and the La Mesa quadrangle of New Mexico (Ivanov et al., 2010). Similar to the fi rst example in the section, the fi rst-arrival data had only two apparent velocity trends at all sites (Figure 3).
Comparing the JARS 2D VP solution with conventional refraction tomography (Figure 4), the JARS solution ap-pears to possess more detail and has a texture consistent with the expected depositional geometries in these river valleys. After close examination of the JARS image, three distinct
Figure 4. Southern New Mexico 2D images from analysis of compressional-wave fi rst-arrival seismic data acquired at the levee crest. (a) Th e JARS compressional-wave solution, with low-velocity intervals, A, B (red arrows), and C (red ellipse). (b) Conventional compressional-wave refraction-tomography solution. Red rectangles at the very top indicate the 3-m levee. Blank areas within the images indicate lack of ray coverage and are intentionally left unrefi ned. Adapted from Ivanov et al. (2010).
Figure 5. Yuma, Arizona, 2D image from shear-wave seismic data fi rst-arrival JARS solution with multiple low-velocity intervals at 2 m, 7 m, 11 m, and 14 m. Blank areas within the image indicate lack of ray coverage and are intentionally left unrefi ned.
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low-velocity zones appear layered between high-velocity layers. Th e velocity inversion with the greatest contrast is between 9 and 11 m below ground surface. Another low-con-trast, low-velocity layer is evident between 7 and 8 m.
Velocity inversions of these types are consistent with the depositional history along the Rio Grande Valley, where al-ternating sandy and clayey sand deposits are encountered in drill holes. Th e top 3 m imaged by the JARS method is con-sistent with known levee construction methods at this site. As a general rule, a higher-velocity levee core at a depth of 1–2 m sits above a lower-velocity native material base. Comparisons of JARS and conventional tomography methods consistently demonstrated similar patterns and characteristics across all levee sites.
Th e JARS method can also be applied in the analysis of shear-wave fi rst-arrival data. Using JARS for this applica-tion is simpler because both the refractions and surface-wave analysis are attempting to solve the same parameter, VS, and no VP/VS relationship is needed. Such an approach would be appropriate for calculating high-resolution VS maps beyond what can be achieved using only the MASW method.
Surface-wave propagation and associated particle motions tend to smear spatial sampling as a function of depth (wave-length). Th is smearing phenomenon decreases the VS lateral resolution and therefore smooths the fi nal 2D VS model. Because the JARS method incorporates the surface-wave VS model as a reference (controlled by weighting) and it does not suff er from the long-wavelength smearing, it can produce a more detailed VS IFATP solution than MASW results alone.
Th e VS-JARS algorithm was applied to shear-wave data collected in the Yuma Desert, Arizona using 248 horizontal 14.5-Hz geophones spaced at 1.2 m. In comparison to the MASW method, the obtained refraction (i.e., IFATP) solu-tion image (Figure 5) had higher resolution. Th e tomogram resolved multiple LVL sequences that we correlate to vari-able material composition (clay, sand, silt). Th is pattern may be seen in nearby well-log samples and is characteristic of alluvial-plain depositional sequences. Th is site supports our previous fi ndings and again demonstrates that the method is capable of resolving complex velocity structures and velocity inversions that many fundamental approaches neglect.
DiscussionTh e VP/VS ratio assumption (regardless of how accurate it is), utilized by JARS to create a pseudo VP model, appears to improve the initial geophysical model and constrains the in-version far better than the purely mathematical assumptions used by conventional refraction (tomography) algorithms. Th is perception is supported through empirical comparisons of JARS results and conventional methods.
For all published case studies, the JARS IFATP solutions appear equally plausible to those obtained using conventional refraction tomography based on depositional settings and local/regional drilling information. Additionally, the JARS method provides IFATP solutions that include low-velocity layers sandwiched between high-velocity layers. Such solu-tions are not possible using conventional IFATP algorithms.
Th e ability of the JARS method to image low-velocity layers comes from the establishment of an overall vertical-velocity gradient from the site-specifi c reference physical model (de-rived from surface-wave VS). Establishment of such a trend is a factor that can improve the vertical resolution of the fi nal tomogram.
ConclusionsComparison of experimental results applying the joint anal-ysis of refractions with surface-waves method (JARS) and conventional refraction-tomography methods consistently suggests the JARS method provides as good or better veloc-ity-fi eld estimations as any of the commonly used methods. Solutions obtained from JARS in all published studies are geologically realistic. Th erefore, JARS can be considered an advancement in the struggle with the nonunique inverse re-fraction-traveltime problem.
Th e JARS technique benefi ts from the use of two data sets (i.e., body-wave fi rst arrivals and surface waves). One ad-vantage of the VP JARS approach is that both VP and VP/VS ratio results must appear realistic to accept the fi nal solution, whereas conventional methods do not consider the VP/VS ra-tio. In relation to the VS JARS application, the method still gives two VS models, which need to be consistent with each other. Th ese qualitative assessments make the method more robust.
Future directions for the area of research may include the use of expert systems for better approximation of VP/VS trends at specifi c sites, the use of fi rst-arrival amplitudes, and full-waveform inversion to help resolve nonuniqueness problem with inverse refraction traveltimes.
ReferencesAckermann, H. D., L. W. Pankratz, and D. Dansereau, 1986, Resolu-
tion of ambiguities of seismic refraction traveltime Curves: Geo-physics, 51, no. 2, 223–235, http://dx.doi.org/10.1190/1.1442082.
Backus, G. and F. Gilbert, 1968, Resolving power of gross Earth data: Geophysical Journal of the Royal Astronomical Society, 16, no. 2, 169–205, http://dx.doi.org/10.1111/j.1365-246X.1968.tb00216.x.
Backus, G. and F. Gilbert, 1970, Uniqueness in Inversion of Inac-curate gross Earth data: Philosophical Transactions of the Royal Society of London Series a-Mathematical and Physical Sciences, 266, 123–145.
Backus, G. E. and J. F. Gilbert, 1967, Numerical applications of a formalism for geophysical inverse problems: Geophysical Journal of the Royal Astronomical Society, 13, no. 1–3, 247–276, http://dx.doi.org/10.1111/j.1365-246X.1967.tb02159.x.
Burger, H. R., 1992, Exploration geophysics of the shallow subsurface: Prentice Hall, Inc.
Constable, S. C., R. L. Parker, and C. G. Constable, 1987, Occam’s inversion—a practical algorithm for generating smooth models from electromagnetic sounding data: Geophysics, 52, no. 3, 289–300, http://dx.doi.org/10.1190/1.1442303.
Delprat-Jannaud, F. and P. Lailly, 1993, Ill-posed and well-posed for-mulations of the refl ection traveltime tomography problem: Jour-nal of Geophysical Research. Solid Earth, 98, B4, 6589–6605, http://dx.doi.org/10.1029/92JB02441.
Healy, J. H., 1963, Crustal structure along coast of California from seismic-refraction measurements: Journal of Geophysical
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Research, 68, no. 20, 5777–5787, http://dx.doi.org/10.1029/JZ068i020p05777.
Ivanov, J., R. D. Miller, J. Xia, J. B. Dunbar, and S. L. Peterie, 2010, Refraction nonuniqueness studies at levee sites using the refrac-tion-tomography and JARS methods, in R. D. Miller, J. D. Brad-ford and K. Holliger, eds., Advances in near-surface seismology and ground-penetrating radar: SEG, 327–338, www.seg.org/docu-ments/10161/78675/ANStoc.pdf.
Ivanov, J., R. D. Miller, J. H. Xia, and D. Steeples, 2005, Th e inverse problem of refraction traveltimes, part II: Quantifying refraction nonuniqueness using a three-layer model: Pure and Applied Geo-physics, 162, no. 3, 461–477, http://dx.doi.org/10.1007/s00024-004-2616-0.
Ivanov, J., R. D. Miller, J. H. Xia, D. Steeples, and C. B. Park, 2006, Joint analysis of refractions with surface waves: An inverse solution to the refraction-traveltime problem: Geophysics, 71, no. 6, R131–R138, http://dx.doi.org/10.1190/1.2360226.
Lay, T. and T. Wallace, 1995, Modern global seismology: Academic Press.
Meju, M. A., 1994, Biased-estimation—a simple framework for in-version and uncertainty analysis with prior information: Geo-physical Journal International, 119, no. 2, 521–528, http://dx.doi.org/10.1111/j.1365-246X.1994.tb00139.x.
Miller, R. D., J. Xia, C. B. Park, and J. M. Ivanov, 1999, Multichannel
analysis of surface waves to map bedrock: Th e Leading Edge, 18, no. 12, 1392–1396, http://dx.doi.org/10.1190/1.1438226.
Palmer, D., 1981, An Introduction to the generalized reciprocal meth-od of seismic refraction interpretation: Geophysics, 46, no. 11, 1508–1518, http://dx.doi.org/10.1190/1.1441157.
Park, C. B., R. D. Miller, and J. H. Xia, 1999, Multichannel analysis of surface waves: Geophysics, 64, no. 3, 800–808, http://dx.doi.org/10.1190/1.1444590.
Portniaguine, O. and M. S. Zhdanov, 1999, Focusing geophysical inversion images: Geophysics, 64, no. 3, 874–887, http://dx.doi.org/10.1190/1.1444596.
Slichter, L. B., 1932, Th e theory of the interpretation of seismic travel-time curves in horizontal structures: Physics, 3, no. 6, 273–295, http://dx.doi.org/10.1063/1.1745133.
Song, Y. Y., J. P. Castagna, R. A. Black, and R. W. Knapp, 1989, Sen-sitivity of near-surface shear-wave velocity determination from ray-leigh and love waves: 59th Annual International Meeting, SEG, Ex-panded Abstracts, 509–512, http://dx.doi.org/10.1190/1.1889669.
