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Analysis of a Sub-Bottom Sonar Profiler for
Surveying Underwater Archaeological Sites
by
Amy Vandiver
Submitted to the Department of Electrical Engineering and ComputerScience
in partial fulfillment of the requirements for the degree of
Master of Engineering in Electrical Engineering and Computer Science
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
May 2002
© Amy Vandiver, MMII. All rights reserved.
The author hereby grants to MIT permission to reproduce anddistribute publicly paper and electronic copies of this thesis document
in whole or in part.
A uthor .................Department of Electrical Engineering and Compyter Science
Oay 24, 2002
Certified by.......... %.. . . , .. . . . . . . . . . . . . . .
David A. MindellProfessor,
Thesis Supervisor
Accepted by.........Arthur C. Smith
Chairman, Department Committee on Graduate Students
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Analysis of a Sub-Bottom Sonar Profiler for Surveying
Underwater Archaeological Sites
by
Amy Vandiver
Submitted to the Department of Electrical Engineering and Computer Scienceon May 24, 2002, in partial fulfillment of the
requirements for the degree ofMaster of Engineering in Electrical Engineering and Computer Science
Abstract
Imaging buried objects with bottom penetrating sonar systems is a research problemof interest to archaeologists as well as the defense community and geologists. Thedeep sea archaeology setting brings a unique set of design constraints to this field,namely high resolution imaging and limited depth of penetration. A prototype high-frequency sub-bottom profiler was designed and built by David Mindell and MarineSonic Technologies,Inc. The characteristics and limitations of this prototype areanalyzed in this thesis with the intent of improving our ability to interpret the datathat it collects. By characterizing the transducer and the signal processing electronicsit was possible to collect quantitative field data with the sensor and compare it with amodel of the system. In addition, several sources of error are identified and suggestionsfor improving the system are made.
Thesis Supervisor: David A. MindellTitle: Professor, Science Technology and Society
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Acknowledgments
I would like to acknowledge my thesis advisor David Mindell and the DeepArch
research group at MIT including Brian Bingham, Brendan Foley, Aaron Broody,
Katie Croff, Johanna Mathieu, and the students in STS.476. I would also like to
thank my academic advisors Gill Pratt and Frans Kaashoek for their guidance during
the time I have spent at MIT.
My Mother, Father and Step-Mother have been a great source of inspiration and
motivation for me over the years and I would not be where I am today without them.
Finally, I would like to thank Terry Smith for his endless patience with me this spring
and Ben Vandiver for keeping me on track and providing moral support.
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Contents
1 Introduction
1.1 Acoustic Profiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Deep Water Archaeology . . . . . . . . . . . . . . . . . . . . . . . . .
1.3 Precision Navigation . . . . . . . . . . . . . . . . . . . . . . . . . . .
2 Related Work
2.1 Sub-Bottom Profilers ........
2.1.1 Chirp Signals . . . . . . . .
2.1.2 Buried Object Detection . .
2.1.3 Scattering, Attenuation and
2.2 Medical Ultrasound . . . . . . . . .
3 Description of the Existing System
3.1 Overview . . . . . . . . . . . . . . .
3.2 Ashkelon Shipwreck Data.....
3.3 Monitor Turret Survey . . . . . . .
4 Electronics and Signal Processing in the
4.1 O verview . . . . . . . . . . . . . . . . . .
4.2 Pulse Shape and Bandwidth . . . . . . .
4.3 Power Consumption . . . . . . . . . . .
4.4 Time Varying Gain . . . . . . . . . . . .
4.5 Enveloping and Sampling . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
Acoustic Modeling
. . . . . . . . . . .
Current System
. . . . . . . . . .
. . . . . . . . . .
. . . . . . . . . .
. . . . . . . . . .
. . . . . . . . . .
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29
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46
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. . . . . . . . . . .
. . . . . . . . . . .
4.6 Improvements to the Existing System . . . . . . . . . . . . . . . . . . 48
4.6.1 Analog to Digital Conversion . . . . . . . . . . . . . . . . . . 49
4.6.2 Digital Signal Processing . . . . . . . . . . . . . . . . . . . . . 50
5 Analysis of the Transducer 55
5.1 R esolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.1.1 Wavelength . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.1.2 Beam Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.2 Depth of Penetration . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.2.1 Reflection Coefficients . . . . . . . . . . . . . . . . . . . . . . 61
5.2.2 Attenuation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.2.3 Estimate of the Depth of Penetration . . . . . . . . . . . . . . 64
5.3 Model of the Sub-Bottom Profiler . . . . . . . . . . . . . . . . . . . . 64
6 Experimental Results 71
6.1 Experiment Description . . . . . . . . . . . . . . . . . . . . . . . . . 71
6.2 R esults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
6.3 A nalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
6.4 Further Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
7 Design Recommendations for Future Systems 87
8 Conclusion 91
A Schematics 95
A.1 Amplification and enveloping . . . . . . . . . . . . . . . . . . . . . . 95
A.2 TVG suface mount board . . . . . . . . . . . . . . . . . . . . . . . . 96
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List of Figures
3-1 A vertical cross section taken by the prototype sub-bottom profiler of
the Tanit shipwreck (circa 750 BC) located in 400 meters of water off
the coast of Ashkelon, Israel . . . . . . . . . . . . . . . . . . . . . . . 32
3-2 Photomosaic of the Tanit shipwreck. . . . . . . . . . . . . . . . . . . 32
3-3 Expanded view of the cross-sectional imaged produced by the sub-
bottom profiler. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3-4 Sub-bottom profiler data collected during the survey of the Monitor. . 35
4-1 Block diagram of the electronics . . . . . . . . . . . . . . . . . . . . . 38
4-2 The uniform pulse shape and bandwidth. . . . . . . . . . . . . . . . . 39
4-3 Shape and bandwidth of the Gaussian pulse. . . . . . . . . . . . . . . 40
4-4 Predicted and measured gain as a function of numerical gain parameter. 42
4-5 Time varying gain values as a function of time . . . . . . . . . . . . . 44
4-6 Measured and modeled time varying gain and corresponding average
error as a function of step size . . . . . . . . . . . . . . . . . . . . . . 45
4-7 Signal processing performed by the current electronics. . . . . . . . . 46
4-8 Signal processing of 2 pulses separated by 10 microseconds . . . . . . 47
4-9 Alternative enveloping options . . . . . . . . . . . . . . . . . . . . . . 48
5-1 The far field beam pattern . . . . . . . . . . . . . . . . . . . . . . . . 57
5-2 Data collected during a swimming pool test to estimate the beam width
2.3 meters away from the sensor. . . . . . . . . . . . . . . . . . . . . 59
5-3 Data collected during a swimming pool test to estimate the near field
sidelobes of the transducer . . . . . . . . . . . . . . . . . . . . . . . . 60
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5-4 Diagram of a boundary between two materials . . . . . . . . . . . . . 61
5-5 Estimated maximum depth of penetration for various sediment types. 65
5-6 A sample object field and the corresponding modeled data for a sensor
with a beam width of 1 cm. . . . . . . . . . . . . . . . . . . . . . . . 67
5-7 The top figure shows the coefficients of the moving average filter. The
bottom figure is the modeled profiler data including the moving average
approximation of the beam width. . . . . . . . . . . . . . . . . . . . . 68
5-8 Modeled data including the effects of a low-pass filter. The colors are
displayed on a log scale and time varying gain is not modeled. .... 69
6-1 Pictures of the trench (top) and gantry structure over the trench after
the objects were buried (bottom). . . . . . . . . . . . . . . . . . . . . 73
6-2 Top view of objects buried at the test site . . . . . . . . . . . . . . . 74
6-3 Vertical cross-section of the test site . . . . . . . . . . . . . . . . . . . 74
6-4 Predicted data displayed on a log scale. . . . . . . . . . . . . . . . . . 75
6-5 Two data sets collected at the test site with constant gain. . . . . . . 77
6-6 Swimming pool test to verify the hypothesis that the second double
bounce was caused by a reflection off of the surface of the water. . . . 78
6-7 Model data including the low pass filter and plotted on a linear scale 79
6-8 Two data sets collected at the test site with time varying gain. ..... 81
10
List of Tables
3.1 Specifications for a prototype sub-bottom profiler from Mindell and
Bingham , 2001 [22]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.1 Power Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5.1 Typical sediment properties from Orsi and Dunn [27]. . . . . . . . . . 62
5.2 Acoustic properties of typical objects embedded in a medium grained
sand-silt-clay mixture. . . . . . . . . . . . . . . . . . . . . . . . . . . 62
6.1 Types and positions of objects buried at the test site. . . . . . . . . . 72
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Chapter 1
Introduction
The DeepArch research group at MIT has been studying methods for investigating
shipwrecks in the deep ocean. In 1998, Prof. David Mindell in conjunction with
Marine Sonic Technology Inc. designed a low-cost, high-frequency, narrow-beam,
sub-bottom profiler. This prototype has been used in several surveys and has demon-
strated that it is possible to use a high frequency (150 kHz) profiler to detect some
structure in the first meter below the surface [22] [3].
The qualitative images that have been published are highly promising but they
clearly do not provide as much information as possible about the objects buried
beneath the surface. In order to accurately interpret the data it is necessary to
understand the quantitative characteristics and limitations of the system. Without a
quantitative analysis of the capabilities of the sensor, all of the data which is collected
will remain pretty pictures, and not a viable diagnostic tool for evaluating buried
objects.
In this thesis I will analyze the theoretical capabilities of the current sub-bottom
profiler using laboratory measurements and numerical models. By characterizing the
properties of the system the data collected can be used to quantitative as well as
qualitative data. A model of the system created and is compared to data collected in
a controlled field experiment. In addition, careful evaluation of the current prototype
revealed several possible sources of error. Suggestions for improvements to the system
are presented. Lastly, design criteria for the next generation sub-bottom profiler are
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developed, and possible designs are discussed.
1.1 Acoustic Profiling
Acoustic profiling sensors span a very wide range of remote sensing applications from
seismic surveys for natural resources, to sonar studies of sediment layering in the sea-
floor, to medical ultrasound imaging. These different applications operate on very
different scales, but the underlying physics is the same. Bottom penetrating sonar
systems have been in use in under water archaeological surveys for decades, starting
with that of Harold Edgerton in 1967[8].
A sub-bottom profiler functions by sending acoustic energy straight down per-
pendicular to the bottom. The sound wave reflects off the bottom as well as any
inhomogenaties below the surface. This reflected wave is measured by a receiver lo-
cated in the same place as the source. By measuring the travel time of the reflected
wave it is possible to estimate the depth of each feature. However, in order to accu-
rately convert from time to depth it is necessary to estimate the speed of sound for
every material in the path of the sound wave.
The strength of the reflected signal from an object is dependent on the material
properties of the object as well as it's orientation. Specifically, the amplitude of
the reflected wave is proportional to the contrast in acoustic impedance between the
object and the surrounding media. Thus, a very dense metal object buried in sediment
will have a much stronger return signal than a clay vessel which has a similar density
and speed of sound as the surrounding sediment.
Sediment absorbs and scatters high frequency compression waves. The higher the
frequency, the more rapidly sediment (and most other materials) attenuates acoustic
energy. For this reason sub-bottom profiling systems generally use fairly low frequen-
cies (2 and 20 kHz) to image sediment layering and other geological features up to 100
meters in depth. This frequency regime severely limits the resolution because objects
smaller than one wavelength are impossible to resolve. At best modern chirp systems
can resolve 10 cm thick sediment layers. In addition, the wide beam widths of most
14
sub-bottom profilers cause the signal from a large area to be combined, resulting in
poor horizontal resolution.
For archaeological surveys of small sites it would be highly beneficial to be able to
survey the top meter of sediment with a great degree of precision. Since most archae-
ological sites are concentrated in the top several meters of the sea floor, resolution
is more important than penetration. By increasing the frequency and narrowing the
beam width it is possible to improve the resolution both horizontally and vertically.
Acoustic profiling images are often very difficult to interpret because we are used to
thinking about images created by light, not sound. With photographs it is normally a
straight forward process to measure objects, identify materials, and to discern shapes.
Acoustic images are not as easy to interpret because the speed of sound and rate of
attenuation is not constant in all the materials in the image. Additionally in acoustic
profiling the wavelength is normally close to the same size as the objects of interest
so complex diffraction and scattering effects can occur. Finally, object shapes are
difficult to detect because surfaces which are not perpendicular to the beam generally
do not reflect very much energy towards the profiler.
Sub-bottom sonar images are even more difficult to interpret than sidescan sonar
images because in sidescan sonar the only media which the sound waves travel through
is water, which for small areas has fairly consistent properties. Additionally, it is
possible to use higher frequencies to improve resolution because the rate of attenuation
is water is far less than it is in sediment, and with sidescan sonar it is possible to
move closer to the object which is exposed on the sea floor. Lastly, acoustic shadows
are generally not present because of volume scattering and transmission through the
target.
Due to the complexity of interpreting sub-bottom sonar images it is important
to fully understand the characteristics of the sensor. An unknown and uncalibrated
sensor can create qualitative images. However, in order to collect quantitative data
about the number, size and type of objects buried beneath the sea floor, a thorough
understanding of the capabilities and limitations of the sensor is necessary. Forward
and inverse modeling of the data is required to obtain the maximum amount of
15
information from the data.
1.2 Deep Water Archaeology
Surveying archaeological sites imposes an entirely different set of requirements for
bottom penetrating sonar systems than most of the other areas of current research.
Small archaeological sites such as buried wooden shipwrecks are one of the few cases
when it would be highly beneficial to precisely image a small region with a limited
depth of penetration. Sub-bottom sonar is commonly used for geological mapping
and in industry for surveying pipelines, both of which are on a larger scale than ar-
chaeological sites. Bottom penetrating sonar is also used by the defense community
for mine detection. Although mines are of a similar scale to archaeological artifacts,
mine detection is inherently a problem of detection and not imaging and the exis-
tence of large survey areas and explosive targets severely constrains the investigation
process. Archaeological sites are one of the few places where it is worth the effort and
expense to precisely image a small area.
The DeepArch research group at MIT is currently interested in discovering and
investigating deep water Bronze and Iron Age shipwrecks in the Mediterranean. We
generally define deep water to be depths beyond what a scuba diver can reach such
that no direct human contact with the site is possible. Even if a human were to travel
in a submersible to the wreck site, it would not be possible to interact with the sur-
roundings without the aid of mechanical arms or other indirect methods. Because the
deep water environment forces the use of advanced technological means to investigate
a shipwreck, it is an excellent place for technological advancement. By comparison
at an archaeological site where the cost of excavation is low, the cost of developing
complex remote sensing techniques is prohibitive.
The advancement in ROVs (Remotely Operated Vehicles) and AUVs (Autonomous
Underwater Vehicles) in the last 15 years is astounding. In 1989 the ROV Jason came
online and was used to investigate mid ocean ridges, hydrothermal vents and ship-
wrecks. Since then rapid advancements in navigation, control, sensors and computer
16
systems have made the idea of precision operations in deep water feasible. AUVs
are increasingly becoming valuable tools in industry. A Louisiana based company, C
and C Technologies is currently using a Hugin AUV to survey oil pipeline routes for
hazards and archaeological sites in the Gulf of Mexico. [42]
At present it is not possible to fully excavate a shipwreck without the aid of divers,
but Bob Ballard's Institute for Exploration and the Woods Hole Oceanographic In-
stitute are in the process of developing an ROV designed to excavate an 8th Century
Phonecian shipwreck in 800 meters of water off the coast of Ashkelon, Israel. Sarah
Webster of WHOI presented her preliminary design at the 2002 DeepArch Conference
at MIT [43].
Before a shipwreck can be surveyed it must be located. Side-scan sonar can be used
from a towfish or AUV to identify possible wrecks by their surface expression. [23].
Sidescan sonar operates at a high frequency (150kHz to 1.2MHz) and generates an
image of the sea floor. Since the high frequencies used do not penetrate very far below
the surface, for a wreck to be discovered it must be partially exposed. A commercial
chirp sub-bottom profiler can be used to help distinguish between geological features
and shipwrecks.
After a shipwreck has been identified, a photographic survey or photomosaic and
microbathemetric mapping can be performed to determine nature of the wreck [34].
Although full-scale archaeology has never been done in water deeper than divers can
reach, submersibles and ROVs have been used to lift or sample specific objects. [2]
In this context a high-frequency, narrow-beam, sub-bottom profiler can be used
to image the substructure of the shipwreck located below the sea floor [22]. However,
for the data from a pencil beam sonar to be useful, precise positioning information
must be available. Without knowing exactly where the data was taken it is useless.
If the sub-bottom sonar is moved back and forth over the site in parallel track lines, a
series of vertical cross sections of data can be collected. If these vertical cross sections
are close enough together they should be able to be combined into a 3 dimensional
image of the site.
In order for an AUV or ROV to be used to collect sub-bottom sonar data it must
17
be passively stable in pitch and roll, capable of hovering and maneuvering at slow
speeds, and be able to do precision navigation. Several vehicles have been developed
which fit these criteria including Jason, ABE, and SeaBed.
1.3 Precision Navigation
Precision control and navigation is essential to be able to collect sub-bottom profiler
data using a narrow beam sonar. Presently this type of precision is feasible using a
LBL system such as EXACT or SHARPS [44] or its subsequent commercial equiva-
lents [35]. An ROV or AUV can be put under closed loop control to navigate track
lines approximately 10-30 cm apart over the entire wreck, thus "mowing the lawn"
to collect vertical cross-sectional data.
There are two necessary elements involved in positioning the sub-bottom sonar
over the ship wreck. The first part is precision navigation or knowing the current
location of the vehicle (and the position of sonar sensor relative to the navigation
transponder). The second part is precision control or the ability to maneuver a
vehicle to the desired positions. Using current technology it is possible to maneuver
a passively stable vehicle with decimeter level accuracy and to know its position to
within centimeter level accuracy [44].
Since electro-magnetic waves do not propagate very far under water it is impossible
to use GPS to position vehicles under water. Consequently, acoustic transponder
systems have been developed. There are 2 common methods in use today, Ultra
Short Baseline (USBL) and Long Baseline (LBL). Doppler Velocimeter Logs (DVLs)
and gyros can be used for inertial navigation and in conjunction with transponder
based systems to increase accuracy [44].
USBL systems involve positioning a vehicle relative to a surface ship without the
aid of other transponders in the water. The position of the vehicle can be determined
by the bearing and range to the surface craft. At this time navigation to within 1 to
5 meters can be achieved. This type of navigation is terrific for use with an AUV or
towfish to do long range sidescan sonar surveying. However USBL systems are not
18
accurate enough to do fine scale investigation of a shipwreck.
To get the precision of under 1 meter necessary for making photomosaics, micro-
bathemetic mapping using a multi-beam sonar, or to do sub-bottom surveys, a LBL
system is necessary. LBL systems require placing multiple transponders around the
survey area. The location of the transponders can be determined by triangulation.
By measuring the travel time to each of the transponders, the location of the vehicle
can be determined much in the same way as GPS. Traditional LBL systems have used
low frequencies which propagate for a long ways to allow large survey areas. However
the low frequencies prevent precise localization. The EXACT system developed by
Dana Yorger and David Mindell at WHOI was the first high frequency wireless sys-
tem which achieved centimeter level precision [44]. Recently several companies such
as Sonardyne have been working on comercializing this system [35].
This precision navigation framework makes it possible to consider doing a precision
survey using a pencil beam sub-bottom profiler by maneuvering the vehicle in closely
spaced track lines. It also makes it possible to consider reproducibly maneuvering a
vehicle over the same area multiple times. Another even more extraordinary idea is
bi-static or multi-static sonar using multiple AUVs is currently under investigation
for use in mine detection in the GOATs project [10]. Without precision navigation, a
pencil beam sub-bottom sonar would be impractical. Either large arrays or scanning
sonar would be a necessity to get the same resolution.
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Chapter 2
Related Work
The high-frequency bottom penetrating sonar described in this thesis is a hybrid of
current low frequency chirp bottom profilers and medical ultrasound. Chirp profilers
generally operate in the 2-20 kHz range, have a resolution of about 10 cm and a depth
of penetration of about 100m [15]. Medical ultrasound images are usually performed
at frequencies typically ranging from 1-4 MHz in attempt to resolve sub-millimeter
sized features located less than 10 cm inside a human body [36]. The high frequency
sonar described in this thesis has an operating frequency of 150 kHz, a depth of
penetration of a few meters and a desired resolution of several centimeters.
Because the problem of high resolution sub-bottom profiling is bounded above and
below by chirp profilers and ultrasound, it is worth examining these two technologies
in great detail. By evaluating the similarities and differences between our problem
and the ones encountered by the other technologies, it is possible to appropriately
apply the knowledge and techniques learned in those fields. There are several areas
that are particularly important to examine including: transducer design and beam
forming, signal processing, scattering and attenuation, and methods to achieve large
dynamic range.
21
2.1 Sub-Bottom Profilers
Commercial sub-bottom profilers generally operate in the far field of the transducer.
They generally have a wide beam width of 20 to 40 degrees. The beam widths
of chirp sub-bottom profilers are wide because narrow beam widths would require
prohibitively large arrays of transducers. The traditional application for sub-bottom
profilers is mapping sediment layering or large objects such as pipelines.
The majority of recent research in sub-bottom profiling systems has been in geolog-
ical studies such as sediment classification, sea floor scattering and low grazing-angle
effects, or in the detection of buried objects. Many research projects have combined
two or more of these goals, because they are not independent problems.
2.1.1 Chirp Signals
The current state of the art commercial sub-bottom profilers produce a chirp signal
or frequency modulated (FM) signal [15] [9] [13]. This broadband signal is generally
a relatively long waveform (several milliseconds) with a linearly swept frequency,
although there are other options. By using any reproducible signal with a strong
autocorrelation, it is possible to matched filter the signal, thereby increasing the signal
to noise ratio and precisely determining the arrival time [1]. Using matched filtering
of chirp signals, it is possible to increase the length of the signal without sacrificing
resolution. Increasing the length of a sonar signal increases the total energy, and thus
greater penetration depths are possible.
Because chirp bottom profilers generally operate in relatively low frequency ranges
(2-20 kHz), digital signal processing is straight forward. High resolution (24 bit)
analog to digital converters with 50 kHz sampling rates are available and fairly in-
expensive. Although the resulting data rate is relatively high, matched filtering the
digitized waveform is well within the limits of modern digital signal processors (DSPs)
[7].
The functionality of matched filter processing is dependent on a controlled and
reproducible waveform with a strong auto-correlation. For this reason, the best chirp
22
systems apply the inverse transfer function of the transducer to the electrical chirp
signal before it is applied to the transducer [32]. In other words, the transducer
cannot be assumed to be transparent so the electrical signal necessary to produce
the desired acoustic chirp signal must be applied to the transducer. Similarly the
properties of the receiver must be taken into account. Without accounting for the
actual signal, matched filtering will fail to correctly determine the magnitude, phase
and travel time of the received signal.
2.1.2 Buried Object Detection
Recent research indicates that conventional single-channel reflection profilers are not
suitable for locating and imaging buried objects because the noise from surface and
volume scattering often exceeds the amplitude of the buried targets. [33]. Conse-
quently, there have been many studies to investigate other transducer designs and
beam forming methods.
One of the most promising studies was Frazier et.al. [12], who designed a 6 kHz
pulse system for detecting cultural artifacts. Their system used delay and sum beam
forming from a 33 cm circular array in attempt to image targets in the top meter of
sediment. They detected and resolved objects under 5 cm in size, but shape detection
was limited.
Schock et.al. [33] designed a scanning sonar which uses a linear array with beam
steering and near field focusing to improve coverage and increase the signal to noise
ratio. Their system used a 2 millisecond chirp signal varying in frequency from 5 to
23 kHz.
Dolphin bio-sonar capabilities greatly exceed those of any man-made system at
tasks such as shape and material detection as well as buried object detection. Roit-
blat et.al. [31] used dolphin sonar to inspire their buried objection detection sonar.
Dolphin clicks have a narrow beam width (about 10 degrees) and are a broadband
pulse approximately 50 psec in length and vary in frequency from 40 to 130 kHz. In
their manufactured system a neural network was used to identify 20 cm sized targets
buried 20 cm deep and insonified at oblique angles.
23
Several researchers have also attempted to use parametric sonar to identify buried
objects [4] [25]. A parametric sonar beam is generated by taking the difference of two
beams generated at different frequencies. Consequently narrow beams with virtually
no side lobes are possible, but the power output is severely limited.
During the SAX99 experiment, Piper [28] used a linear array, synthetic aperture
sonar with a high frequency (180 kHz) pulse. This system achieved mixed results at
detecting mines buried up to 50 cm of sediment.
The GOATS project is investigating the use of multiple AUVs to create a synthetic
aperture sonar for use in detecting mines [10]. The first phase of their research is to
characterize the typical return of mines in 3 dimensions.
The mine countermeasures community is highly interested in reliable methods
to detect buried and partially buried mines. Consequently there is a lot of defense
funding for buried object detection. Several different approaches to target recognition
have been taken including pattern-recognition [39], neural networks [14] [31] and pre-
whitening filters [40]. However, the problem of detecting mines is inherently a problem
of detection and not imaging, so not all of the research is entirely applicable to imaging
archaeological artifacts.
2.1.3 Scattering, Attenuation and Acoustic Modeling
In order to reliably detect buried objects from a distance, it is important to understand
sea floor scattering and low grazing angle effects. There is at present no good model
for scattering caused by the sea floor in the frequency range of 2 to 300 kHz. There
have been several large field experiments such as SAX99[38] and GOATS [10]to study
the these effects. However, the field experiments have mostly concentrated on the
lower frequencies of about 2 to 50 kHz. Sea floor scattering is still poorly understood;
but it has been shown that sub critical refraction leads to evanescent Biot-slow waves
[20]
The most significant source of acoustic noise in sub-bottom profilers operating in
normal incidence is surface scattering due to the roughness of the sea floor and volume
scattering due to inhomogenaties in the sediment [33]. Thus, unless good models of
24
scattering are developed, accurately interpreting data from sub-bottom profilers such
as ours will not be possible.