Xia, J. H., R. D. Miller, and C. B. Park, 1999, Estimation of near-surface shear-wave velocity by inversion of Rayleigh waves: Geo-physics, 64, no. 3, 691–700, http://dx.doi.org/10.1190/1.1444578.
Corresponding author: [email protected]
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Field testing of fi ber-optic distributed acoustic sensing (DAS)for subsurface seismic monitoring
Distributed acoustic sensing (DAS) is a relatively recent development in the use of fi ber-optic cable for
measurement of ground motion. Discrete fi ber-optic sensors, typically using a Bragg diff raction grating, have been in research and development and fi eld testing for more than 15 years with geophysical applications at least 12 years old (Bostick, 2000, and summary in Keul et al., 2005). However, developments in recent years have sought to remove the need for point sensors by using the fi ber cable itself as a sensor (Mestayer et al., 2011; Miller et al., 2012).
Th rough Rayleigh scattering, light transmitted down the cable will continuously backscatter or “echo” light so that it can be sensed. Because light in an optical fi ber travels at ap-proximately 0.2 m/ns, a 10-ns pulse of light occupies about 2 m in the fi ber as it propagates. Th e potential of DAS is that each 10 nanoseconds of time in the optical echo response can be associated with refl ections coming from a 1-m portion of the fi ber (two-way time of 10 ns). By generating a repeated pulse every 100 μs and continuously processing the returned optical signal, one can, in principle, interrogate each meter of up to 10 km of fi ber at a 10-kHz sample rate. Local changes in the optical backscatter because of changes in the environment of the fi ber can thus become the basis for using the fi ber as a continuous array of sensors with nearly con-tinuous sampling in both space and time.
Because the technology for deploying fi ber-optic cable in boreholes is well developed for thermal sensing (distributed tem-perature sensing, or DTS), a DAS system has the potential of having thousands of sensors permanently deployed in the subsurface, at relatively low cost. DAS systems currently use single-mode fi ber, as opposed to the multimode fi -ber typically used for DTS, but the type of fi ber does not aff ect deployment, and multiple fi bers are easily deployed in a single capillary tube.
Recent advances in opto-elec-tronics and associated signal pro-cessing (Farhadiroushan et al., 2009)
THOMAS M. DALEY, BARRY M. FREIFELD, JONATHAN AJO-FRANKLIN, and SHAN DOU, Lawrence Berkeley National LaboratoryROMAN PEVZNER, CO2CRC, Curtin UniverstyVALERIYA SHULAKOVA, CO2CRC, CSIROSUDHENDU KASHIKAR and DOUGLAS E. MILLER, SilixaJULIA GOETZ, JAN HENNINGES, and STEFAN LUETH, GFZ
have enabled the development of a commercial distributed acoustic sensor (DAS) that actualizes much of this potential. Unlike disturbance sensors, (Shatalin, 1998), the DAS mea-sures the strain on the fi ber to characterize the full acoustic signal. Unlike systems relying on discrete optical sensors (Bo-stick, 2000; Hornby et al., 2005; Keul et al., 2005; Hornby et al., 2008), the distributed system does not rely upon manu-factured sensors and is not limited by a need for multiple fi bers or optical multiplexing to avoid optical crosstalk be-tween interferometers.
We have conducted a series of fi eld tests examining the application of this methodology in borehole and sur-face measurements. Th ese tests are all part of CO2 stor-age monitoring pilots and use the prototype acquisition system, iDAS, developed by Silixa (Miller et al.). Th e fi rst test was undertaken at a site operated by the Southeast Carbon Sequestration Partnership (SECARB), a U.S. De-partment of Energy (DOE)funded pilot in storage and monitoring of anthropogenic carbon within an oil fi eld operated by Denbury Resources in Citronelle, Alabama. Th e second test was part of the Otway sequestration pilot
Figure 1. Flatpack with DAS cable photo (a) and schematic (b). Note that the geophone tubing encapsulated conductor (TEC) cable was deployed separately from the fl atback. (c) Flatpack and wall-locking geophone clamped on tubing.
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The DAS seismic data acquisition at Citronelle was a walk-away vertical seismic profile (VSP) recorded with an early version of the Silixa iDAS system. The initial DAS test, used a ~35,000-lb force vibroseis truck, data were processed with a synthetic lin-ear 16-s sweep from 10 to 160 Hz. From 4 to 6 sweeps were recorded at each source point (Figure 2). A strong tube wave is observed along the entire length of the cable.
We were encouraged to observe that DAS does record seismic energy; however, the DAS recordings do not have sufficient signal-to-noise ratio (SNR) for observing P-waves below approximately 1600 m (the 2.7 km/s event in Figure
project operated by the CO2CRC research organization near Warrnam-bool, Victoria, Australia. The third test was at the Ketzin, Germany, pi-lot storage site currently operated by the German Research Centre for Geosciences (GFZ). In this article, we focus on the DAS data collected and the initial results from each test. Taken together, these three tests demonstrate many potential applications of DAS technology, as well as some current limitations in sensitivity as compared with con-ventional geophone recording. Our testing includes both borehole and surface cable data.
Citronelle field testUse of DAS at Citronelle was facili-tated by the deployment of a modu-lar borehole monitoring (MBM) package on production tubing in the Citronelle D-9-8 monitoring well which included multiple fiber-optic cables and an 18-level clamping geophone string (information available at http://www.co2captureproject.org/reports.html). Deployment in March 2012, and the associ-ated initial seismic testing, was used as an opportunity to acquire DAS data. The DAS fiber was a “fiber in metal tube” (FIMT) which was itself part of a multiconductor cable in-side a molded “flatpack” (Figure 1a) which was clamped to the production tubing, in the fluid–filled annulus between tubing and casing (Figure 1b). The MBM flatpack was de-ployed to a depth of almost 3 km.
Figure 4. MBM tubing-deployed, clamped geophone data (50-ft interval between geophones) from source station 2021 (approximately 700 ft offset) with 60-Hz notch filter and removal of bad traces. Vertical and three-component geophones are labeled with most of the 3C channels removed. A clear P-wave arrival is seen between 500 and 600 ms.
Figure 2. DAS data from tubing-deployed MBM flatpack for a shot point approximately 100 ft from the well. There are two observed wave speeds, 1.4 km/s and 1.3 km/s; this is likely from two modes of tube waves related to the presence of a fluid-filled annulus (Marzetta and Schoenberg, 1985). Depth index is in meters.
Figure 3. DAS data from source station 2021 at Citronelle, approximately 700 ft offset from the D-9-8 sensor borehole. Estimated wave speeds for two events (red and blue lines) are labeled in km/s.
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3), while P-wave energy is easily seen on the clamped geophones at 6000 ft (1.8 km) to 7000 ft (2.1 km) (Fig-ure 4). We felt this result, while not useful for seismic monitoring of the ~2.9-km deep reservoir at Citronelle, was suffi ciently successful to move forward with work on improving ac-quisition and planning for another fi eld test. We plan to return to the Citronelle site for further testing, where the MBM package remains installed and serves as an example of multiple instrument deployment and a test site with geophones and DAS codeployed.
Otway fi eld testTh e Otway Project is operated by the Cooperative Research Centre for Greenhouse Gas Technologies (CO2CRC), a joint venture with the Australian government and other industry and government organiza-tions, to demonstrate that CO2 can be safely transported and stored in geo-logic formations commonly found in Australia. More than 65,000 tons of CO2 were injected and monitored in the ~2-km deep Waarre C formation during the project’s Stage 1. Under Stage 2, a second injection well, the ~1.5-km deep CRC-2, was drilled in 2010 and has been used for injection testing in the Paaratte formation.
CRC-2 has tubing-deployed in-struments, including a fi ber-optic cable
Figure 7. Upgoing energy for the 41-fold VSP data of Figure 6, using the DAS acquisition in well CRC-2. Refl ected energy is observed.
Figure 5. (a) Otway site location. (b) Schematic plan view map of fi ber-optic deployment in well CRC-2 and surface trench (purple line), along with buried geophones used for testing and the local fence line (black line). Receiver spacing along the line for conventional geophones was 10 m.
Figure 6. Shot-gather DAS data from Otway CRC-2 borehole using weight-drop source with 41-fold stack. Th e top ~300 m of the well experiences multiple reverberations (which had been observed on previous geophone VSP data), but below 300 m the P-wave dominates.
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Figure 8. Comparison of P-wave signal-to-noise ratio (SNR) for wireline, clamped geophones and tubing-deployed DAS fiber-optic from separate VSP surveys in the same well. Filtered fiber data has a band-pass filter of 5–180 Hz applied to improve SNR. Different sources were used in the two data sets. To obtain SNR, the root-mean-squared (rms) amplitude values for a 20-ms time window around the P-wave arrival is compared to early time pre-arrival noise.
Figure 9. DAS recording of fiber cable in loop, giving symmetric data. The iDAS channel index is 1-m spacing. The two segments of fiber cable in the main surface trench are channels 96–270 and 818–992.
Figure 10. Comparison of two 1-m segments of cable colocated in a surface trench.
similar to that deployed at Citronelle, but without the flat-pack. Current Stage 2 planning includes testing of perma-nent surface seismic sensors. During one testing session, the iDAS system was brought on-site to record DAS data with a weight-drop surface seismic source. We scheduled an extra source effort to increase potential observation and use of P-wave energy.
In addition to the borehole fiber-optic cable deployed in CRC-2, a surface trench was used to deploy fiber cable and look at surface sensing with DAS (Figure 5). The surface fi-ber cable was looped within the trench so that two parallel lengths were recorded. A set of 25 standard geophones was placed along the same line in 3-m deep vertical boreholes with an additional 25 geophones in spike land cases planted on the surface in a parallel line.