Different types of sediment attenuate sound energy at different rates. There are
two important factors governing the rate of attenuation of a specific sediment: poros-
ity and grain size. Course grained media with large poor sizes, such as sand, exhibit
a larger rate of attenuation than fine grained sediments such as clay and silt [17.
Based on this principle, there have been several studies to classify sediments types
based on their acoustic properties [37] [30] [27].
In addition to attempts to determine sediment type based on attenuation, there
has been a fair amount of research applying geophysical inversion methods to 2-
dimensional acoustic datasets for the purposes of tomographic imaging [6] [45]. Pro-
filing in normal incidence does not yield enough data to do these types of inversion.
Imaging archaeological artifacts requires understanding the acoustic properties
of the material being imaged. Bull, Dix and Quinn [29] have been studying the
acoustics of wood in order to be able to better interpret the results of sub-bottom
surveys of shipwrecks such as the Invincible and the Mary Rose. They have found
that the acoustic impedance of wood is highly anisotropic. The speed of sound and
thus the acoustic impedance is much higher in the longitudinal direction than in
either the radial or tangential direction. Their other important contribution is that
the impedance values they calculate are very similar to those of sediment, with the
longitudinal direction corresponding to sand and the radial direction corresponding
to a finer grained sediment such as silt or clay.
2.2 Medical Ultrasound
The other area of research that is closely related to high-resolution sub-bottom sonar
is medical ultrasound imaging. In addition to creating 2 and 3 dimensional images
with sub-millimeter scale resolution, ultrasound is being used to identify tissue types
based on their acoustic properties. Medical ultrasound systems encounter many of
the same problems that are found in sub-bottom profiling including penetrating in-
25
homogeneous and anisotropic materials with high rates of attenuation. Thus, many
of the techniques that have been applied to ultrasound including beam forming, sig-
nal processing and image display are highly applicable to high-resolution sub-bottom
imagery.
There are several important differences between medical uses of ultrasound and
sub-bottom profiling besides the increased frequency regime. The first difference is
that medical ultrasound systems almost always operate in the near field whereas sonar
systems are almost always used in the far field of the transducer. It is common for the
size of the ultrasound scan head to be approximately the same size as the desired image
or depth of penetration. Secondly, medical targets, such as the human heart, are often
moving, so incredibly rapid collection of an entire 3D data set is necessary in order to
avoid distortion. Fortunately, buried archaeological artifacts are stationary, so there
is comparatively no restriction on the amount of time it takes to collect a dataset
and the data collected from a given location is reproducible. Another difference of
medical ultrasound is that there is a restriction on the amount of energy which can
be used without harming a human subject. Lastly, early ultrasound systems were
strictly used in direct contact with the skin which prevents signal loss due to the first
surface reflection; however, many recent systems do not have this restriction.
The high frequencies used in medical ultrasound (typically 1-4 MHz, but experi-
mental models are as high as 7 MHz [19]) make matched filter digital signal processing
prohibitive. Consequently, medical ultrasound generally uses short pulse signals and
not chirp or coded signals. Although one Danish manufacturer, B-K Medical, is
breaking that trend [24]. The short pulse signals used by most medical ultrasound
are very similar to the signal used by our prototype sub-bottom profiler. These pulses
tend to be a few cycles long and roughly Gaussian in shape [36].
The acoustic properties of living tissue and those encountered in sub-bottom pro-
filing are roughly equivalent. Since the human body is mostly water, the speed of
sound in most soft tissue is about 1540 m/s which is roughly equivalent to sea water
and unconsolidated sediment. Attenuation is approximately 1 dB/cm per megahertz
of frequency [36]. For example, at 4 MHz the loss to an object 10 cm deep can be
26
as high as 80 dB. Analog to digital converters at megahertz sampling frequencies do
not have the accuracy (24 bits) necessary to handle the large dynamic range of these
signals. For this reason precise time varying gain (often referred to as automatic gain
control or AGC) systems are essential [36].
Since medical ultrasound sensors operate in the near field of the transducer, the
image is normally formed at the focal point. In order to form a vertical cross-section
image the focal distance of the receiver is commonly dynamically adjusted. In addi-
tion, in order to maintain constant lateral resolution, the receiving aperture is often
increased as well. In an array system this is accomplished by including additional
receiver elements in beam forming [36].
Recently medical ultrasound has been used to create 3 dimensional images. 3D
images can be created from linear, tilting and rotational scanning devices in addition
to 2D arrays. The linear scanning method normally mechanically moves a 1 dimen-
sional array over the region to be scanned. A ID array is made up of individual
elements (approximately the size of the wavelength) which steer the beam across the
plane. Complete 2D arrays are a recent area of very active research [18] [19].
The vertical cross sections generated by linear scanning are very similar to sub-
bottom profiling data collected by navigating a vehicle through parallel track lines.
Consequently much of the research in 3D visualization methods and volumetric esti-
mation might be applicable to sub-bottom profiling.
27
28
Chapter 3
Description of the Existing System
3.1 Overview
David Mindell in conjunction with Marine Sonic Technology Inc [16] designed a low-
cost high-frequency, narrow-beam, sub-bottom profiler for archaeological applications.
This prototype sub-bottom profiler uses a transducer with a very narrow beam width
to emit a short 150 kHz pulse waveform. The pulse is approximately 40 microseconds
long or 6 cycles of a 150 kHz sine wave.
The transducer was constructed by Marine Sonic Technologies by rearranging
the transducers used in a sidescan sonar into a circular array approximately 30 cm in
diameter. All of the array elements are driven in phase with one another. The receiver
employs the same transducers as the transmitter. The received signal is amplified by
a pre-amp and bandpass filtered and then transmitted to the data collection circuitry.
The electronics and computer control were designed and implemented by David
Mindell and his team at MIT. Because of the rapid attenuation of sound energy at
this high frequency, a time varying gain (sometimes called an automatic gain control)
stage was necessary prior to the digitization of the data. Additionally, due to the
limitations of the serial communications and the digital to analog converter, the
signal is low pass filtered to envelope the pulse. A more detailed explanation of the
electronics is presented in Chapter 4.
Specifications for the sub-bottom profiler were presented by David Mindell and
29
Brian Bingham at the 2001 IEEE Oceans Conference [22]. Their specifications are
summarized in Table 3.1.
Table 3.1: Specifications for a prototype sub-bottom profiler from Mindell and Bing-
ham, 2001 [22].
Array size 30 cm, circularBeam width 2-3degCenter frequency 150 kHzPulse length 40 psec (6 cycles)Bandwidth 34 kHzOutput Power 220 dB (re 1 pPa © 1 m)Receiver pre-amp noise 1 pVAmplifier gain 12-108 dBTime varying gain 12 bits ©400 psec/stepA/D converter resolution 12 bits
In this chapter sample field data that has been collected using the sub-bottom
profiler is presented. In the following 2 chapters through the use of laboratory exper-
iments and numerical models I report attempts to verify the specifications published
by Mindell and to add to our knowledge of the system.
3.2 Ashkelon Shipwreck Data
The prototype sub-bottom profiler was used in a deep water survey of two eighth
century B.C. Phonecian shipwrecks off the coast of Ashkelon Israel in 1999 [2]. This
expedition was a joint research project lead by Robert Ballard and involved many
scientists, engineers, and archaeologists from Woods Hole, MIT and many other insti-
tutions. The sub-bottom profiler was mounted on the ROV Jason which was placed
under closed loop control using EXACT LBL navigation beacons. Jason successfully
navigated several track lines back and forth across the Tanit shipwreck (shipwreck
A) approximately 3.5 meters above the bottom. In addition during the expedition
a microbathemetric map and photomosaic were created, as well many objects were
recovered.
30
The Tanit shipwreck had 385 nearly identical amphoras exposed on the surface
(see Figure 3-2). The only other types of artifacts that were visible were one stone
anchor and several cooking pots [2]. There are three obvious questions that cannot
be answered by the surface surveys and selected objects that were lifted. First, were
there other types of cargo such as metal ingots. Second, is any of the hull preserved?
And finally, what is the total size of the cargo? A precise sub-bottom profiler is in the
unique position of potentially being able to answer these questions without excavating
the wreck site.
The data that was collected (see Figure 3-1) is highly promising. It demonstrates
that the profiler can detect some archaeological artifacts below the sea floor. Because
the surface of the shipwreck has many exposed amphoras, the surface scattering
conditions are complex. Because a pile of amphora is not a good boundary to couple
the acoustic energy into the sea floor it is possible that much of the coherent energy
is lost due to diffraction and scattering at this interface.
A zoomed in version of one shows several features below the sea floor (Figure 3-3.)
The vaguely round orange objects in the surface layer are likely to be amphora. The
"ringing" effect noticed (left side) is likely to be reverberations of the sound wave
inside an amphora situated in the ideal orientation [22]. The amphora average 69
cm in height and 22 cm in width which corresponds very well with the size of the
observed objects. In addition there are several dark objects in the right hand side of
the blown up image. The nature of these objects is unknown but it is likely that they
are related to the shipwreck [22].
Although the images are highly promising, it is clear that there is room for im-
provement. The images do not have the desired resolution nor do they provide as
much information as possible about the objects buried beneath the surface. Without
a quantitative understanding of how the sensor should respond to different types of
buried objects, a positive identification of any target is impossible.
31
Figure 3-1: A vertical cross section taken by the prototype sub-bottom profiler ofthe Tanit shipwreck (circa 750 BC) located in 400 meters of water off the coast ofAshkelon, Israel
TANIT (Shipwreck A) Circa 750 B.C.Tat. PtoU Of the 166WI Secm. . Ow imwp:st 1IIP-uu f aIa ie 1 Iz, e;a fp1.%qc~e: As'afloIM"Ashcra ofthc Sce "
Figure 3-2: Photomosaic of the Tanit shipwreck. Courtesy of H. Singh, J. Howland,WHOI, IFE, and Ashkelon Excavations.
32
Figure 3-3: Expanded view of the cross-sectional imaged produced by the sub-bottomprofiler.
3.3 Monitor Turret Survey
The sensor was tuned up and a better time varying gain system was added before
the sub-bottom profiler was used in a field experiment in North Carolina. The sub-
bottom profiler was carried by divers during a survey of the turret of the Monitor
off Cape Hatteras in the Summer of 2001 [3]. Unfortunately the amount of metal in
and around the turret of the Monitor proved to be a very hostile environment. Echos
due to side-lobe reflections and the complexity of the sub-surface returns obscure
33
positive identification of possible buried objects (see Figure 3-4). The possible target
indicated in the figure was recorded about 1 to 1.3 milliseconds after the first surface
reflection. This corresponds to a depth of on the order of 1 meter.
34
Punctuation: divers raise andlower xducer to signal start/end
a) of survey line.
Sonar side lobereflection from armor belt
x Primary echo - firstreturn fromsediment
Second echobounce
b) Echo from turretwalls
Surface ofsediment in turret- concave shapeis excavationhole
Layers or objectsMort Within sediment
Hard target / buriedOhiect
Figure 3-4: Sub-bottom profiler data from the survey of the exposed portion of theMonitor turret is represented in the upper figure (a). The lower figure (b) is a closeup, with the x-axis representing survey time and the y-axis is acoustic travel timein number of samples (500 samples = 6.5 ms or about 5 meters.) Three distinctechos indicate stratified sediment, or buried structure, and a distinct "hard" target isevident. Figure and analysis courtesy of Brian Bingham, David Mindell and BrendanFoley [3].
35
36
Chapter 4
Electronics and Signal Processing
in the Current System
In order to collect quantitative data with the sub-bottom profiler it is necessary to
be able to correlate the numerical values recorded by the data logging system with
voltage values from the transducer. Additionally in order to improve the system, it is
necessary to carefully analyze the characteristics of the current system and look for
possible sources of error and/or opportunities for improvement. The signal processing
and data collection electronics are the logical place to begin this investigation because
they are the easiest to examine in the laboratory and also the easiest to modify.
4.1 Overview
The data collection system amplifies, filters, digitizes, and logs the signal from the
transducer. Consequently, the data collection system consists of 4 functional parts:
time varying gain, enveloping, digitization and serial communication, and computer
control. A block diagram of the system can be found in Figure 4-1. Schematics of
the current electronics are located in Appendix A.
The laptop computer, located on the surface, has a real time display of the data
as well as a convenient graphical interface for setting the number of samples, gain,
and signal to start and stop collecting data. The computer program is responsible for
37
si alto TF8 serial C ataping Microcontroller ' 38400 Collection
baud Computer
Figure 4-1: Block diagram of the electronics
logging the data and sending control signals by serial cable down to the TattleTale 8
(TT8) microcontroller located on the vehicle near the sea floor.
The TT8 (indirectly) signals the transducer to ping by lowering the power supply
voltage for 10 ,usec. The TT8 is also responsible for controlling the time varying gain
system and signaling analog to digital converter to sample.
Time varying gain is accomplished by a combination of the TT8, a digital to
analog converter and a variable gain amplifier. Indirectly by way of a digital to analog
converter, the TT8 provides a 0 to 12 volt control signal to the variable gain amplifier.
At constant intervals, the TT8 increases the gain according to predetermined values.
Due to limitations in bandwidth to transmit data back to the surface and the
limitations of the digital to analog converter, Mindell and his team decided to envelope
the signal. The enveloping circuit rectifies the received signal and low-pass filters it
to obtain the envelope of the pulse shape. The enveloped signal is passed to a 12-bit
analog to digital converter which samples at about 80 kHz. The TT8 then passes the
digitized data up to the computer on the surface for display and logging.
In an attempt to verify that the electronics have the desired behavior and to add
to our knowledge of the system, I have performed several laboratory measurements
as well as made numerical models of the systems.
38
4.2 Pulse Shape and Bandwidth
Since the transducer is sealed to operate at high pressures, I was unable to examine
the electronics which generate transducer waveform nor was I able to examine the
pre-amp used in the receiver.
Marty Wilcox of Marine Sonics claims that the electronics produce a uniform 6
cycle pulse. The sonar signal output from a transducers is commonly a modified
version of the electrical waveform. Marine Sonics claims that the transducers have
a Q value and thus modify the pulse shape into a one which is slightly wider than
a Gaussian pulse. Without measuring the properties of the transducer or the actual
transmitted acoustic wave it is not possible to determine the exact shape. However,
the resulting pulse shape is probably between the initial uniform or square-shape and
a Gaussian.
It is possible to calculate the frequency content of both the uniform pulse and the
Gaussian pulse by Fourier transform. Figure 4-2 shows the uniform pulse shape in
both the time and frequency domains. Similarly, Figure 4-3 is the Gaussian pulse in
both time and frequency.
0.8 - -..--. 0.9 - -
0.6 0.8 -
0.4- 0.7
0.2- .6 -
EO.5
E-0.2 - 0.4
-0.4- .0.3-
-0 .6 - - --- -- 0 .2 - - - - - - - -
-0.8 - 0.1-
-40 -20 0 m20 40 6O 80 60 80 100 120 140 160 180 200 220 240Time (microseconds) Frequency kHz
(a) (b)
Figure 4-2: The uniform pulse in the time domain (a) and the frequency domain (b).
It should be noted that the half power bandwidth of the Gaussian pulse, about
30 kHz, is considerably wider than the uniform pulse has a bandwidth of 23 kHz.
However, if the two signals have and equal maximum amplitude in the time domain,
39
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-40 -20 0 20 40 60Time (microseconds)
0.9
0.8
,0.7
0.6
-0.5
'0.4
0.3
0.2
0.1
80 '60 80 100 120 140 160Frequency kHz
180 200 220 240
(a)
Figure 4-3: Gaussian pulse in the time domain (a) and theThe magnitude in the frequency domain is normalized to the
the total power of the Gaussian
the closer the actual signal is to
resolution of the sensor, and the
of penetration.
(b)
frequency domain (b).uniform pulse.
pulse is less than that of the uniform pulse. Thus,
a Gaussian enveloped pulse, the greater the vertical
closer it is to a uniform pulse, the greater the depth
4.3 Power Consumption
The current prototype electronics are rather inefficient in terms of power consumption.
The electronics and an active transducer pinging once per second requires 11 Watts.
The electronics alone without the transducer connected require 8.4 Watts. Thus,
only 2.6 Watts or 24 percent of the total power could possibly be being used by the
transducer. The system was designed to run off of a 12 Volt DC power supply, but
the sonar transducer requires 48 Volts DC. Therefore it is likely that much of the
power is wasted by the DC to DC converter. Since this prototype system was not
designed for minimum power consumption this design choice is understandable.
40
- -. -. .......-. - -. -. -. .-. .- - .
-.... .- .. -.. - ......... -..- - - - - -- - - ---- -- --- -- -.-- -.-- - --- .
-. -.. -. . -. -.. -.. .. -.. .. ...
Table 4.1: Power Consumption
Voltage Current Power
Electronics, no transducer 12 V 0.7 A 8.4 Watts
Electronics + Transducer silent 12 V 0.75 9 Watts
Electronics + Transducer pinging 1/sec 12 V 0.92 A 11 Watts
4.4 Time Varying Gain
The time varying gain system was designed to compensate for the rapid attenuation of
pressure waves in sediment at high frequencies and the limited dynamic range of the
analog to digital converter. In order to properly calibrate the system it is necessary
to quantify the actual gain of the system as a function of time, that way the raw data
can be reconstructed during post-processing. Without knowing the actual system
gain as a function of time, the images that are created are limited to being pretty
pictures and will never have quantitative meaning.
The attenuation in water is much less than it is in sediment, so it is desirable
have the system gain increase at a constant rate after the first reflection from the
sea floor is received. An approximation to the behavior was achieved as follows. The
TT8 receives a list of predetermined gain parameters provided by the user of the
system. The user can also specify a "lockout" number in microseconds corresponding
to the approximate height the sensor is above the sea floor. By updating the gain
parameter at constant intervals (400 psec) the gain can be increased over time. The
gain parameter is a number ranging from 1 to 99.
The gain parameter is converted to an analog voltage value between 0 and 12
Volts. This conversion is linear in that if the numerical gain parameter in increased
by 1 the analog voltage level is increased by 0.12 Volts. This analog voltage is scaled
to 0 to 2.5 volts by a voltage divider and passed to the variable gain circuit designed
by Marty Wilcox of Marine Sonics. This variable gain circuit maps a linear change
in control voltage to a exponential change in amplification using an AD605.
The AD605 is a dual channel variable gain amplifier. In the high gain setting,
41
each amplifier can provide between 0 and 48 dB of gain depending upon the input
gain control voltage. For this application the two amplifiers were wired in series to
give a total range of 0 to 96 dB. The amplification in decibels varies linearly with the
input gain control voltage over the range from 8 to 88 dB. The amplifiers provide an
additional 8 dB of gain on either side of this range, however it is not linear. The linear
region corresponds to input gain control voltages of 0.5 to 2.5 Volts, or a numerical
gain parameter between 20 to 100.
The behavior of the variable gain amplifier was tested by experimental measure-
ments in the laboratory. The sonar receiver was replaced with a 150 kHz sine wave
signal. The numerical gain parameter was varied and the ratio of output voltage
to input voltage was calculated (see Figure 4-4.) Various input voltages were tested
varying from 150 mV to 0.6 V. The experimental measurements agree very well with
the expected behavior. All of the data points above a numerical gain parameter of 20
matched the predicted "ideal" values very well, whereas the those below 15 exhibit
the non-linear behavior of the amplifier. In the region from 20 to 100, an increase in
the numerical gain parameter of 1 corresponds to a 1 dB increase in gain.
30 Time Varying Gain Settings Time Varying Gain Settings100
25- 80
~82OCO20 - 60-
0 E
(n10 20-
5-0~
O -20 -0 5 10 15 20 25 30 35 40 0 20 40 60 80 100Numerical Gain Parameter Numerical Gain Parameter
(a) (b)
Figure 4-4: The solid line is the predicted gain as a function numerical gain parameter.The asterisks are the experimentally measured values. The left figure (a) is the gainexpressed as a ratio and the right figure (b) is the same data on a log scale expressedin terms of decibels (20log 1 0(Vut/Vjn)).
Clearly for the purposes of calculating the original values from the output of
42
the variable gain circuit, it would be better to be operating in the linear region
of the amplifier. Data collected during the survey of the Monitor turret indicates
that this might not always be practical. A typical time varying gain parameter
setting started at 10 and ended at 40 by increasing in steps of 2 about every 400
microseconds. However, it should be noted that the increase in amplification began
before the primary return from the sediment, so the gain settings corresponding to
sediment returns were mostly in the linear region.
Laboratory measurements indicate that the actual time varying gain as a function
of time is not quite as simple as an increase in gain parameter every 400 microseconds.
The time between increases in gain is actually about 390 microseconds and there is a
280 microsecond delay after the ping but before the time varying gain settings begin.
The system begins collecting data immediately. In addition to this built in delay in
the time varying gain, the user can specify a lockout period in microseconds which
corresponds to the height the sensor is position above the sea floor. The system delays
the start of the time varying gain and does not collect any data during this period.
Figure 4-5 (a) shows the effective numerical gain parameter as a function of time.
One other property of the variable gain circuit that is worth mentioning is that
gain control voltage is passed through a low pass filter prior to being used by the
variable gain amplifier. According to the schematic, this RC low-pass filter has a time
constant of about 80 microseconds, which agrees well with laboratory observations.
Consequently, as the gain is changed each "stair step" in amplification is effectively
rounded off (see Figure 4-5 (b). ) Thus, no sharp lines are noticeable in the resulting
image. While this filter improves the displayed image, it makes it difficult to calculate
the exact system gain as a function of time.
There are two different ways that the system gain can be modeled as a function
of time. The first and simplest method is to model the gain as a linear increase
with time. The second method is to model the entire system by passing the ideal
"stair-step" gain though a low-pass filter with a time constant of 80 microseconds.
The second method is more accurate, but it is also more complicated.
The time varying gain can be modeled as a linear increase in gain with time by
43
4000
3500
40 - -28
3000-
250030 - -18 C
0 2000
20 - 4 - - 8 1500 -
Z 1000.10 - - - -2
500
Time (milliseconds) Time (milliseconds)
(a) (b)
Figure 4-5: Tine varying gain as a function of time for a gain setting which starts at20 and increases in steps of 2. The left figure, (a), is the gain parameter as a functionof time. The right figure, (b), is the data collected by the system when the transduceris replaced with a 150 kHz sine-wave that is 160 mV peak-to-peak in amplitude. Thedashed line is the modeled data.
fitting a line to the data. The best fist line has a slope of the step-size times 2.56
dB/ms and is delayed by 555 microseconds (i.e. 275 microseconds in addition to
the 280 microsecond delay discussed earlier.) This method is particularly poor at
approximating the gain in initial half a millisecond after data collection begins. The
equation of the best fit line is:
Gain[dB] = Start - 12 + Step * (2.56[dB/ms]) * (Time - 0.555[ms]) (4.1)
The amount of error caused by approximating the system gain as a linear increase
is dependent on the step size. The larger the step size, the more error is introduced.
The average amount of error for any given step size can be expressed as the square
root of the mean squared error. A graph of the average error for various step sizes
can be found in Figure 4-6 (b). For a step size of 2, the average error is about 0.34 dB
and the maximum error is 0.6 dB. Thus, the linear approximation is fairly accurate
for small step sizes, but is not very good for large step sizes.
The linear region of amplification of the time varying gain system was determined
44
30 - 18
20 -20 8
0.5z10- -2
0 500 1000 1500 2000 2500 30 0 0 4 z6 7 8 9 10Tim (microseconds) Step Size
(a) (b)
Figure 4-6: Figure (a) is a sample gain ramping from 20 to 32 in steps of 2. The dottedline indicates the ideal stair-step gain, the solid line indicates the gain including theeffect of the low pass filter. The straight dashed line is the best-fit line to the low-pass filtered gain. The right figure is a plot of the square root of the mean squarederror of the best fit line compared to the low-pass filtered version versus step size.This represents the steady state error and ignores the initial effects of the first 500microseconds.
to be between 8 and 88 dB or a factor of 2.5 to 25,000. The variable gain amplifier
saturates when the output voltage exceeds ± 1.68 Volts. This limited range (3.3
Vp is probably due to the fact that power supplied to the circuit is limited to +5
and ground. Marine Sonics stated that the received signal is conditioned by pre-amp
with a gain of 50 before being sent up the cable to the electronics. The pre-amp was
reported to have 1 ptVolt of noise. Consequently, valid signal values which can be
input into the time varing gain system is between 0.4 Volts and 10 pVolt, or 92 dB
of dynamic range.
The maximum output voltage of the time varying gain circuit is very small and
the enveloping amplifier operates at a much higher voltage. Consequently, the signal
is amplified by an additional factor 8.33 prior to enveloping and sampling. This
transforms a ± 1.7V signal into a t14V signal.
45
4.5 Enveloping and Sampling
After the received signal has been amplified by the time varying gain circuitry, it is
rectified and low pass filtered to envelope the signal. The current low pass filter is a
simple RC circuit with a time constant of 40 pusec. The resultant signal is digitized.
Analog to digital conversion is performed by an AD774 chip. The A to D converter
turns an analog input signal between 0 and 10 Volts into a 12 bit number. Laboratory
measurements indicate that the sampling frequency is about 13psec. The dynamic
range of system is the precision of the A to D, i.e. 12 bits or 72 dB.
The current signal processing and sampling is presented in Figure 4-7.
10-
o 0
-10-
-20 0 20 40 60 80 100 120 140 160 1
10-
-20 0 20 40 60 80 10 120 140 160
1C> 5-
0 -A-20 0 20 40 60 80 100 120 140 160 1
2000
0~ 5 - f' 0 (~~0 p
-20 0 20 40 6 80 100 120 140 160 1Time (usec)
-80
I80
I
80
80
Figure 4-7: a) ideal gaussian received pulse after amplification. b) rectified pulse c)low pass filtered pulse d) sampled pulse
Clearly using a simple RC low-pass filter with a time constant equal to the the
length of the pulse results in a considerable loss of precision in the edges of the pulse.
This represents a loss in precision in vertical resolution since targets located close
together would result in overlapping signals. After being enveloped by this low-pass
filter is takes roughly 100 microseconds for filtered signal to drop below 10 percent of
its maximum value.