The 720-kg weight drop source was used at an array of source locations, to allow recording of walkaway VSP-type data. One hundred fifty source points were located along the line orthogonal to the receiver line; source spacing along the line was 10 m. Walkaway VSP data were acquired using stacks of 8 shots per shot point position (2 passes of the receiver line, 4 shots in each pass). One additional shot point location (~100 m away from CRC-2) was used to acquire zero-offset VSP using an enhanced stack of 41 individual shots for DAS testing.
Otway borehole DAS dataAt this site, a clear P-wave arrival can be seen (Figure 6). Separation processing of upgoing and downgoing energy was performed (Figure 7). While this upgoing section does not have enough signal-to-noise to allow imaging, the potential is evident in contrast to the previous Citronelle data test.
A conventional VSP has been recorded in the Otway CRC-2, allowing comparisons of signal-to-noise levels (Fig-ure 8). While the two surveys had different sources and sen-sors, we see the difference between the two surveys, while large, is consistent with depth. This demonstrates that the DAS acquisition is not affected by the more than 1.5-km cable length. Because the DAS cable has consistent sensitiv-ity throughout, obtaining increases in DAS SNR could allow comparable data quality to conventional geophone data.
Otway surface DAS dataThe surface fiber cable deployed in a trench (Figure 5) and looped back, allowed direct comparison of the repeatability of two segments (Figure 9). We find good consistency in both amplitude and time (Figures 10 and 11)
We stacked sections of fiber-optic cable of various lengths to optimally create a single receiver. Here we use time de-lays corresponding to the optimum direct-wave stacking. No other processing or filtering is applied to the data. Both di-rect P-wave and strong ground roll are clearly observed in the data (Figure 12). A 4–16-m fiber-optic cable section provides SNR comparable to conventional geophones for the near off-sets (less than ~400 m). However, the amplitude of the signal quickly decays beyond this range of offsets. Keeping in mind that the receiver line is orthogonal to the source line, this fact
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704 The Leading Edge June 2013
Nonreflection seismic and inversion of surface and guided waves
may indicate infl uence of the direc-tivity pattern of the fi ber-optic cable on compressional waves.
Considering the strength of the ground roll (consisting largely of Rayleigh waves) observed in the DAS surface array, similar deploy-ments may have utility for measur-ing near-surface soil properties us-ing multichannel analysis of surface waves (MASW) techniques (Park et al., 1999). Th e P-wave fi rst arrival is detectable but weak on a shot gather from the walkaway line for the source location closest to the DAS fi ber ar-ray sampled at 1 m, an inline geom-etry similar to a traditional 2D refrac-tion or surface-wave survey (Figure 13). A direct S-wave and a strong Rayleigh-wave package are visible on DAS shot gathers. Th e fundamental mode of the most useful surface-wave energy is normally dispersive across a frequency range of 15–23 Hz (Figure 13b). In general, the surface-wave data acquired with iDAS were of rela-tively high quality and amenable to inversion using MASW approaches.
Ketzin fi eld testAt Ketzin, Germany, a pilot CO2 storage project was started in 2004 by the European CO2SINK group (currently operated by GFZ) at a site with multiple monitoring wells (Wuerdemann et al., 2010; Martens et al., 2012). At the Ketzin site, fi ber-optic cables were deployed behind casing in two observation wells. A conventional geophone VSP was previously recorded at this site. As an R&D test, a DAS survey was re-corded. Th e DAS survey used an ac-celerated weight drop source, while the conventional survey used a Vib-sist source from Vibrometric.
Like the “loop” of surface cable at Otway, we were able to use a bore-hole loop at Ketzin, in well Ktzi 202 to acquire VSP data (Figure 14). Th e downgoing P-wave and many refl ec-tions can be seen and are consistent with previous conventional geophone data (Figure 15). Th e DAS data pos-sess strong artifacts resulting from the dual casing completions, with the DAS fi ber on the inner casing.
Figure 11. Cross-correlation results for two sections of surface cable colocated in trench showing, for each meter of cable (trace #), the relative time delay (a) and the normalized cross-correlation coeffi cient (b).
Figure 12. Comparison of iDAS and conventional geophone data in a common-receiver gather. iDAS data stacked to form 1-, 4-, 16-, and 64-m cable segments (taking into account time delays for the direct wave).
June 2013 The Leading Edge 705
Nonreflection seismic and inversion of surface and guided waves Nonreflection seismic and inversion of surface and guided waves
Figure 14. Raw DAS VSP data from upper 300 m in the Ketzin Ktzi 202 borehole for a stack of 92 source drops. DAS channels have 1-m spacing and the fiber cable is installed behind casing. The downgoing P-wave and upgoing reflections can be seen.
Uncemented zones from 177 to 269 m and 583 to 669 m can be seen on all three data sets as a loss of seismic energy recorded (Figure 15). The generally good quality of the DAS data at Ketzin is initially attributed to the behind-casing deployment. Fur-ther analysis and testing will attempt to confirm this conclusion.
Summary and conclusionsDAS seismic acquisition is a recent, and still-developing technology with many potential advantages. Three sites have been tested for DAS acqui-sition of borehole seismic data and one included surface seismic data.
At the Citronelle site, the fiber ca-ble was tubing deployed to 2.9 km in a well coincident with a short string of clamping geophones. The results showed observable seismic energy, mostly tube waves, high-lighting the relative low sensitivity of the fluid-coupled fiber and insufficient SNR to see P-waves to 2.9 km, with a stan-dard source effort (4–6 sweeps of a vibroseis per shot point).
At the Otway site, the fiber was again tubing-deployed in a borehole, but a more energetic source and high stack counts (41 stacks of weight drop) generated more useful VSP data. DAS data from the 1500-m deep well at Otway could be compared to a previously acquired geophone VSP (with different source) and we observed approximately 40–50 dB difference in SNR over the entire length. While this is a large difference, improvement in DAS sensitivity is possible, and some partial SNR improvement can be expected with extra source effort. Additionally, the high spatial sampling of 1 m for DAS provides potential for further noise reduction.
At Otway, we also ran a two-way loop of fiber in a surface trench allowing comparison of side-by-side repeatability from separate segments of cable in a surface seismic geometry. The data were found to be quite repeatable. This implies that mul-tiple runs of fiber could be stacked together to improve SNR, and to allow some redundancy in sensors. Furthermore, the surface cable data are shown to be useful for MASW and pos-sibly directional in sensitivity.
At the Ketzin site, a loop of fiber cable was deployed on casing with some of the cable cemented in place. This pro-vided the best overall data quality, again demonstrating the repeatability of separate segments of fiber cable, and showing the adverse effects of uncemented zones. Comparison with a conventional geophone VSP demonstrated both the effects of a lack of cement (as expected), and the capability of DAS data to record upgoing VSP reflections over the ~700-m depth of the well.
Taken together, these tests demonstrate a variety of de-ployment and acquisition possibilities for DAS recording. Increased sensitivity is still a goal, especially for deep wells or long surface arrays, but both the Otway and Ketzin tests
indicate that current technology can provide useful data with increased source efforts. The Ketzin tests indicate the benefit of cementing the fiber in place or deployment behind casing strings, rather than relying on fluid coupling. We expect that further testing at these sites and processing of these data will advance the development of DAS technology. The potential of large numbers of relatively inexpensive sensors, perma-nently deployed, opens many opportunities to be explored in the future.
ReferencesBostick, F., 2000, Field experimental results of three-component fiber-
optic seismic sensors: 65th Annual International Meeting, SEG, Expanded Abstracts, http://dx.doi.org/10.1190/1.1815889.
Farhadiroushan, M., T. R. Parker, and S. Shatalin, 2009, Meth-od and apparatus for optical sensing: Patent application WO2010136810A2.
Hornby, B., F. Bostick III, B. Williams, K. Lewis, and P. Garossino, 2005, Field test of a permanent in-well fiber-optic seismic system: Geophysics, 70, no. 4, E11–E19.
Figure 13. Section of a trace-normalized DAS shot gather for the inline source (a) and associated normalized dispersion spectrum (b) calculated in the frequency slowness domain. As can be seen in (a), while the refracted P-wave arrival is weak, a direct S-wave and a strong Rayleigh-wave package are visible. The dispersion plot in (b) shows the fundamental mode is normally dispersive from 15 to 23 Hz.
706 The Leading Edge June 2013
Nonreflection seismic and inversion of surface and guided waves
Keul, P. R., E. Mastin, J. Blanco, M. Maguérez, T. Bostick, and S. Knudsen, 2005, Using a fi ber-optic seismic array for well moni-toring: Th e Leading Edge, 24, no. 1, 68–70, http://dx.doi.org/10.1190/1.1859704.
Martens, S., T. Kempka, A. Liebscher, S. Lueth, and F. Möller, 2012, Europe’s longest-operating on-shore CO2 storage site at Ketzin, Germany: a progress report after three years of injection: Environ-mental Earth Sciences, 67, no. 2, 323–334, doi:10.1007/s12665-012-1672-5.
Marzetta, T. and M. Schoenberg, 1985, Tube waves in cased bore-holes: 55th Annual International Meeting, SEG, Expanded Ab-stracts, 34–36, http://dx.doi.org/10.1190/1.1892647.
Mestayer, J., B. Cox, P. Wills, D. Kiyashchenko, J. Lopez, M. Costel-lo, S. Bourne, G. Ugueto, R. Lupton, G. Solano, D. Hill, and A. Lewis, 2011, Field trials of distributed acoustic sensing for geo-physical monitoring: 71st Annual International Meeting, SEG, Expanded Abstracts, http://dx.doi.org/10.1190/1.3628095.
Miller, D., T. Parker, S. Kashikar, M. Todorov, and T. Bostick, 2012, Vertical seismic profi ling using a fi ber-optic cable as a distributed acoustic sensor: 74th EAGE Conference and Exhibition.
Park, C. B., R. D. Miller, and J. Xia, 1999, Multichannel analysis of surface waves (MASW): Geophysics, 64, 800–808.