If 2 pulses were separated by 10 microseconds they could barely be resolved (see
46
Figure 4-8.) This corresponds to reflecting surfaces of objects located 50 Psec apart
or 7.5 cm apart at a speed of sound of 1500m/s. Unfortunately it is rare to observe
discrete responses from objects buried in sediment due to the high amount of scatter-
ing. In addition, the actual pulse is likely to be wider and thus the actual resolution
is poorer.
10-
-~ 0
-10-
-60 -40 -20 0 20 40 60 80 100
10-
-60 -40 -20 0 20 40 60 80 10010
5-
0-60 -40 -20 0 20 40 60 80 100
4000-
CO72000 -
-60 -40 -20 0 20 40 60 80 100Time (usec)
Figure 4-8: Two pulses separated by 10 microseconds: a) ideal received pulse afteramplification. b) rectified pulse c) low pass filtered pulse d) sampled pulse
There are several ways to improve the enveloping circuitry. Decreasing the cutoff
frequency of the simple RC low pass filter would slightly increase the ripple but
would better preserve the shape of the pulse. Using a better filter with a shaper
cutoff would dramatically improve this behavior. Even a simple 2nd order filter such
as a Butterworth, Elliptic, or Bessel decreases the signal smearing. 4-9
However, there is one problem with increasing the cutoff frequency of the low-pass
filter. The low-pass filter used for enveloping the signal is also used to prevent aliasing
in signal when it is sampled by the A to D. The sampling rate of the A to D is 77 kHz,
thus in order to prevent aliasing, the signal must not contain any frequencies above
38 kHz. Consequently, without increasing the sampling rate it will be very difficult to
increase the accuracy of estimating the arrival time of a pulse in the received signal.
47
-sgnal- original RC lowpass (cutoff 25 kHz)
9 ....- . ... RC lowpass (cutoff 75 khz)2nd order Butterworth fiter (cutoff = 250 khz)
8
0 2.... 4....... 1 ... 12.
Time (usec)
Figure 4-9: Alternative enveloping options
4.6 Improvements to the Existing System
Since the sonar transmitter, receiver and signal generation electronics are sealed inside
the transducer, the only part of the existing system that can be modified is the signal
processing and data collection electronics. Altering the transducer or waveform in any
way would require replacing the entire system. Improvements to the signal processing
electronics can only effect the vertical resolution and depth of penetration of the
system. The horizontal resolution is dependent on the transducer beam pattern and
will be analyzed in more detail in the next chapter.
One contributing factor to the poor vertical resolution of the current system is
that the signal is enveloped. The current process of enveloping the signal smears the
signal in time and removes any frequency and phase information. The improvements
to the low pass filter mentioned in the last section would reduce the smearing of the
signal at the risk of introducing sample aliasing problems. Consequently, the only
way to significantly increase the precision of arrival time estimation is to increase the
sampling rate.
The quality of analog to digital (A/D) converters is improving at a rapid rate. It is
currently possible to get a 16-bit A/D suitable for reconstructing waveforms capable
of sampling at up to 1.2 MHz (for example an AD7723 [7]). By using an A/D with
a high sampling rate it would be possible to digitize the entire waveform. Although
48
the amount of data generated by digitizing the entire waveform (2.4 MB/s) would
be prohibitive most of the time, having the ability collect the entire waveform would
be highly useful to characterize the system and to determine the best method for
compressing the data.
Digital signal processing chips to compress the resulting data are also becoming
cheaper, more powerful, and easier to program. The pulse signal employed by our
current transducer severely limits the types of signal processing that are possible.
Constant frequency pulse signals have a particularly poor auto-correlation, and thus
it is not possible to use matched filtering to isolate a specific return. However, there
are several different methods possible to compress the resulting signal including linear
quadrature filtering and a digital version enveloping technique that is currently being
used.
The last part of the system is control and communication with the data logging
computer. The TT8 currently performing this function is not fast enough to handle
such high data rates. However, most DSP chips have the ability to handle high
speed serial communication as well as enough memory to buffer the data if there is a
bandwidth limitation in communicating with the data logging system.
4.6.1 Analog to Digital Conversion
The increase in precision of analog to digital (A/D) converters with high sampling
rates makes it possible to digitize the entire received signal instead of just an enveloped
version of it. Digitizing the signal at a high sampling rate allows us to use digital signal
processing techniques to process the data as well as to collect the entire waveform
in order to characterize the sensor or possibly to use the frequency information to
classify the sediment attenuation and dispersion.
The dominant frequency of our system is 150 kHz. Thus, we must sample at
greater than the Nyquist frequency or 300 kHz. In order to accurately reconstruct
the waveform it would be better to sample at 600 kHz or above. The highest precision
achievable by A/Ds sampling at these high frequencies is 16 bits. Analog to digital
converters with 24 bits of resolution are available; however, the fastest 24-bit A/D that
49
I could find was a prototype not yet released by analog devices capable of sampling
at 96 kHz.
If it were possible to remove the time varying gain system it would be easier to
calibrate the system. Non-linearities in the variable gain circuit and the presence of
a low-pass filter on the gain control voltage make it very difficult to determine the
total system gain as a function of time. However, as was demonstrated in the section
on the time varying gain system, if the system is used properly it is possible to back
the original signal out of the recorded data.
Although a 16-bit A/D would probably not provide enough dynamic range to
eliminate the need for variable gain, there is a good chance that it could be adjusted to
the specific bottom conditions and used as a constant factor instead of a time varying
gain. Our current variable gain circuit has a theoretical range of amplification from
8 to 88 dB. However, when it was used on the monitor survey only a small fraction
(25dB) of this range was actually used. The current A/D has 12 bits of resolution or
72 dB of dynamic range. A 16-bit A/D used to digitize a bipolar waveform has about
90 dB of dynamic range, which is not quite equivalent to the effective range of 97 dB
used during the Monitor survey.
4.6.2 Digital Signal Processing
The digital signal processor is responsible for processing the signal as well as communi-
cating with the data logging computer and providing control signals to the transducer,
variable gain control, and analog to digital converter. Much like A/Ds, digital signal
processors (DSPs) have improved rapidly in the last decade and many have more
than enough computation power for our application. For example, Analog Devices
BlackFin line of DSPs (ADSP-21535) run at 300 MHz and have 308 KB of memory,
power saving features, multiple serial inputs an outputs and a good set of program-
ming tools. There are many other examples of equally powerful DSPs which would
suit our needs, probably the most important factor in deciding which one to use is
the quality of the development tools.
The biggest advantage to digitizing the entire signal and passing it into a DSP
50
is that it would enable us to record the entire signal as well as providing a flexible
platform for signal processing. Having these two modes of operation makes it easy to
record the entire signal and to test different methods of signal processing during post-
processing. Ideally it would be possible to extract the phase and frequency content of
a return signal as well as an accurate magnitude and arrival time. Unfortunately, the
fact that our signal is a constant frequency pulse severely limits our signal processing
options.
The phase information or polarity of the return signal can be used to determine
whether the reflected signal was caused by an object with an increased or decreased
acoustic impedance. Identifying the phase information from a pulse is difficult because
both the speed of sound and the distance to reflecting targets are variable. Thus, we
need to detect the polarity of return signal, not the phase angle of the received signal
relative to other pulses or a reference wave form.
If we were working with a system that had very few discrete returns such as
a radar signal reflecting off of airplanes, it would be easy to examine the received
pulse to determine if it initially went up or down and thus to determine the polarity.
However, this system is a sub-bottom profiler, and objects buried in sediment rarely
exhibit discrete returns due to the high degree of scattering. Since it is not possible
to use matched filtering to identify specific pulses, it is also not possible to identify
phase information from overlapping return signals. However, if there is more than one
object in the path of the acoustic wave, or if the object is thinner than the wavelength
of the signal, then phase information is likely to be invalid anyway.
The other piece of information that would be beneficial to calculate is the change
in frequency content or dispersion of the return signal. Being able to detect a fre-
quency shift or increase in width of the return pulse might be able to provide useful
information about the material properties of the sediment or buried objects. However,
in order to determine if it is possible to obtain this type information it is necessary
to measure the exact waveform produced by the transducer and to be able to digitize
the entire received signal.
Even though it might not be possible to extract the phase and frequency content
51
from the return signal, there are several methods of digital signal processing which
could be used to compress the digitized signal into a more manageable data rate.
Digital enveloping and linear quadrature filtering are both likely to be more effective
than the current analog enveloping circuit at accurately reporting the travel time and
magnitude of the return signal.
Digital enveloping uses the same idea of enveloping the return pulse as the analog
circuit; however, it is easier to implement good low-pass filters and more effective
down-sampling methods in a DSP than it is in analog electronics. Down sampling
methods include averaging or taking the maximum value over a period of time.
Perhaps the most promising alternative is quadrature sampling. Linear quadrature
filtering and sampling is accomplished by modulating the return signal with a sine
wave and another 90 degrees out of phase (i.e. a cosine). The frequency of the
sine and cosine waves should be very close to the same as the dominant frequency
of the incoming waveform. Each of the modulated signals is then sampled. The
magnitude of the original signal can be reconstructed from the sum of the squares of
the modulated signals. The phase angle relative to the modulating frequencies can
also be easily calculated. However it is not possible to calculate the polarity of a a
specific return signal. [21] [26] [5]
In order to properly reconstruct the original waveform including the frequency
content above 150 kHz it is necessary to sample the modulated signal at more than
300 kHz. However, this is not necessary in this case because the useful frequency
information would be a decrease in fundamental frequency of 150 kHz or a widening
of the envelope of the pulse both of which are reconstructible from a samples of the
modulated signals taken at 300 kHz in phase with the peaks.
Sampling modulated signals in phase with the peaks is equivalent to sampling our
signal at 300 kHz with 2 channels that are 90 degrees out of phase. Alternatively,
sampling the signal using a single channel at 600 kHz, results in signals being inter-
leaved. The magnitude of the return signal can be calculated by the square root of
the sum of the squares of the amplitudes as was mentioned earlier. If the resulting
300 kHz string of magnitudes was still more information than could be recorded by
52
our system, it could be down-sampled.
53
54
Chapter 5
Analysis of the Transducer
The transducer has been used in several field tests but has never been properly char-
acterized. The beam pattern, vertical and horizontal resolution and depth of pen-
etration have not been verified by experiment or analytical models. The analytical
models and field experiments presented in this chapter are an attempt to explore the
properties of the sensor, verify its functionality and quantify its behavior.
5.1 Resolution
5.1.1 Wavelength
The speed of sound in water and unconsolidated sediment is approximately 1500
m/s, therefore a 150 kHz frequency corresponds to a wavelength of about 1 cm by
the equation A = v/f, where A is the wavelength, v is the velocity of sound in the
medium, and f is the frequency. Thus, the theoretical limit of vertical resolution for
a 150 kHz transducer is 1 cm.
The actual resolution is dependent on the length and shape of the pulse (i.e. the
bandwidth) and the type signal processing used. In our case the wave form is 6 cycles
long, yielding a potential resolution of 6 cm. However, the current electronics envelope
the signal with a single pole low-pass filter so the actual resolution is considerably
less.
55
5.1.2 Beam Pattern
The horizontal resolution of the sub-bottom profiler is determined by the area that
is insonified by the transducer at the appropriate distance away. It is possible to
calculate the theoretical beam pattern of the transducer at this frequency. If the
transducer is modeled as a 30 cm piston, according to Urick [41], the beam pattern
is given by equation 5.1.
2 * J,(( 7r * sinO)Beam(O) = ( sine )2 (5.1)
where D = diameter = 30 cm, and A = wavelength = 1 cm and J is the Bessel
function of the first kind and first order. (see Figure 5-1)
Using this equation the far field beam width is approximately 4 degrees with
very small side lobes (theoretically 40 dB less than the main lobe). However, this
is probably an over estimation of the beam width. At half power (-3dB), the beam
width is approximately 1.5 degrees. Although the half power beam width is a common
number to use, it is probably an under estimation of the actual area that is insonified
by our sonar transducer. The beam width at one tenth the intensity, (-10 dB from the
main lobe) is about 2.5 degrees. Thus, 2 to 3 degrees is probably a good, conservative
estimate of the beam width. Figure 5-1 shows this graphically. The receiver is the
same as the transducer and not omni-directional so the actual horizontal resolution
is likely to be even better.
In order for the estimate of a 2 to 3 degree beam width to be accurate we must
be operating in the far field of the transducer. The standard minimum distance away
that the far approximation is applicable is the area of the transducer divided by the
wavelength.
Area 2 * pi * r 2
= >> 7.07m (5.2)A A
If the profiler is positioned 7 meters above the target, then the area that is insoni-
fled (using a 2.5 degree beam width) is tan(2.5deg)* 7 meters = 0.31 meters. This
makes sense because the diameter of the transducer is 30 cm, and you would expect
56
0
-30 30 2 -10 ----- - --- - -
2-20---50 dB
- - -4 0~_1 - -.. --- - - - -.--.-- -- - - - -
-60 60C,,
.- 100dB-- - - ~ ~ ~ ~ - -50 - ----
-70
-80-10 -8 -6 -4 -2 0 2 4 6 8 10
Angle off of the center axis (degrees)
(a) (b)
Figure 5-1: The left figure (a) is a graphical representation of the beam shape. Theright figure (b) is a quantitative graph useful for estimating the side lobes. The farfield beam width is approximately t2 degrees.
the beam width to be approximately the width of the transducer at the transition to
the far field.
Unfortunately, when the sensor has been used in the past to collect data in the
field, it has typically been positioned 2-4 meters above the sea floor. Clearly 2 to 4
meters is not in the far field of a 30 cm piston transducer operating at 150 kHz.
The beam pattern of the transducer can be described in terms of three different
regimes, the near field, the mid field, and the far field. The far field of the sensor
is the easiest to understand and characterize numerically because the sonar signal
can be approximated as a plane wave. The power decreases with the square of the
distance from the transducer and the beam width is approximately proportional to
the distance away.
In the near field of the sensor the wavelength is not negligible compared to the
distance from the sensor. In this region standing waves can develop causing large
fluctuations in power with small changes in distance from the sensor. Near field
effects are normally considered to be significant within a distance of 100 wavelengths
of the sensor (1 meter in our case.) Alternatively, another common approximation
for the extent of the near field is x < A"' = 1.75 meters. Thus, as long as the sensor
is used more than 1.8 meters away from the sea floor, rapid power fluctuations are
57
unlikely.
The mid field of the sensor is the transition region between the near field and
the far field. Rapid fluctuations in power with distance from the sensor are not
common but the acoustic wave cannot be assumed to a plane wave. The transducer
is actually made up of individual 1" transducers arranged in an unknown pattern.
The individual transducers are driven in phase with one another and no attempts
were made to calibrate the sensor to any specific focal length. Consequently, in the
mid field, the arrival time and power of the acoustic signal might depend on radial
distance from the center of the beam. Although the 2-3 degree estimation of the
beam width is not appropriate in the mid field, the beam width should not be much
greater than the transducer itself or about 30 cm.
Experimental Results
Since it is not possible to calculate the exact beam pattern in the mid field where the
transducer is commonly used, several swimming pool experiments were performed in
attempt to characterize the beam pattern. The results of these tests were disappoint-
ing. The sensor is incredibly sensitive to the angle of the target, surface roughness,
any any motion. In addition, the return signal from a metal sheet or the tile pool wall
is sufficient to saturate the variable gain amplifier so quantitative comparisons of the
amplitude of the return signal are not possible. Finally, the highly reflective environ-
ment of a swimming pool generates complicated multi-path returns from reflections
off of the walls and water surface.
The beam width 2.3 meters from the sensor was measured qualitatively by sliding
a metal sheet in front of the sensor. The return signal from the metal sheet was only
observed when sheet was directly in front of the transducer. Thus, at 2.3 meters the
beam width is effectively about 30 cm. Figure 5-2 shows data collected during this
test. During this test the sensor was resting on it's side on the bottom of the pool.
It was also noted that the sensor has fairly strong sidelobes. Figure 5-3 shows
the results of a swimming pool test in which the sensor was aimed at the wall of the
swimming pool. The distance between the sensor and the bottom of pool was varied
58
4000
13500
30002
2500
E3
2000
1500
5 1000
5006
022:53:30 22:53:49 22:54:08 22:54:26 22:54:45
Figure 5-2: This figure is the data collected during a swimming pool test to estimatethe beam width 2.1 meters away from the sensor. A 2 foot by 2 foot square metal platepositioned perpendicular to the transducer approximately 7.5 feet (2.3 m) away. Thetarget was slid along the bottom past the sensor. A return signal was only recordedwhen any portion of the metal plate was directly in front of the transducer. This testwas performed with a constant gain setting of 3.
from 0 to 1 meter. Multiple sidelobe reflections were observed in the data. Travel
time analysis indicates that the angle of the first sidelobe is approximately 30 to 45
degrees.
In order to further estimate the sidelobes another swimming pool test was per-
formed. The transducer was suspended in the middle of the deep end of the swimming
pool and aimed at the bottom. A 2'x2' sheet metal plate was positioned perpendic-
ular to the sensor and touching its side. The distance that the metal plate stuck out
past the bottom of the sensor was varied between 0 and 2 feet. No return signal from
the plate was observed at any point during the test. The most likely explanation
for this lack of response is that the metal plate had a very smooth surface and since
the plate was not oriented perpendicular to the sound wave, most of the energy was
reflected at the angle of incidence and not back at the sensor. Rougher targets such
as the water surface and the tile bottom of the swimming pool tend to reflect more
energy back toward the sensor.
59
Sidelobe Test
4000
1 3500
3000
2500
3
20000D'a)
4 1500
5 1000
5006
021:51:47 21:51:55 21:52:04 21:52:12 21:52:20
Figure 5-3: Data collected during a swimming pool test to estimate the near fieldsidelobes of the transducer. The transducer was aimed at the wall of the swimmingpool and the distance between the bottom of the pool and the side of the transducerwas lowered from one meter down until it was resting on the bottom . Multiplesidelobe reflections can be observed. This test was performed with a constant gainsetting of 3.
5.2 Depth of Penetration
The depth of penetration of a sub-bottom profiler is dependent on the type of sedi-
ment, the reflectivity of the target, and the sensitivity of the receiver and associated
electronics. In this section I will present a simple method for estimating the depth of
penetration of the system. In the next section this calculation will be expanded into
a simple 1-dimensional model of the system.
The return signal from a buried target is effected by many different factors, in-
cluding attenuation, scattering, reflection and transmission loss at boundaries. Since
sub-bottom profiling only operates in normal incidence, acoustic modeling is much
simpler than the general case. This model will ignore the effects of scattering cause by
irregularities at boundaries and concentrate on attenuation and reflections at bound-
aries. This simulation directly follows from simple ray tracing of Biot fast waves [11].
Biot slow waves are ignored because they are generally not observed in sub-bottom
profiling data due to their greater attenuation rate.
60
Since we are working in deep water, the water can be thought of as a homogeneous
half space, with the transducer/receiver is located a distance above the sea floor. The
sea floor can be thought of as another homogeneous half space with discrete objects
buried in it. Each half space and objects have a characteristic density, speed of sound,
and rate of attenuation.
5.2.1 Reflection Coefficients
The acoustic impedance of a material is the product of the density and the speed of
sound. If a propagating sound wave encounters a boundary between 2 materials with
different acoustic impedances, the sound wave is reflected and/or refracted much in
the same way as light. In other words, the angle of incidence equals the angle of
reflection and Snell's law governs the angle of refraction. In normal incidence, the
sound wave is either reflected or transmitted. The coefficient of reflection (KR) is
dependent on contrast in acoustic impedance across the boundary. Figure 5-4 shows
the geometry and material properties of a boundary.
TransducerMaterial 1:
Density = piVelocity v= , Incident Reflected
Wave Wave
Intensity I
Material 2:Density = P2 Transmitted
Velocity=V2 Wave
12
Figure 5-4: Diagram of a boundary between two materials. The intensity of the soundwave is the amplitude squared.
61
The reflection coefficient between 2 media is given by:
KR = V2 * P2 - VI * P1
V 1 * P1 ± V 2 * P2
KT = I - KR= 2 * v1 * p1
V1 * Pi + V2 * P2
I2 = Io (KT) 2
(5.3)
(5.4)
(5.5)
The compression wave velocity for most materials is independent of frequency
in the kHz frequencies of interest to sub-bottom profiling. [11]. Typical values for
several sediment types including sand, mixed sediment, and clay can be found in
Table 5.1. Typical acoustic parameters for several different object types can be found
in Tabletable-object-props.
Table 5.1: Typical sediment properties from Orsi and Dunn [27].
Material Density Velocity Reflection Coeff(kgm- 3 ) (ms-1 ) re water
Water 1000 1500Sand 2100 1734 0.41Sand-silt-clay 1740 1575 0.29Clay 1450 1496 0.18
Table 5.2: Acoustic properties ofsand-silt-clay mixture.
typical objects embedded in a medium grained
Material Density Velocity Acoustic Reflection Coeff(kgm- 3 ) (ms- 1) Imped. re sed
Sediment 1740 1575 2.7Aluminum 2700 6300 17 0.72Copper 8960 4650 42 0.88Granite 2700 5500 15 0.69Ceramic 2100 1600 3.4 0.10
62
5.2.2 Attenuation
Some of the energy of the transmitted wave is lost, or attenuated as it travels through
sediment. The primary mechanisms for attenuation in sediment are absorption and
scattering [30]. Attenuation can be modeled as an exponential decrease in the ampli-
tude of the the compression wave with time or correspondingly with distance traveled
(i.e. A(t) = Aoe- c.)
The rate of attenuation of compression waves is dependent on the frequency as
well as the type of sediment. Larger grained and more porous sediments, such as
sand, attenuate sound energy more rapidly than finer grained sediments such as silt
and clay. In addition, the higher the frequency the greater the rate of attenuation.
This increase in attenuation rate has been modeled as both directly proportional to
frequency [30] and also as proportional to the frequency squared [17]. Unfortunately,
150 kHz seems to fall at the transition between these two regimes. Additionally,
the wavelength (about 1 cm) is of approximately the same scale as irregularities in
the sea floor causing complex diffraction and scattering effects. Consequently, the
attenuation of sound waves in sediment is poorly understood for the 100 to 200 kHz
frequencies of interest to our system.
Attenuation rates of sediments can be described in many different units. Probably
the most useful units are dB/wavelength or (dB/m)/kHz. Quinquis et al. [30] report
that typical values for attenuation rates of sediment are between 0.1 dB/wavelength
and 1 dB/wavelength. This corresponds to about 10 to 100 dB/m for our 150 kHz
system. LeBlanc et al. published graphs of attenuation rates ranging from 0.1 to
0.8 (dB/m)/kHz which corresponds to 15 to 120 dB/m at 150 kHz. Both of these
estimates of the range of possible attenuation rates agree fairly well and demonstrate
that there is a large amount of variation. Consequently the depth of penetration
achievable by our system will be highly variable depending on the sediment type.
The attenuation rate of salt water at 150 kHz is almost negligible for the short
distances used in our sub-bottom sonar system. Typical distances are under 10 meters
and attenuation rates are as low as 0.05 to 0.1 dB/m.
63
5.2.3 Estimate of the Depth of Penetration
The depth of penetration of the sub-bottom profiler is dependent on the rate of
attenuation of the sediment, the reflectivity of the target, and the sensitivity of the
sensor and signal processing electronics. According to Marine Sonics, the receiver pre-
amp noise is approximately 1 pVolt. The maximum signal which does not saturate
the electronics is about 1 Volt. Thus, the maximum sensitivity of the system is 120dB.
The achievable sensitivity is probably less, but 120 dB is a good upper bound on what
we could possibly detect.
Figure 5-5 represents a plot of the reflected signal due to a target with a coefficient
of reflection of 0.1 buried in various types of sediment. The x-axis is the travel time
after the primary surface return. The red pluses are targets buried in sand with a
rate of attenuation of 75 dB/m. The green triangles are a medium grained sediment
with an attenuation rate of 50 dB/m, and the blue asterisks are clay with a rate
of attenuation of 25 dB/m. The sound velocity, densities, and sediment reflection
coefficients are those listed in Table 5.1. The circled symbols on the far left are the
primary sea floor returns which correspond to the reflection due to the initial sediment
interface. The rest of the asterisks represent targets buried at different depths from
10cm to im. From this graph it is clear that the depth of penetration in sand is
estimated to be 50 cm, in mixed sediment it is 80 cm and in clay it is 180 cm (not
shown on graph).
5.3 Model of the Sub-Bottom Profiler
Unfortunately our sub-bottom profiler does not collect enough data to do a full inver-
sion of the type used in seismic arrays or other geophysical surveys. If the acoustic
information were collected over an array of points then several different forward and
inverse models could be used [6] [45]. However, due to the limitations of data col-
lected from a single frequency, single channel profiler used in normal incidence, it is
only possible to make a forward model of data collected from the sensor.
The simple calculation of depth of penetration presented in the last section was
64
" sed. surf.
-20-
10 cm
-40 ACO *C0 + A * 50cm0. 50 cm
-600
* m'E-0 50 cmCO
-100-50 cm 80 cm
0 100 200 300 400 500 600 700Time from first surface reflection (microseconds)
Figure 5-5: Graph of reflected signal strength versus travel time for targets with areflection coefficient of 0.1 in various type of sediment. The red pluses on the bottomare sand, green triangles are a medium grained sediment, and the blue asterisks onthe top are clay. The first circled symbol on the left represents the return from thesediment surface, and the other symbols represent targets buried at 10 centimeterintervals from 10 cm through 1 meter.
turned into a model of the system. The user can define a 2-dimensional of field
objects. This 2-dimensional field of objects consists of cells one square centimeter in
size. Each cell has a characteristic density, velocity, and attenuation rate. The data
collected by the sub-bottom profiler as it is moved across the top of the object field
is simulated by a series of 1-dimensional models. A 2-dimensional image is created
from the time series data in the same way as data collected from the actual sensor.
The 1-dimensional model considers the effects of attenuation loss as well as reflec-
tion and transmission at boundaries. The model was implemented recursively so that
the effects of multiple reflections or reverberations could be considered. A propagat-
ing wave is only removed if it leaves the object field, the time limit expires, or the
amplitude drops below a reasonable detection limit. In this way all possible return
signals resulting from reflection from horizontal boundaries can be modeled.