Shatalin, S. V., V. N. Treschikov, and A. J. Rogers, 1998, Interfero-metric optical time-domain refl ectometry for distributed optical-fi ber sensing: Applied Optics, 37, 5600-5604.
Figure 15. (left to right) Well completion for Ktzi 202, processed VSP data from DAS downgoing fi ber, upgoing fi ber and conventional geophone (with well lithology insert). All seismic data sets have been aligned on downgoing P-wave, indicated by red line on geophone data using a linear moveout (LMO) velocity as labeled. Th e fi ber data had the source at nearby well Ktzi 203 while the geophone survey had the source near the sensor well Ktzi 202. Cemented segments of casing in well diagram are indicated by black sections.
Wuerdemann H., F. Moeller, M. Kuehn, W. Heidug, N. P. Chris-tensen, G. Borm, F. R. Schilling, and the CO2SINK Group, 2010, CO2SINK–From site characterization and risk assessment to mon-itoring and verifi cation: One year of operational experience with the fi eld laboratory for CO2 storage at Ketzin, Germany, Interna-tional Journal of Greenhouse Gas Control, 4, 938–951.
Acknowledgments: Th e authors thank the U.S. Department of Energy, the SECARB partnership, the CO2 CaptureProgram, the Electric Power Research Institute, Advance Resources International, and Denbury Resources Inc. for support of the Citronelle work; the CO2CRC Otway Project for Otway test support; the GFZ and CO2MAN project for Ketzin support. Th is research was partially supported by the assistant secretary for Fossil Energy, offi ce of natural gas and petroleum technology, CSRP/GEO-SEQ Program, through the National Energy Technology Laboratory of the U.S. Department of Energy, under U.S. DOE Contract No. DE-AC02-05CH1123.
Corresponding author: [email protected]
Call for Papers
Venue: Le Meridien Convention
Centre, Kochi, Kerala
Society of Petroleum Geophysicists, India
Call for Papers for
10th Biennial International Conference and
Exposition
23-25 , November, 2013
Venue: Le Meridien Convention Centre, Kochi, Kerala
Theme: Changing Landscape in
Geophysical Innovations
• Invitation for original technical papers • Expanded Abstracts to be uploaded online • Start of Online submission : 20.4.2013 • Last date of submission : 15.7.2013 • For list of Topics & general guidelines visit
www.spgindia.org
708 The Leading Edge June 2013
Geophysics bright spots Coordinated by Ken MahrerGeophysics bright spots Coordinated by Yonghe Sun
This is the last column of my two-year term as the coordinator for Bright Spots. I would like thank you
for tracking the column and tracking new developments in Geophysics. I thank the editors for identifying Bright Spot papers, and thank Editor Tamas Nemeth for this enjoyable assignment.
You might recall that we featured in the September-Octo-ber column a paper by Gallardo et al. on integration of multi-ple geophysical data types (e.g., seismic and EM) using struc-ture-coupled joint inversions. The authors applied a so-called “cross-gradient constraint” in the inversion. Such a constraint favors structural consistencies among the multiple types of model parameters. In the current issue of Geophysics, Lo-chbühler et al. present another appli-cation of structure-coupled inversion. Their twist is to impose a “structural similarity constraint” among differ-ent model parameter types for the joint inversion of geophysical (GPR) and hydrological data.
Structure-coupled joint inversion of geophysical and hydrological data by Tobias Lochbühler, Joseph Doetsch, Brauchler Ralf, and Niklas Linde. In groundwater hydrology, geophysical data promise to provide high resolu-tion and spatially extended coverage of the subsurface. The problem is that geophysical data are not sensi-tive to hydraulic conductivity and that the link between geophysical parameters and hydrological param-eters is often weak, poorly known, nonstationary in space and time, and scale-dependent. The authors pro-pose a joint inversion of geophysical and hydrological data to recover lat-erally extended high-resolution hy-drological models using a “structural similarity constraint.” The constraint is based on the assumption that the spatial distributions of model param-eters have similar patterns within the model domain. Figure 1 shows results of inversions of GPR traveltimes and hydraulic tomography data. In the separate inversions of different indi-vidual data types, the spatial varia-tions of the recovered specific storage model (Figure 1e) appear unrealistic with high and low values concentrat-ed in the center, possibly an artifact of the signal coverage decreasing with distance from the test well. This ap-parent artifact is imprinted into the hydraulic conductivity model (Figure 1g). In the results obtained by joint
inversion with the structural similarity constraint (Figure 1h), this artifact is mitigated.
The following is a list of papers recommended by the As-sociated Editors (AE) for the May-June issue of Geophysics Bright Spots:
1) The use of wavelet transforms for improved interpretation of airborne transient electromagnetic data by Vanessa Nenna and Adam Pidlisecky. AE Richard S. Smith.
2) Analytic solutions to the joint estimation of microseismic event locations and effective velocity model by Emil Blias and Vlad-imir Grechka. AE Shawn Maxwell.
3) The upside of uncertainty: Identification of lithology contact
Figure 1. (Figure 3 of Lochbühler et al.): Inversions of GPR traveltimes and hydraulic tomography data. Results from individual inversions are on the left, and those of the joint inversions on the right. (a) and (b) GPR velocity models, (c) and (d) hydraulic diffusivity models, (e) and (f ) specific storage models. The hydraulic conductivity models in (g) and (h) are obtained by multiplying diffusivity and specific storage values for every grid cell. Black asterisks depict the positions of GPR and pressure–pulse transmitters, white dots indicate the positions of GPR receivers and white circles the positions of the pressure sensors for hydraulic tomography.
June 2013 The Leading Edge 709
Geophysics bright spotsGeophysics bright spots Coordinated by Ken MahrerGeophysics bright spots Coordinated by Yonghe Sun
zones from airborne geophysics and satellite data using random forests and support vector machines by Matthew J. Cracknell and Anya M. Reading. AE’s remark: This is the first application in the geosciences of the novel approach of random forests for classification. The application is nov-el and accessible. I think it would have broad appeal and stimulate others to try something similar. I am aware of random forests and their growing reputation in the field of computational science.
4) Structure-coupled joint inversion of geophysical and hydrolog-ical data by Tobias Lochbühler, Joseph Doetsch, Brauchler Ralf, and Niklas Linde. AE André Revil/Mauricio Sacchi.
5) Migration velocity analysis using residual diffraction moveout in the post-stack depth domain by Tiago Coimbra, Jose Jad-som Figueiredo, Joerg Schleicher, Amelia Novais, and Jesse Costa. AE John Etgen’s remark: There are relatively few attempts to update velocities based on residual diffraction signatures in migrated images. This one is unique in its “ray-tracing-like” approach.
6) Dynamics and navigation of autonomous underwater vehi-cles for submarine gravity surveying by James Kinsey, Mau-rice Tivey, and Dana Yoerger. AE Xiong Li’s remark: Gra-vimetry from an autonomous underwater vehicle (AUV) is one of the near-future technologies for exploration in extra deep water and under the ice. This paper represents the first on this topic for Geophysics. It doesn’t discuss
gravimetry itself directly. Instead, it deals with the naviga-tion of an AUV, an extremely important piece of AUV gravimetry.
7) Nonlinear scattering-based imaging in elastic media: theory, theorems and imaging conditions by Matteo Ravasi and An-drew Curtis. AE Deyan Draganov.
8) Convolutional time-lapse seismic modeling for CO2 sequestra-tion at the Dickman Oifield, Ness County, Kansas by Jintan Li, Christopher Liner, Jianjun Zeng, and Po Geng. AE G. Randy Keller’s remark: The Dickman field in the in United States midcontinent provides two possible CO2 sequestra-tion targets: a regional deep saline reservoir is the primary objective and a shallower mature, depleted oil reservoir is a secondary objective. The goal of this work is to character-ize and simulate monitoring of the carbon dioxide (CO2) movement before, during, and after its injection into these sequestration targets. The goal of this effort is to provide an evaluation for the effectiveness of 4D seismic monitor-ing in providing assurance of long-term CO2 containment.
9) The Backus-Gilbert method and their minimum-norm solution by Jose M. Pujol. AE Sven Treitel’s remark: This paper con-tains significant new material about the background and the impact of Backus and Gilbert’s work. Moreover, there is a discussion of the role of the BG method in modern computing terms, and of the differences in its popularity among different scientific communities.
Seismic Uncertaintiesand their Impact
Registration and housing are now open!www.seg.org/meetings/Banff2013
Contact us for more information:[email protected]
710 The Leading Edge June 2013
Multicomponent seismic interpretation: Call for papers Th e editors of Interpretation invite papers on the topic “Multicomponent seismic interpretation” for publication in a August 2014 special section.
Multicomponent seismic data allow geologic sequences to be defi ned with both P- and S-waves. Th ese two wave modes provide diff erent options for defi ning stratigraphy and facies within stratigraphic intervals because P refl ectivity is controlled by diff erent elastic properties than is S refl ectivity. How should interpreters take advantage of the diff erences in P-wave and S-wave refl ection behavior to expand their under-standing of rock and fl uid properties and to optimize geologic interpretations?
Th e purpose of this special issue is to encourage papers that off er guidance and insight for interpretation challenges such as:
• S-waves seem to react more strongly to subtle faults than do P-waves. Is this statement correct? Why? Can compari-sons of P and S faults be presented?
• Is there a robust way to depth register P and S data so that P and S attributes are positively extracted from depth-equivalent data windows?
• Are there advantages to combining P and S data into joint AVO analyses?
The Leading Edge
Announcements
• Interpreters need examples that show P-waves reveal a tar-get that S-waves do not see, and conversely, S-waves reveal a target that P-waves do not see. Why does this happen? Are subsurface calibration data available to explain the dif-ferences in refl ectivity behavior?
• Any multicomponent seismic interpretation case history will help others understand proper procedures for per-forming joint interpretations of P and S data. Case his-tories will be essential for proper application of S-wave technology.