The biggest limitation of this model is that no reflections outside the 1-D strip
are considered. Additionally, no scattering or diffraction effects are modeled. Since
65
all objects are composed of 1 cm squares, all boundaries are considered to be perpen-
dicular to the direction of propagation of the wave. This restriction would be easy to
remove by associating an angle with each boundary and performing a complete 2D
model; however, time limitations prohibited it at this time. Loss due to scattering
from uneven surfaces could be modeled as additional loss at each boundary. The final
major source of signal loss that is not modeled is loss due to beam spreading.
The pulse shape is modeled by 6 amplitude coefficients to give it a roughly Gaus-
sian shape. The duration of the pulse was 6 time segments in length for a total of
40 microseconds. There was assumed to be no dispersion of the Gaussian pulse. The
150 kHz wave was not modeled, but the attenuation values are those appropriate for
a 150kHz signal.
Since the individual vertical strips of the object field were 1 cm in width, the
initial model acts as if the beam width were 1 cm. A sample of an object field and
resulting data can be found in Figure 5-6.
In order to model the actual characteristic beam width of the transducer a weighted
average of multiple strips was taken. This moving average method allows for a more
accurate estimation of the actual return signal recorded by the sensor. The weights
used were chosen assuming that the beam width is 30 cm with a half power amplitude
at 15 cm in diameter and a shape roughly equivalent to the far field beam. (see Figure
5-7)
The last important feature of the current system that is not considered by the
previous model is the signal processing electronics. The low pass filter has a significant
effect on the observed data. The low-pass filter smears the initial surface return such
that the buried objects are barely observable. (See Figure 5-8.) In actuality the
TVG system might help to reduce the smearing since amplification happens prior to
low-pass filtering.
66
Relative density of objects
50
100
150
200
250
300
350
400
1
2
E:E3
4
5
50 100 150 200 250distance (cm)
Figure 5-6:impedances.beam width
300 350 400 450 500
The top figure is the object field. Colors represent different acousticThe bottom figure is the time series returns created by the model. A
of 1 cm is used. The colors represent the values on a log scale
67
50 100 150 200 250 300 350 400 450 500distance (cm)
Return signal from a 6 cycle gaussian pulse with a 1cm beam width (dB)
0.9-
0.8-
,0.7
80.6
0.5
0.3 -
02-
-15 -10 -5 0 5 10 10distance from center (cm)
Return signal from a gaussian pulse with a 30cm beam width (dB)
E
3)
5
50 100 150 200 250 300 350 400 450 500distance (cm)
Figure 5-7: The top figure shows the coefficients of the moving average filter. The bot-tom figure is the modeled profiler data including the moving average approximationof the beam width.
68
1
H4
5
II I
50 100 150 200 250 300 350 400 450 500distance (cm)
Figure 5-8: Modeled data including the effects of a low-pass filter.displayed on a log scale and time varying gain is not modeled.
The colors are
69
1
I I I
70
Chapter 6
Experimental Results
Although the sub-bottom profiler has been used in several field experiments it has
never been used to collect data over a known set of objects. It was necessary to collect
such a data set in order to determine if the prototype sub-bottom profiler is capable
of detecting buried objects and to verify that the data generated by the sensor is
similar to predicted data of the model described in the last chapter. Unfortunately,
it is very unusual to know the exact location, size, material, and depth of objects
buried in the sea floor. Consequently, it was necessary to construct a test of varied
objects by burying them.
6.1 Experiment Description
A field site was chosen in Salisbury Beach State Reservation near the outlet of the
Merrimac River in Massachusetts. This test site was chosen because it had a very fine
silty sediment and a large tidal range. In addition, this location is relatively close to
MIT and has fairly easy access from a near by parking lot. The site also has a very
flat working area which is exposed at low tide near a steep bank and an area which
is dry at high tide.
On May 19, 2002, a trench was dug in the sediment at low tide and several types
of objects were buried at different depths. At high tide, sub-bottom sonar data was
collected to create a cross-sectional image of the objects buried in the trench. A PVC
71
and aluminum gantry was built over the site to ensure that the sub-bottom sonar
track-line was over the objects. Figure 6-1 shows pictures of the trench and gantry
structure.
The trench was approximately 10 feet long, 2 feet wide and 2 feet deep. Various
objects were buried including flat sheet metal, a block of granite, and several ceramic
vessels. The position of each object was recorded at the time that it was placed in the
trench an again after it was buried by leaving a thin wooden stake in contact with it
as it was buried. After the trench was filled in, the stakes were removed to determine
the actual burial depths. Table 6.1 lists the positions and types of objects that were
buried. A plan view of the objects is in Figure 6-2 and a vertical cross-section is in
Figure 6-3.
Table 6.1: Types and positions of objects buried at the test site.
Object Type Dimensions Distance Depth(± 6") (t 3")
Aluminum sheet metal 14" x 22" 2' 1",5' 7" 7"7' 3" 14"15'5" 2-6" (uneven)
Granite block 6" x 12" x 8" 9' 2" 24"Copper sheet metal 12" x 22" 10, 2" 16-18"Ceramic plate (large) outer diam. = 16.5" 12' 16"
inner diam. = 13.5"height = 2"
Ceramic plate (small) outer diam. = 12.5" 12' 1" 6"inner diam. = 10.5"height = 2"
two flower pots diameter = 9" 13' 9" 8"placed rim to rim height = 10"
It was hoped that the shallow metal plates would act as markers for either end
of the trench. Identical metal plates were buried at different depths to determine
the rate of attenuation of the sediment. Ceramic objects were buried to provide low
contrast targets. Flower pots were buried end to end and not full of sediment to
simulate a liquid filled amphora. It was hoped that a resonance could be observed.
72
Figure 6-1: Pictures of the trench (top) and gantry structure over the trench afterthe objects were buried (bottom).
73
loft
AL AL CUILBEE H C
Zf N
AL 0PVC
Gantry
Granite Flat FlowerCeramic PotsPlates
Figure 6-2: Top view of objects buried at the test site
50 100 150 200 250 300 350 400 450 500distance (cm)
Figure 6-3: Vertical cross-section of the test site
74
S 5 ft
0PVC
Gantry
50
100
70.c 150
200
250
300
----~
Return signal from a gaussian pulse with a 30cm beam width (dB)
-20
1
-40
2
-60
E3
-804
5 -
6 -120
50 100 150 200 250 300 350 400 450 500distance (cm)
Figure 6-4: Predicted data displayed on a log scale.
A model of the predicted data can be found in Figure 6-4. For this model the
sediment was assumed to be similar to the sand-silt-clay mixture of Orsi and Dunn.
This mixed sediment has an attenuation rate of 50 dB/m.
75
6.2 Results
Twelve sets of data were collected over the test site with different gain settings.
Unfortunately, the data was rather disappointing because none of the objects were
clearly visible in the primary reflection. Figure 6-5 shows two different data sets
collected with constant gain.
The x-axis of the data is survey time which roughly corresponds to position along
the trench. The right side is the north end of the trench. The y-axis of the data is
travel time which corresponds to distance from the sensor. If the sound velocity in
the medium is 1500 m/s then one millisecond corresponds to a total distance traveled
of 1.5 meters. Since the data recorded is the reflection off of the target, the distance
between the sensor and the object is half of the total distance traveled or 0.75 meters
per millisecond.
The top horizontal red stripe (under 1 ms) is the generation of the sonar ping.
The start of the transmitted ping occurs at about 0.1 ms, but is not recorded because
the amplifier is set to a very low gain during that time period.
The second stripe (2 to 3 ms) is the reflection off of the sea floor and the buried
objects. The earliest sea floor reflection occurs at about 2.3 ms. At a speed of 1500
m/s, a travel time of 2.3 milliseconds corresponds to a distance between the sensor
and the sea floor of 1.7 meters which closely matches the actual height of the sensor
above the sea floor.
There are several discrete objects visible in the water column (between 1 and 2
ms.) The locations of these objects between the different data sets. A large amount of
seaweed was observed floating in the water during the tests, therefore it seems likely
that the seaweed caused these reflections.
Between 4 and 6 milliseconds there are two distinct arrivals that occur at approx-
imately double the time of the initial sea floor reflection. The first arrival (about
4.6 ms) is the double bounce between the sensor and the sea floor. In other words,
the sound wave reflected off the sea floor, then reflected off of the sensor, and then
reflected off of the sea floor a second time before it was recorded by the sensor. The
76
4000
1 3500
30002
2500
E3
2000
41500
5 1000
5006
18:03:12 18:03:24 18:03:36 18:03:48 18:04:00 0
Survey Time
S N
4000
1 3500
3000
2500
E3
-6 2000
1500
1000
5006
018:07:18 18:07:32 18:07:46 18:08:00 18:08:14
Survey Time
Figure 6-5: The top figure is data collected with a constant gain of 3. The bottomfigure was collected with a constant gain of 10. When the upper figure data wascollected there was probably a pause before the sensor began to move.
77
S N
msonar ping 4000
1 generation
2 -3500
water surface- -- -primary bottom 3000
4 reflection4' 4"'
5 2500
Transducer
double bounce 2000off of sensor
X 6" Transmited 8 1500Wav e 9 "-double bouce
off of water 1000surface
pool bottom 11 500
12
13 010:14:56 10:15:04 10:15:11 10:15:18
Figure 6-6: Swimming pool test to verify the hypothesis that the second doublebounce was caused by a reflection off of the surface of the water. The sensor waslocated in the middle of the deep end of the pool aimed at the bottom of the swimmingpool. The transducer was 4'4" below the surface of the water and 8'6" from thebottom of the swimming pool. The primary reflection from the bottom of the pooloccurs at 3.5 ms, the first double bounce occurs at 7.1 ms and the second doublebounce occurs at 8.9 ms. At 1500 m/s the predicted distance between the watersurface and the bottom of the sensor is 4'" and the distance between the sensor andthe bottom of the pool is 8'7" which are very close to the actual measured values.
second arrival (about 5.1 ms) occurs about 0.5 milliseconds after the first double
bounce. The bottom of the sonar sensor was located about 15 inches (38 cm) below
the surface of the water. Because this distance matches the difference in travel time
between the two double bounces, it is likely that the second arrival is caused by a
sound wave which reflected off of the bottom, then off of the surface of the water,
then off of the bottom a second time before being recorded by the sensor. In order
to test this hypothesis, an experiment was performed in the swimming pool and a
similar double bounce off of the water surface was observed (see Figure 6-6.)
Since the data collected using a constant gain does not increase the amplification
with depth is is more fair to compare the data with modeled data plotted on a linear
scale instead of a log scale. In addition the effects of the low-pass filter should be
taken into account. Figure 6-7 shows the modeled data including the low-pass filter
78
Modeled data including lowpass filter displayed on a linear scale
4000
1 3500
-30002
2500
U,E3
22000
1500
5 1000
5006
050 100 150 200 250 300 350 400 450 500
distance (cm)
Figure 6-7: Model data including the low pass filter and plotted on a linear scale
plotted on a linear scale. This model shows that it is reasonable to predict that none
of the objects would be visible in the primary return of the constant gain data sets.
Although none of the buried objects are clearly visible in the primary return
(between 2 and 3 ms), I believe that the bright red spot in the double bounce was
caused by the metal plate which was buried 1" under the surface. Although it is is
not present in all of the data sets, it occurs in several of them. The models shown
in Figure 6-4 and 6-7 predicts a strong signal in the double bounce due to the metal
plate buried at the south end of the trench.
There was a similar metal plate buried close to the surface on the north end of
the trench, however the data does not a have strong signal in the double bounce.
One reason for this is that the metal plate was crumpled and damaged when it was
79
buried. Since the actual burial depth varied between 2 and 6 inches and the metal
sheet was not flat, it is logical that the metal plate on the north end of the trench
did not produce and equivalent signal to the metal plate on the south end.
The most promising feature of the data is that there is a qualitative difference
between the south end of the survey area and the trench full of buried objects. This
difference is even more clear in the data collected using time varying gain (see Figure
6-8.) The data collected between 0.75 milliseconds and 1 millisecond shows a strong
reflection in the area of the trench and a weak reflection over the undisturbed area at
the south end of the trench. The apparent horizontal strips in the data are due to the
non-linearities of the time varying gain. The very bright signal after 2.5 milliseconds
is caused by the saturation of the amplifier by the double bounce.
No distinct objects are visible in the data between 1 and 2 milliseconds. This
makes sense because the all of the objects were buried less than 2 feet below the sea
floor. Thus, the expected arrival time of primary reflections from the objects is less
than 1 millisecond after the initial sea floor reflection.
80
N
- -4000
- -3500
- -3000
E
CD
2
3
18:22:31
00
2000
1500
1000
500
18:22:49 18:23:05
Survey Time
S
18:23:23
N
18:23:40
- -4000
- -3500
m al - - 3000
'a
3
18:30:57 18:30:39 18:30:21
Survey Time
18:30:03 18:29:45
Figure 6-8: Both data sets were collected with time varying gain and a 2 millisecondlockout. The top figure was collected with a time varying gain which started at 10at 0.3 milliseconds and increased in steps of 10 every 0.4 milliseconds thereafter. Thegain for the bottom figure started at 10 and increased in steps of 12. The black regionin the first 0.3 milliseconds is the low gain period of the amplifier. The initial seafloor reflection was cut off by the amplifier and probably occurs between 0.25 and 0.3milliseconds.