Special Section Editors
E-mail Addresses
Michael DeAngelo [email protected] Hardage [email protected] Murray [email protected] Roche [email protected] Sava [email protected] Simmons [email protected] Sullivan [email protected] Wagner [email protected] Zhou [email protected]
SEG Continuing Education Course Schedule
REGISTRATION IS OPEN! SEG Distinguished Instructor Short Course
by David H. Johnston
Houston, TX USA
After 4 September, only on-site registration available
For more information, e-mail [email protected] or [email protected]
Continuing Education Courses
Houston, TX USA
To view the full course descriptions and register online, visit
June 2013 The Leading Edge 711
Special Section Editors
E-mail Addresses
Adam Baig [email protected] Blangy [email protected] Cabarcas [email protected] Chen [email protected] Diller [email protected] Eisner [email protected] Rich [email protected] Shemeta [email protected]
Interested authors should submit their manuscripts for review no later than 1 October 2013. In addition, the spe-cial section/supplement editors would like to receive a pro-visional title and list of authors as soon as possible. Authors should submit via the normal online submission system for Interpretation (https://mc.manuscriptcentral.com/interpreta-tion) and select this topic in the manuscript-type dropdown option. Th e submitted papers will be subject to the regular peer-review process, and the contributing authors are also ex-pected to participate in the review process as reviewers.
We will work according to the following timeline:
Submission deadline: 1 October 2013Peer review complete: 1 March 2014All fi les submitted for production: 15 March 2014Publication of issue: August 2014
Microseismic monitoring: Call for papersTh e tremendous growth in unconventional plays over the last decade has led to a signifi cant interest in microseismic moni-toring. Th e seismic response of the reservoir is recorded with either downhole or surface geophones. Algorithms ranging from traditional ray tracing to migration are used to locate the source of the signal. Signifi cant research is ongoing relat-ed to improving the locations and advancing source charac-terization. It is understood that velocity models account for the most signifi cant error in event locations and that signifi -cant potential may exist in source characterization, but the interpretational community still lacks a basic understanding of how to interpret and benefi t from data gathered through passive monitoring.
Th e editors of Interpretation invite papers on the topic “Microseismic monitoring” for publication in the August 2014 special section. Contributions are invited in all areas of microseismic investigation including case studies, integrated interpretation, location algorithms, and source characteriza-tion. Of particular interest are the following:
• Case studies highlighting integration with engineering and geologic data
• Case studies highlighting pitfalls in microseismic interpre-tation
• Case studies illustrating the importance of understanding location accuracy
• Case studies highlighting the value of source characteriza-tion
Interested authors should submit their manuscripts for review no later than 1 October 2013. In addition, special section editors would like to receive a provisional title and list of authors as soon as possible. Authors should submit via the normal online submission system for Interpretation(https://mc.manuscriptcentral.com/interpretation) and select the Microseismic monitoring special section option in the manuscript-type dropdown box. Th e submitted papers will be subject to the regular peer-review process, and the contrib-uting authors also are expected to participate in the review process as reviewers.
Submission deadline: 1 October 2013Peer review complete: 1 March 2014All fi les submitted for production: 15 March 2014Publication of issue: August 2014
2013 Pacific South Honorary Lecturer
Aeromagnetics — A Driver for Discovery & Development of Earth ResourcesDave Isles, Consultant, Perth, Australia
For more information or to view previous HL presentations, visit: www.seg.org/hl
Aeromagnetic surveys are very commonly under interpreted. The potential value, captured during acquisition, is all too often unrealised at the interpretation and “action” stages of a project. This presentation illustrates the fundamentals of robust aeromagnetic interpretation using telling case studies.
DATE .............. LOCATION 11 June .................North Ryde, Australia 13 June ................. Brisbane, Australia
DATE .............. LOCATION17 June .................. Auckland, NZ19 June ..................Wellington, NZ
DATE .............. LOCATION20 June ..................Dunedin, NZ21 June ..................Nelson, NZ
Sponsored by Shell
712 The Leading Edge June 2013
InterpretationA jo in t pub l i ca t ion o f SEG and AAPG
A journal of subsurface characterization
INTERPRETATION is a peer-reviewed journal launched by SEG and AAPG
to advance the practice of subsurface interpretation. The journal
will be published quarterly beginning August 2013. Papers will be
published online as they are accepted, edited, and composed.
Article submissions now are being accepted.
For more information, visit seg.org/interpretation
Upcoming Special Sections:Seismic attributes• Submission deadline: 15 June 2013• Publication of issue: February 2014• Special section editors: Saleh al-Dossary, Arthur Barnes, Eric Braccini, Satinder Chopra, Dick
Dalley, Kurt Marfurt, Marcilio Matos, Ralf Oppermann, and Kui Zhang
Pore-pressure prediction and detection• Submission deadline: 30 June 2013• Publication of issue: February 2014• Special section editors: Dan Ebrom, Philip Heppard,
Martin Albertin, and Richard Swarbrick
Interpreting AVO• Submission deadline: 31 July 2013• Publication of issue: May 2014• Special section editors: William Abriel, John Castagna, Douglas Foster, Fred Hilterman,
Ron Masters, George Smith, and Chuan Yin
Well ties to seismic data• Submission deadline: 30 August 2013• Publication of issue: May 2014• Special section editors: Don Herron, Rachel Newrick,
and Bob Wegner
Interpretation and integration of CSEM data• Submission deadline: 1 October 2013• Publication of issue: August 2014• Special section editors: Sandeep Kumar, Lucy MacGregor, and James Tomlinson
Multicomponent seismic interpretation• Submission deadline: 1 October 2013• Publication of issue: August 2014• Special section editors: Michael DeAngelo, Bob Hardage, Paul Murray, Steve Roche, Diana Sava,
James Simmons, Charlotte Sullivan, Donald Wagner, Ran Zhou
Microseismic monitoring• Submission deadline: 1 October 2013• Publication of issue: August 2014• Special section editors: Adam Baig, Jean-Pierre
Blangy, Carlos Cabarcas, Jingyi Chen
M•••
M••••
Submit to INTERPRETATION:INTERPRETATION seeks papers directly relevant to the practice of in-
terpretating the earth’s subsurface for exploration and extraction
of mineral resources and for environmental and engineering ap-
plications. Submit a paper at https://mc.manuscriptcentral.com/
interpretation.
Special Sections:Each issue of INTERPRETATION will include at least one special section
focused on a particular topic. Watch for the first issue of Interpretation
in August featuring a special section on interpreting stratigraphy from
geophysical data. November’s issue will include a special section on
interpretation for unconventional resources.
Submissions are being accepted for the sections listed.
To submit a paper
to one of these
sections, visit https://
mc.manuscriptcentral.
com/interpretation and
select the appropriate
topic from the manuscript
type options.
To suggest a topic for
future special sections,
e-mail interpretation@seg.
org or contact one of the
editors.
www.seg.org/am
Registration & Housing opens: 8 July 2013
S O C I E T Y O F E X P L O R A T I O N G E O P H Y S I C I S T S
Golf Tournament
GAC Lunches
Start assembling your teams! This year’s golf tournament will take place at the award winning Redstone Golf Club. This club was voted Best Golf Course of 2013 by National Golf Course Owners Association. The tournament will take place on Saturday, 21 September, before the annual meeting begins Sunday.
We are excited to bring you
three technical lunches and four GAC lunches in Houston. The Gravity & Magnetics, Near Surface, and Development & Production committees will all hold technical lunches. The Global Affairs Committee will host four luncheons with regional focus on Latin America, Africa/Middle East, Asia/Pacific and Former Soviet Union/Europe. All lunches will take place on Tuesday, 24 September and Wednesday, 25 September.
George R. Brown Convention Center
22–27 September 2013 • Houston, Texas USA
The International Conference for Geophysics
Photo courtesy of Redstone Golf Club
www.seg.org/am
Registration & Housing opens: 8 July 2013
S O C I E T Y O F E X P L O R A T I O N G E O P H Y S I C I S T S
Golf Tournament
GAC Lunches
Start assembling your teams! This year’s golf tournament will take place at the award winning Redstone Golf Club. This club was voted Best Golf Course of 2013 by National Golf Course Owners Association. The tournament will take place on Saturday, 21 September, before the annual meeting begins Sunday.
We are excited to bring you
three technical lunches and four GAC lunches in Houston. The Gravity & Magnetics, Near Surface, and Development & Production committees will all hold technical lunches. The Global Affairs Committee will host four luncheons with regional focus on Latin America, Africa/Middle East, Asia/Pacific and Former Soviet Union/Europe. All lunches will take place on Tuesday, 24 September and Wednesday, 25 September.
George R. Brown Convention Center
22–27 September 2013 • Houston, Texas USA
The International Conference for Geophysics
Photo courtesy of Redstone Golf Club
714 The Leading Edge June 2013
The Leading Edge
Calendar
2013
Summer Research Workshop: Unconventional Resources: Th e Role of Geophysics, Pittsburgh, USA, www.seg.org/meetings/un-conv13, ([email protected])
2–6 Jun
SEG DISC: Making a Diff erence with 4D: Practical Applications of Time-Lapse Seismic Data, Edin-burgh, Scotland, www.seg.org/disc
5 Jun
SEG DISC: Making a Diff erence with 4D: Practical Applications of Time-Lapse Seismic Data, Stavan-ger, Norway, www.seg.org/disc
17 Jun
SEG DISC: Making a Diff erence with 4D: Practical Applications of Time-Lapse Seismic Data, Bucha-rest, Romania, www.seg.org/disc
20 Jun
SEG DISC: Making a Diff erence with 4D: Practical Applications of Time-Lapse Seismic Data, Milan, Italy, www.seg.org/disc
24 Jun
SEG DISC: Making a Diff erence with 4D: Practical Applications of Time-Lapse Seismic Data, Massy, France, www.seg.org/disc
26 Jun
SEG DISC: Making a Diff erence with 4D: Practical Applications of Time-Lapse Seismic Data, Rijswijk, Th e Netherlands, www.seg.org/disc
28 Jun
SEG DISC: Making a Diff erence with 4D: Practical Applications of Time-Lapse Seismic Data, Aber-deen, Scotland, www.seg.org/disc
1 Jul
SEG DISC: Making a Diff erence with 4D: Practical Applications of Time-Lapse Seismic Data, Lon-don, United Kingdom, www.seg.org/disc
3 Jul
D&P Forum—Integrated Geophys-ics for Unconventional Resources, Krakow, Poland ([email protected])
7–11 Jul
Summer Research Workshop: Seismic Uncertainties and Th eir Impact, Banff , Canada, www.seg.org/meetings/Banff 2013, ([email protected]) 8–12 Jul
Near Surface Geophysics Asia Pa-cifi c Conference, Beijing, China, www.seg.org/meetings/nsgapc13, ([email protected])
17–19 Jul
IQ Earth Forum: Visualizing and Predicting the Integrated Earth, Boston, USA ([email protected])
4–8 Aug
SEG DISC: Making a Diff erence with 4D: Practical Applications of Time-Lapse Seismic Data, Perth, Australia, www.seg.org/disc
9 Aug
SEG DISC: Making a Diff erence with 4D: Practical Applications of Time-Lapse Seismic Data, Mel-bourne, Australia, www.seg.org/disc
11 Aug
ASEG-PESA 2013 23rd Interna-tional Geophysical Conference, Melbourne, Victoria, Australia, ([email protected])
11–14 Aug
Unconventional Resources Technol-ogy Conference, Denver, USA, www.URTeC.org 12–14 Aug
Summer NAPE, Houston, USA, http://www.napeexpo.com
14–16 Aug
SEG DISC: Making a Diff erence with 4D: Practical Applications of Time-Lapse Seismic Data, Bris-bane, Australia, www.seg.org/disc
16 Aug
SEG/ExxonMobil Student Education Program, Rio de Janeiro, Brazil, http:// www.seg.org/education/univer-sity-student-programs/sep, ([email protected])
24–26 Aug
AAPG/SEG Fall Student Expo, Hous-ton, USA, http://www.studentexpo.info
16–17 Sep
International Conference & Exhibi-tion on Reservoir Surveillance, Xi’an, Shaanxi Providence, China, ([email protected])
16–18 Sep
SEG DISC: Making a Diff erence with 4D: Practical Applications of Time-Lapse Seismic Data, Caracas, Venezuela, www.seg.org/disc
17 Sep
SEG DISC: Making a Diff erence with 4D: Practical Applications of Time-Lapse Seismic Data, Hous-ton, USA, www.seg.org/disc
20 Sep
SEG/ExxonMobil Student Education Program, Houston, USA, http:// www.seg.org/education/universitystu-dent-programs/sep, ([email protected])
20–22 Sep
SEG Annual Meeting Continuing Education Courses, Houston, USA, (www.seg.org/ce)
21–22 Sep
SEG/Chevron Student Leadership Symposium, Houston, USA, http://www.seg.org/education/university-student-programs/sls, ([email protected])
21–22 Sep
SEG International Exposition and 83rd Annual Meeting, Houston, USA, www.seg.org/am 22–27 Sep
SEG DISC: Making a Diff erence with 4D: Practical Applications of Time-Lapse Seismic Data, Rio de Janeiro, Brazil, www.seg.org/disc
3 Oct
The CPS/SEG Beijing 2014 International Geophysical Conference and
Exposition will be held in Beijing, China, 21–24 April 2014. CPS/SEG
Beijing 2014 organizers invite you to submit abstracts to be considered
for oral or E-poster presentation at this upcoming meeting. Submissions
must conform to SEG formats described in the abstract kit, be written
in acceptable English, and contain high-quality graphics.
Call for Papers
Primary categories for the CPS/SEG Beijing 2014 Technical Program are as follows:
Abstract kits will be available on the SEG Web site in May.
Hardcopies can be requested from the SEG Business Office.
DEADLINE FOR ABSTRACT SUBMISSION:
18 October 2013, 5 P.M. U.S. CENTRAL
STANDARD TIME.
Coorganized by:
Chinese Petroleum Society
Society of Exploration Geophysicists
Do not miss this opportunity. Make plans to submit your abstract
beginning on 1 August 2013.
CPS/SEG Beijing 2014 Geophysical Conference
and ExpositionP.O. Box 702740
Tulsa, OK [email protected]
www.seg.org/meetings/Beijing2014
For more information please contact:Kristi Smith, CMP
Programs and Events [email protected]+1-918-497-5564
www.seg.org/meetings/Beijing2014
716 The Leading Edge June 2013
Applications for Active membership have been received from the candidates listed below. This publication does not constitute election but places the names before the member-ship at large in accordance with SEG’s Bylaws, Article III, Section 5. If any member has information bearing on the qualifications of these candidates, it should be sent to the president within 30 days. The list can be viewed online at membership.seg.org/applicants/.
For Active membershipAbdel Motagally, Ahmad (CGGVeritas, Nasr City,
Egypt, Arab Republic)Ajewole, Thomas Olanrewaju (Petronas Carigali SDN.
BHD., Kuala Lumpur, Malaysia)Bongiovanni, Nicolas (Denbury Resources, Plano, USA)Denny, Stuart (Dolphin Geophysical, Houston, USA)Fritz, Richard (SM Energy, Tulsa, USA)Geck Alvarez, Amanda (Rock Solid Images, Houston,
USA)Gilstrap, Tatiana (Halliburton, Houston, USA)Golob, Bruce (ION-GXT, Lakewood, USA)Gonzalez, Zarko (OGX Petróleo e Gás Ltd., Bogota,
Colombia)Juergens, Chris (Petrominerales Ltd., Calgary, Canada)Lane, Andrew (Nuvista Energy Ltd., Calgary, Canada)McConnell, Douglas (DMT Geosciences Ltd., Calgary,
Canada)Minehardt, Todd (Enthought, Inc., Austin, USA)Reyes, Felix (Paradigm Geophysical, Houston, USA)Stephenson, Paul (Petrominerales Ltd., Calgary, Canada)Tiwari, Dhananjay (TGS, Houston, USA)Udofia, Udeme (Schlumberger Technology Corporation,
Houston, USA)Uzoh, Echezona (Statoil, Cypress, USA)
For reinstate to Active membershipAl-Khelaiwi, Adil (BGP-Arabia , Alkhobar, Saudi Arabia)Brock, Joseph (Agile Seismic, Sugar Land, USA)El-Desoky, Hany (Shell, New Orleans, USA)Graber, Stuart (Marathon Oil Company, Cypress, USA)Kayes, Douglas (KG Consulting LLC, The Woodlands,
USA)Wang, Kexie (Jilin University, Changchun, China)
For transfer to Active membershipBishop, Richard (consultant, Houston, USA)Bogaards, Mark (Noble Energy Inc., Houston, USA)Buonora, Marco Polo Pereira (Petrobras, Rio De Janeiro,
Brazil)Chmela, William (Sekal, Houston, USA)Ford, Sean (Lawrence Livermore National Lab, Livermore,
USA)
The Leading Edge
Membership
Requirements for Membership
Active: Eight years professional experience, partly in-volving exercise of independent judgment.
Membership applications and details on other types of membership, including Associate, Student, and Cor-porate, may be obtained at http://membership.seg.org.
Freeman, Chuyler (Bureau of Ocean Energy Management (BOEM), New Orleans, USA)
Hinton, Douglas (Marathon Oil Company, Houston, USA)
Jonke, Katarina (CGGVeritas, Houston, USA)Nourollah, Hadi (3D-Geo Pty. Ltd., Melbourne,
Australia)Reveron, Jorge (PDVSA Intevep, Los Teques, Miranda,
Venezuela)Roxis, Nikolaos (retired, Korinthos, Greece)Sauve, Jeffrey (consultant, Anchorage, USA)Sparkman, Deane (Devon Energy, Norman, USA)Terlikoski, Louis (Anadarko Petroleum Corporation, The
Woodlands, USA)Torry, Bradley (Arcis Seismic Solutions, Calgary, Canada)Travassos, Jandyr (Wavefront Consulting, Rio de Janeiro,
Brazil)
For reinstate and transfer to Active Membership Edivri, Edafe (Shell Petroleum Development Company,
Part Harcourt, Nigeria)Lopez, Crucelis (ExxonMobil, Houston, USA)Sharma, Ritesh (Arcis Seismic Solutions, Calgary, Canada)
Dr. James IrvingSr. Lecturer at University of Lausanne
Member Since 1998
I am consistently impressed
with the steps that the SEG has taken,
and continues to take, in its evolution
towards being the worldwide ‘home’
of applied geophysics, as opposed
to a society narrowly focused on
petroleum and mining industry
applications. As recent President of
the Near Surface Geophysics Section
(NSGS), and member of SEG for over
10 years, I have seen support for our
section grow tremendously, along with
a marked increase in the amount of
SEG research focused on near-surface
hydrological, environmental, and
engineering problems. This can only
be expected to continue to grow as
issues involving clean water and the
effects of climate change become
increasingly important.
““
SEG is the world’s leading geosciences society with more than 33,000 members in 138 countries across the globe. The Society of Exploration Geophysicists provides its members with the resources and tools they need for a successful professional career, and serves the geosciences community with timely events, helpful information, and networking. To take advantage of the benefits SEG offers and learn more about its numerous programs, join now at www.seg.org/membership/overview.
Together, we transform our passion into advancing geophysics today and inspiring geoscientists for tomorrow.
i am
{ {To take advantage
of the benefits
SEG offers and
learn more about
its numerous
programs
join now at:
www.seg.org/membership/overview.
IamSEG_fillerAd.indd 1 11/27/12 11:27 AM
Dr. James IrvingSr. Lecturer at University of Lausanne
Member Since 1998
I am consistently impressed
with the steps that the SEG has taken,
and continues to take, in its evolution
towards being the worldwide ‘home’
of applied geophysics, as opposed
to a society narrowly focused on
petroleum and mining industry
applications. As recent President of
the Near Surface Geophysics Section
(NSGS), and member of SEG for over
10 years, I have seen support for our
section grow tremendously, along with
a marked increase in the amount of
SEG research focused on near-surface
hydrological, environmental, and
engineering problems. This can only
be expected to continue to grow as
issues involving clean water and the
effects of climate change become
increasingly important.
““
SEG is the world’s leading geosciences society with more than 33,000 members in 138 countries across the globe. The Society of Exploration Geophysicists provides its members with the resources and tools they need for a successful professional career, and serves the geosciences community with timely events, helpful information, and networking. To take advantage of the benefits SEG offers and learn more about its numerous programs, join now at www.seg.org/membership/overview.
Together, we transform our passion into advancing geophysics today and inspiring geoscientists for tomorrow.
i am
{ {
718 The Leading Edge June 2013
The Leading Edge
Personals
Maurice “Mo” Arnold, died on 26 April 2013.Lawrence Morley, died on 22 April 2013.Jack Weyand, died on 25 April 2013.
A memorial fund has been established in the name of these deceased members in honor of their contributions, dedication to the science of geophysics, and support of SEG. Contributions to specifi c funds will be acknowledged as tax-deductible donations to the SEG Foundation, and family members will be notifi ed of your gifts.
To register and for more information, the full itinerary, or previous DISC presentations, visit: www.seg.org/disc2013 DISC travel costs underwritten by ExxonMobil
Time-lapse (4D) seismic technology is a key enabler for improved hydrocarbon recovery and more cost-effective field development. Acquisition, processing, and interpretation methods currently employed by the industry will be demonstrated alongside the diversity of geological settings and productions scenarios where 4D is making a difference.
DATE LOCATION SECTION5-Jun ....... Edinburgh, Scotland .............Heriot-Watt University17-Jun .... Stavanger, Norway .............Statoil20-Jun ..... Bucharest, Romania .............Romanian Society of Geophysics24-Jun ..... Milan, Italy ............................Italian EAGE-SEG Section26-Jun ..... Massy, France .......................CGG University28-Jun ..... Rijswijk, The Netherlands .....Shell1-Jul ........ Aberdeen, Scotland ..............BP3-Jul ........ London, United Kingdom......CGG9-Aug ....... Perth, Australia .....................Australian SEG - Western Australia11-Aug ......Melbourne, Australia ................Australian SEG - Victoria
DATE LOCATION SECTION 16-Aug ......Brisbane, Australia ...................Australian SEG - Queensland17-Sep.......Caracas, Venezuela ..................Soc Venezolana de Ingenieras Geof (SOVG)20-Sep.......Houston, TX, USA ....................SEG Annual Meeting3-Oct.........Rio de Janeiro, Brazil.................Sociedade Brasileira de Geofisica (SBGf)7-Oct.........Buenos Aires, Argentina ........... Asoc Argentina de Geólogos y Geofísicos
Petroleros (AAGGP)9-Oct .........Bogota, Colombia .................... Asoc Colombiana de Geólogos y Geofísicos
del Petroleo (ACGGP)21-22 Oct ..Beijing, China............................CNOOC Research Center24-25 Oct ..Nanjing, China ..........................Sinopec
2013 Distinguished Instructor Short Course
Making a Difference with 4D: Practical Applications of Time-Lapse Seismic DataDavid H. Johnston, ExxonMobil
Edge and Tip Diffractions: Theory and Applications in Seismic ProspectingKamill Klem-Musatov, Arkady Aizenberg, Jan Pajchel, and Hans B. Helle
In Edge and Tip Diffractions: Theory and Applications in Seismic Prospecting (SEG Geophysical Monograph Series No. 14), the theoretical framework of the edge and tip wave theory of diffractions has been elaborated from fundamental wave mechanics. Seismic diffractions are inevitable parts of the recorded wavefield scattered from complex structural settings and thus carry back to the surface information that can be exploited to enhance the resolution of details in the underground. The edge and tip wave theory of diffractions provides a physically sound and mathematically consistent method of computing diffraction phenomena in realistic geologic models. In this book, theoretical derivations are followed by their numerical implementation and application to real exploration problems. The book was written initially as lecture notes for an internal course in diffraction modeling at Norsk Hydro Research Center, Bergen, Norway, and later was used for a graduate course at Novosibirsk State University in Russia. The material is drawn from several previous publications and from unpublished technical reports. Edge and Tip Diffractions will be of interest to geoscientists, engineers, and students at graduate and Ph.D. levels.
ISBN 978-1-56080-149-8, eISBN 978-1-56080-162-7Published 2008, 201 pages, PaperCatalog #154A, SEG Members $79, List $99, e-book $99
Order publications online at: www.seg.org/bookmart E-mail: [email protected]
I n d u s t r y E x h i b i t i o n P o s t e r S e s s i o n F i e l d Tr i p sRésumé Bui ld ing and Review Network ing In terv iews
I c e B r e a k e r I n t e r v i e w i n g T i p s S h o r t C o u r s e s
www.studentexpo.info
Coming Fall 2013…
For more information, visit www.seg.org/hl
FALL DISTINGUISHED LECTURECarl RegoneContractor, Houston, TX, USA
Acquisition modeling: expect the unexpected
NORTH AMERICA HONORARY LECTURENick MoldoveanuWesternGeco, Houston, TX, USA
Evolution of marine acquisition technology after wide azimuth
MIDDLE EAST & AFRICA HONORARY LECTUREGeorge SmithUniv of Cape Town, Cape Town, South Africa
AVO in exploration and development
CENTRAL & SOUTH AMERICA HONORARY LECTUREMarco Polo P. BuonoraPETROBRAS E&P and University Federal Fluminense, Rio de Janeiro, Brazil
The use of mCSEM (marine controlled-source electromag-netics) for deep-water hydro-carbon exploration in Brazil
supported by
For more information, visit www.seg.org/dl
NEAR SURFACE HONORARY LECTUREValentina SoccoPolitecnico di Torino, Turin, Italy
Surface wave analysis for near-surface characterization: Intro-duction, theme and variations
For more information, visit www.seg.org/hl
IQ Earth Forum: Visualizing & Predicting the Integrated Earth
4–8 August 2013 Boston, Massachusetts, USA
Visit the IQ Earth webpage for more information about the Forum and how to participate.
www.seg.org/iqforum
“The IQ Earth challenge is vital to geophysics. IQ
Earth assembles subsurface data users, providers,
and academics to work toward the essential goal of
creating an integrated and quantified interpretation
science.”Bob Hardage2012 SEG President
IQ
ers,
oal ofof
atatatiioionn
Sponsored by:
2014 ARCTIC TECHNOLOGY
CONFERENCE
www. ArcticTechnologyConference.org
Paper proposals due 13 June
Luc T. Ikelle and Lasse AmundsenIntroduction to Petroleum Seismology (SEG Investigations in Geophysics Series No. 12) provides the basic thoretical and practical background needed to tackle present and future challenges of petroleum seismology, especially those related to seismic data acquisition and imaging and to reservoir characterization and monitoring. The first part of the book evolves from first principles of physics to the fundamentals of elastodynamic wave propagation, the building blocks for seismic analysis. The second part discusses modern developments in petroleum seismology such as multicomponent data, multiple elimination, amplitude variation with offset and azimuth analysis and inversion, anisotropy, and linear anelasticity. Aspects of Fourier and wavelet representations of seismic signals and the fundamentals of higher-order statistics for analyzing seismic signals also are treated. The comprehensiveness of this book makes it a suitable text for undergraduate and graduate courses that target geophysicists and engineers as well as a guide and reference work for researchers and professionals in academia and in the petroleum industry. The book is illustrated with color figures and provides a wide range of examples and problems.
Catalog #115A Published 2005, 679 Pages, Hardcover ISBN 978-1-56080-129-0 SEG Members $129, List $159E-book eISBN 978-1-56080-170-2 SEG Members $110, List $135 Order publications online at: www.seg.org/bookmartor E-mail: [email protected]
Much has changed since SEG published a comprehensive book on multicomponent seismic technology in 1991. The current volume, Multicomponent Seismic Technology (SEG Geophysical References Series No. 18), brings the subject up to the present. Emphasis is placed on practical applications of multicomponent seismic technology, with chapters dedicated to data-acquisition procedures, data-processing strategies, techniques for depth-registering P and S data, rock-physics principles, joint interpretations of P and S data, and numerous case histories that demonstrate the value of multicomponent data for evaluating onshore and offshore prospects. All forms of multicomponent seismic data are considered –— three component, four component, and nine component. Interpretation focuses on elastic wavefield seismic stratigraphy, in which a seismic interpreter gives the same weight to S-wave data as to P-wave data when defining seismic sequences and seismic facies. S-wave splitting in fractured media and other key theo-retical concepts are supported by numerous data examples. The book will be of interest to researchers
in multicomponent seismic technology and to explorationists who have to locate and exploit energy resources. The book will be appreciated by those who shun mathematical theory because it explains principles and concepts with real data rather than with mathematical equations.
ISBN 978-56080-282-2 Published 2011, 336 pages, Hardcover
eISBN 978-1-56080-289-1 SEG Members $79, List $99, E-book $99
Catalog #178A
Bob A. Hardage, Michael V. DeAngelo, Paul E. Murray, and Diana Sava
Order publications online at: www.seg.org/bookmart or E-mail: [email protected]
Multicomponent Seismic Technology
722 The Leading Edge June 2013
Company Page Phone Fax E-mail / Web site ContactAAPG Convention Department 695 918-560-2651 918-560-2684 [email protected] / www.aapg.org Crystal GarvinArcis Seismic Solutions Cvr 2, 619 403-781-5866 403-804-5965 [email protected] / www.arcis.com Darla HendersonBG Group 689 [email protected] / www.bg-group.com Mark LawtonCGGVeritas Cvr 4, 653 832-351-8364 832-351-8701 www.cggveritas.com / [email protected] Stephanie PhillipsDawson Geophysical Cvr 3 800-D-DAWSON 432-684-3030 [email protected] / www.dawson3d.com Steve JumperDownUnder GeoSolutions 595 61 8 9287 4100 61 8 6380 2471 [email protected] / www.dugeo.com Matthew G. Lamont Ph.D.Edge Technologies, Inc. 603 403-770-0440 403-770-0443 [email protected] / www.edge-tech.ca Garry KelmanGEDCO 655 403-303-8692 403-262-8632 [email protected] / www.gedco.com Randy KolesargeoLOGIC 623 Geometrics 594, 631 408-954-0522 [email protected] / www.geometrics.com Ikon Science, Ltd. 683 44 (0)20 8941 8975 44(0) 20 8941 8975 [email protected] / www.ikonscience.com Emma Southwell-SanderINOVA Geophysical 673 281-568-2111 [email protected] / www.inovageo.com Teresa RobertsION 627 281-879-3593 281-879-3626 [email protected] / www.iongeo.com Karen AbercrombieMewbourne College of Earth & Energy 597 405-325-3821/4701 405-325-3180 [email protected] Naila WilliamsMicroSeismicInc 647 713-725-4806 [email protected] / www.microseismicinc.com Peter DuncanNAPE (American Assoc. of Professional Landmen) 691 817-847-7700 817-847-7704 [email protected] / www.napeonline.com Christy PayneNEOS GeoSolutions 663 281-892-2651 281-892-2092 [email protected] / www.NEOSgeo.com Chris Friedemann, CMOParadigm Geophysical 609 713-393-4800 713-393-4801 [email protected] / www.paradigmgeo.com PGS Geophysical 593 44 (0) 1932 266404 44 (0) 1932 266512 [email protected] / www.pgs.com John WalshPolarcus DMCC 679 971 4 43 60 966 971 4 43 60 808 [email protected] / www.polarcus.com Rebecca Ericson-GranthamSander Geophysics 611 613-521-9626 613-521-0215 [email protected] / www.sgl.com Malcolm ArgyleSercel/Vibtech 667 33 2 40 30 1181 33 2 40 30 5894 [email protected] / www.sercel.com Alain TisserandTransform Software and Services, Inc. 641 720-283-1929 720-274-1196 [email protected] / www.transformsw.com Murray RothWeatherford International, Ltd. 615 281-646-7184 281-646-7222 [email protected] / www.weatherford.com WesternGeco 601 44 1293 55 6655 44 1293 55 6627 www.westerngeco.com Wireless Seismic, Inc. 633 832-532-5080 281-277-7804 [email protected] / www.wirelessseismic.com Patricia JonesiZ-Terra 599 281-945-0000 [email protected] / www.Z-Terra.com Alexander Mihai Popovici
The Leading Edge
Advertising Index
ADlinc is offered free to display advertisers in the current issue of The Leading edge. Submission of contact information is the responsibility of the advertiser.
Heavy Oils: Reservoir Characterization and Production MonitoringEdited by Satinder Chopra, Laurence R. Lines, Douglas R. Schmitt, and Michael L. Batzle
Heavy Oils: Reservoir Characterization and Production Monitoring presents an integrated and general description of the development and production of heavy-oil fields throughout the world, with particular emphasis on geophysical characterization of heavy-oil fields. The book (SEG Geophysical Developments Series No. 13) introduces the important economic impact of heavy oil as a major world energy resource, with reserves being roughly equivalent to the world’s conventional oil reserves. The origin of heavy-oil sands, its phase behavior, and unique physical properties are described in the context of the world’s major heavy-oil fields. Particular attention is paid to the unique rock physics of heavy-oil sands, which offers challenges to the conventional theories that describe fluid-saturated sandstones. Given the high viscosity and density of this oil, there are distinct challenges to production. This book describes a wide range of enhanced oil recovery methods (EOR) including steam injection, solvent injection, cold production, and combustion. In all these EOR methods, it is imperative to accurately describe the reservoir before and after production. As pointed out by the book, this reservoir characterization requires integration of engineering, geology, and geophysics, with rock physics supplying a key link. The book emphasizes geophysical methods, especially time-lapse 3D seismic methods, while providing numerous case histories from the 2007 SEG Development and Production Workshop at the University of Alberta. The heavy-oil geology and production from major heavy-oil reservoirs is compared and contrasted. Given the economic importance and need for detailed information about heavy-oil production, this book should prove interesting to all reservoir engineers, geologists, and geophysicists in this field.
ISBN 978-1-56080-222-8, eISBN 978-1-56080-223-5Published 2010, 338 pages, Hardcover, Catalog #134A SEG Members $99, List $124, E-book $124Order publications online at:
www.seg.org/bookmartor E-mail: [email protected]
SEG ANNUAL MEETING 2013
www.seg.org/ns SOCIETY OF EXPLORATION GEOPHYSICISTS nsPO BOX 702740 TULSA, OK 74170 -2740
near surfaceTuesday, 24 September
Near Surface Luncheon 11:30 AM – 1:30 PM
Near Surface Evening Social Sambuca Restaurant in Rice Hotel 7:00 PM – 11:00 PM
Friday, 27 September
SEG-AGU NSFG-EEGS Post Convention Workshop: Near Surface Geophysics in the Dynamic Coastal Environment — Crossing the Land/Sea Interface
8:00 AM – 5:00 PM Workshop sponsored by
Near Surface Geophysics
724 The Leading Edge June 2013
On this particular day, the conversation at the lunchroom table had wandered among several different topics before settling on stories about
famous gaffes and blunders in technical presentations. This topic, the lore of which has expanded geometrically since the advent of PowerPoint (originally called Presenter) as THE application for composing and delivering presentations, added palpable energy to the routine mid-day break in an otherwise average day at the office.
Sam knew that the storytelling on this day would be in-spired when someone described PowerPoint as the “kudzu of corporate communication.” Having just returned from an early summer vacation in the Appalachian Mountains of eastern Tennessee and seen firsthand the choking, para-sitic growth of kudzu on the native vegetation, this metaphor seemed oddly appropriate to Sam in view of the omnipres-ence of PowerPoint—he could not remember the last time he had attended or given a presentation without it.
But the enjoyment on this day would come from the memories of presentations gone bad long before PowerPoint had been nothing more than a gleam in a programmer’s eye. These stories usually involved either 35-mm slide projectors or transparencies on overhead projectors, both of which are now essentially obsolete. The 35-mm projector for many years was the device of choice for delivering presentations at professional society meetings, with the following problems being most common:
Slides were improperly loaded into the carousel, which is the tray that snaps into place on the top of the projec-tor and rotates one slide at a time (ideally) as the presenter moves from one slide to the next. The common problem here was that an improperly loaded slide, that is, one which was backward or upside down, had to be removed and reloaded correctly before the presentation could proceed. This task was accomplished by the projectionist, who could not always tell what the proper orientation of the slide should be, and so the reloading process frequently involved iterative rotation of the offending slide until the speaker indicated that it was properly in place. Of course this had the unfortunate effect of disrupt-ing the flow of the speaker’s presentation. In the worst case, a balky carousel would not easily yield to the projectionist’s ef-forts to remove and reload a slide, and an entire presentation was at risk of collapse.
Many times the projector was out of focus, at least to the taste of the audience, and the speaker was distracted by calls of “Focus! Focus!” until acceptable image clarity was achieved. After Sam had witnessed this circumstance on a number of occasions, he could not help but wonder if one of the qualifi-cations for a job as a projectionist was poor eyesight.
The Leading Edge
Interpreter SamEveryday misadventures of the everyman of interpretation
Every now and then, by way of a practical joke, a carousel which had been left unattended would be “adjusted” by sur-reptitiously reordering or substituting unexpected slides into a presentation. This was intended to confound the speaker but with humorous effect—such a prank would never have been possible, and certainly would not have been tolerated, at a professional society meeting.
After having recounted a few anecdotes involving de-bacles with slide projectors, the conversation moved on to transparencies and overhead projectors. There were many good stories about these, but one that Sam’s good friend Jack told was the best of all and had not that much to do with an overhead projector.
In graduate school we had these geology seminars, usu-ally on Wednesday evenings, where a guest lecturer pre-sented the results of his latest research or published pa-per. In this meeting, the speaker had been chief scientist on a research cruise in the Middle East, during which a number of sparker profiles were collected over the sea-floor spreading center along the axis of the Red Sea. He had pointed out on one of his images the remarkable symmetry of the opposing flanks of the axial ridge—the two halves of the profile were virtually identical. He was about to move on to his next transparency when one of the grad students in the back of the room asked a question.
“Can you please go back to your base map for the sparker pro-files and point out the location of the line you’re showing us?”
Now looking more closely at the base map, the details of which the speaker had not described, we all saw that the ship’s track for the line in question began just off the coast and ran out to the center of the ridge, at which point it reversed direction and returned to the coast on a path parallel to and only several hundred meters away from its outward pass.
“It looks like the symmetry we’re seeing is just the result of the vessel having traveled over the same geology in opposite directions on its outward and inward passes. Is that right?”
An agonizing few seconds of silence followed, and then came the speaker’s reply.
“Yes, I believe that’s correct.”
He gave no further explanation and offered no apol-ogy for the mistake, but it didn’t matter, because from that point on no one was listening anymore.
Corresponding author: [email protected]
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