81
S
, i j , .
6.3 Analysis
There are several possible reasons why the bottom penetrating sonar did not clearly
detect the objects we buried. The transducer was only 1.7 meters above above the
sea floor, thus the buried objects were not in the far field of the sensor. Additionally,
the objects were buried on the same day as the data was collected so the sediment
was highly disturbed. The effects of surface scattering due to irregularities in the
sea floor are not well understood at the primary frequency used by this transducer.
Consequently, the response from the objects could have been obscured by the noise
caused by the initial surface return. In addition, swimming pool tests indicate that
the transducer is highly sensitive to small movements which cause large amounts of
noise which could obscure the return signal from the objects. Finally, the sensor
might not have the resolution to detect objects that are as small and close together
as the ones buried in the test site.
The first reason that the sensor might not have performed as well as we had
hoped is that it was not used in the far field of the transducer. In fact, the distance
of 1.7 meters between the sensor and the sea floor is almost in the near field of the
transducer. Since the acoustic signal is not a plane wave in the near field, the arrival
time and intensity of the sound wave might vary unpredictably with axial and radial
distance from the sensor. Consequently, it is possible that the acoustic wave would
not arrive at all parts of a perfectly flat target at the the same. This could effectively
make the 40 microsecond pulse much longer and complicate the reflected signal and
thus degrade the resolution. Swimming pool tests indicate that a flat piece of sheet
metal commonly generates a return signal that is 300 to 500 microseconds in length
instead of one equivalent to the processed version of generated pulse of about 40 to
100 microseconds. The height of the sensor above the sea floor was limited in the
Salisbury Beach test by the tidal range. The only way to increase this distance would
be to use a test site with a larger tidal range or bury the objects in deep water using
scuba divers.
A second possible reason that discrete objects were not visible in the data is that
82
the data was collected on the same day that the objects were buried. The disturbed
sediment only had 5 hours to settle before the data was collected. It is possible
that small air bubbles were trapped in the sediment thereby increasing the rate of
attenuation. In addition, when the gantry structure was removed on the following day
it was noted that sediment in the trench was considerably softer than the surrounding
sediment. The surface of the sediment was uneven and depressed by approximately
4 inches. The disturbed nature of the sediment above the buried objects could have
scattered and attenuated the sound wave before it reached the buried objects. This
hypothesis could be tested by going back to the test site and collecting another data
set.
The measured data has a considerably longer primary surface return than the
modeled data. In addition to possibly being caused by near field effect or disturbed
sediment, this could be caused by scattering effects due to irregularities in the sea
floor. Although the sediment was fine grained and did not include any small rock
or shells, the surface was not flat. It had ripples and residual footprints which could
effectively lengthen the primary surface return. The diffraction and scattering effects
of such irregularities were not modeled and are poorly understood at 150 kHz. It is
possible that the primary surface reflection caused a return signal that is between 0.5
and 1 ms in length. Consequently, the reflection from the objects which were buried
less than 2 feet from the surface were masked by the stronger multiple reflections due
to the sea floor interface.
One final reason that the objects were not detected at Salisbury Beach is that
objects were too small and close together to be detected by the current sub-bottom
profiler. It is possible that the model of the transducer is incorrect and the actual
transmitted acoustic signal is actually longer than 40 microseconds or that the beam
width is wider than 30 cm. Alternatively, the model might be correct but the sensor
might only be able to detect objects with a very high contrast in acoustic impedance
which are oriented in such a way to reflect the acoustic energy directly back at the
transducer.
83
6.4 Further Work
The Salisbury Beach experiment shows that the prototype sub-bottom profiler was
not capable of detecting buried objects such as sheet metal less than 2 meters from
the sensor. Previous data sets such as the Tanit shipwreck data (see Figure 3-1),
where the sediment was undisturbed and the sensor was positioned 3-4 meters above
the sea floor seem to indicate that the sensor is capable of detecting some structure
below the sea floor. Although it is possible that the buried objects in the Tanit data
could be the result of surface scattering, this data suggests that the sensor might be
able to collect better data if it is positioned farther away from the sea floor.
There are two different methods which could be used to determine the minimum
operating distance between the sensor and the sea floor. The first method is to collect
data over a known set of objects at variable heights above the sea floor and compare
the results. The second method is to measure the transmitted acoustic signal as a
function of axial and radial distances from the transducer. The first method has the
advantage that it validates the functionality of the sensor while the second method
increases knowledge of the beam pattern thereby improving the model of the system.
The process of doing either of these experiments exhaustively is more difficult
than it initially seems. Collecting data sets at varying heights requires a set of buried
objects whose position is precisely know. Mapping out the beam pattern requires
being able to precisely positioning a hydrophone at relatively large distances from
the sensor in an anechoic environment. However, it might be more realistic and still
quite beneficial to do a modified version of both of them.
For example, it is comparatively easy to measure the acoustic signal quantitatively
at several different axial distances. If the hydrophone were small enough it might be
possible to qualitatively measure the acoustic signal as a function of radial distance
by sliding it past. A similar version of this experiment was performed by suspending
the sensor from a lap-lane in the deep end of the swimming pool. A metal sheet
was suspended as a target at several different distances and the reflected signal was
measured in attempt to measure both the strength and length of the return signal.
84
Unfortunately it was not possible to hold the metal plate or the sensor still enough
to collect good data. The experiment could be repeated using either rigid mounting
frame or more ropes to constrain the motion. In addition, the swimming pool proved
to be a very difficult working environment due the enclosed and highly reflective
environment.
It would also be comparatively easy to take a collect data sets at variable heights
over a set of more or less known objects such as the previously excavated Defense
shipwreck in Penobscot Bay, Maine. Although the locations of the buried objects is
not precisely known it is still possible to compare the data taken at different heights
above the sea floor. However, if a navigated data set were taken over a known set
of objects it would be possible to overlay the locations of the objects over the data.
This type of data could be used to develop methods for processing the data and to
determine the range of objects which the sensor can detect. If the sensor cannot detect
large and highly reflective buried objects such as corner reflectors, the improvements
to the electronics suggested earlier will not be of any use.
85
86
Chapter 7
Design Recommendations for
Future Systems
Ideally a bottom penetrating sonar system would be able to produce photographic
quality images of the subsurface with centimeter scale resolution. However, as the
prototype bottom profiler analyzed in this thesis demonstrates there are many diffi-
culties which will need to be overcome in order to solve this problem. The increasing
attenuation of acoustic energy with frequency creates a trade off between resolution
and depth of penetration. Thus, if the desired depth of penetration of the system is
several meters in moderately attenuating sediment, the resolution will be restricted
to be more one centimeter. In addition, archaeological shipwreck sites tend to have
a large number of exposed artifacts which result in a very complicated interface be-
tween the water and the sediment. This complicated interface causes a large amount
of scattering and reduces the amount of coherent sound energy which penetrates the
sub-surface.
Narrowing the effective beam width is perhaps the most critical aspect of designing
the next generation sub-bottom sonar. A very narrow beam instrument is the only
way to create accurate images without complicated tomographic inversion methods
which do not work very well in an environment as complex as an archaeological site.
To reduce the effective beam width to on the order of 5 cm it will be necessary to work
in the near field of the beam and use focusing and beam-forming techniques similar to
87
the ones used by medical ultrasound. Working in the near field of the sensor requires
using a transducer with a larger aperture, either synthetic or physical.
Increasing the size of the array of transducers causes a new set of challenges. The
traditional method of achieving a large array is a fixed grid of transducers which
imposes a minimum size on the vehicle used to deploy it. Another possibility is that
the size of AUVs is decreasing, so the concept of using multiple AUVs is becoming
more appealing.
Another advantage of going to a multi-channel, multi-static sonar system is that
is is possible to make inverse acoustic models of the data. To learn as much as
possible about objects buried under the sea floor it is important not to be restricted
to analyzing the data as images. Instead it useful to be able make quantitative
forward and inverse models of the system as well as to apply deconvolution filters
similar to the ones used in ground penetrating radar. In order for complete inverse
acoustic models to be performed a larger dataset than is provided by a single channel
reflection profiler is necessary. A multi-channel, multi-static system can supply this
data.
The other important consideration is what type of signal should be used by the
sonar. Chirp signal allow for an increased depth of penetration for the same amplitude
signal by increasing the signal to noise ratio. However, the true advantage of chirp
signals is that is is possible to extract frequency shift and phase information from the
resulting data.
The biggest drawback to using a chirp signal is that they are difficult to generate.
The characteristics of transducers make it essential to measure the actual waveform
and adjust the electrical signal by the inverse transform of the transducer. Because
chirp signals are more difficult to generate than single frequency pulse signals, they
cannot be generated at the same amplitude. Additional must be taken when im-
plementing the matched filter for chirp signals in this frequency range because the
frequencies are not attenuated equally. The final disadvantage to chirp signals is that
beam-forming is much more difficult.
In my opinion the best signal to use would be a chirp signal that varies linearly
88
in frequency between 50 kHz and 200 kHz. A 50us signal with a Blackman-Harris
window would allow accurate analysis of dispersion in the windowed pulse. A 50 Psec
pulse yields a total of 10 cycles. The auto correlation of this signal is a Gaussian
pulse with a width of 4.lus. Assuming a speed of sound of 1500 m/s this corresponds
to a vertical resolution of 6.2 mm.
An inherent assumption in a high resolution bottom penetrating sonar is that it
is possible to precisely locate and navigate the sensor. Centimeter accuracy data is
meaningless without centimeter level positioning. Any platform which is used to col-
lect high resolution sub-bottom profiling data must be capable of precision navigation,
passively stable in pitch an roll, and capable of moving slowly and hovering.
89
90
Chapter 8
Conclusion
The goal of this thesis was to analyze the characteristics and limitations of the current
sub-bottom profiler with the intent of improving our ability to interpret the data that
it collects. By characterizing the transducer and the signal processing electronics it
should be possible to collect quantitative data suitable for acoustic modeling. By
using acoustic modeling techniques it should be possible to extract more information
about the material type, size, and number of buried objects than is possible by only
looking at an image of the data.
In order to collect quantitative data with the current system it was necessary to
understand the properties of the transducer. Additionally, there needed to be a way
to correlate a voltage level produced by the receiver with a number recorded by the
data logging system. Using the characteristics of the transducer and the electronics,
a model the behavior of the system was created. This model was tested by collecting
data with the sensor over a know set of objects. Unfortunately, the results of this
field test were disappointing and further work will be necessary to fully understand
the characteristics and capabilities of the system.
The transducer characteristics were estimated by several theoretical calculations.
In the far field the transmitter has a beam width of about 2-3 degrees. The far field
of the transmitter begins at approximately 7 meters. At 7 meters, the beam width is
about 30 cm. Consequently, there is a minimum bound on the horizontal resolution of
the sensor of about 30 cm. The near field beam width was estimated experimentally
91
and seemed to be about 30 cm. The transducer has relatively strong sidelobes at
about 30 to 45 degrees from the main lobe.
The theoretical vertical resolution of the signal produced by the transducer in
the far field is 6 centimeters. The actual vertical resolution is far less due to the
current signal processing methods. The current method of enveloping the signal with
a low-pass filter smears the received signal in time, thus severely reducing the vertical
resolution. Experimental results show that near field effects and the noise caused by
scattering at uneven boundaries is probably the largest factor which limits resolution
The depth of penetration of the sub-bottom profiler is highly dependent on the
type of sediment and the reflectivity of the buried targets. The depth of penetration
in coarse grained sand is theoretically less than half a meter, while in clay it could by
up to 2 meters. Models and field experiments indicate that multiple reflections can
have magnitudes that are much larger than the primary reflection of a more deeply
buried object and thus limit the effective depth of penetration.
In order to correlate the numerical values recorded by the data logging system
with the received signal the effects of the time varying gain must be removed during
post processing. In order for this to be possible, the TVG must be used in the linear
region of the variable amplifier which corresponds to numerical gain settings between
20 and 100. A gain setting of 20 corresponds to 8 dB amplification. An increase in
the numerical setting by 1 corresponds to a 1 dB increase in amplification. The slope
of the time varying gain is the step-size setting multiplied by 2.5 dB / ins. As long
as the step-size is under 7, the error introduced by assuming a linear slope is under
1dB.
However, in order to counteract the high rates of attenuation sediment the time
varying gain must be used at a very large step sizes. For every 10 dB/m increase
in attenuation, the step size must increase by 6 in order to counteract the loss due
to penetration of the sediment. For example, a clay with a low attenuation of 25
dB/m requires a step size of 14, whereas a medium grained mixed sediment with an
attenuation rate of 50 dB/m should require a step size of 30. The Salisbury Beach
data was recorded with a maximum step size of 15. Since the object were buried
92
less than 2 feet, the return signals would have been less than one 1 ms after the
primary surface return. The data appeared to have an approximately constant value
throughout this period.
The enveloping circuit consists of an amplifier, a rectifier, and a low-pass filter.
This circuit transforms the output of the variable gain amplifier into a 0 to 10 Volt
signal suitable for digitization by the 12-bit A/D. If the variable amplifier is in sat-
uration it produces a ±1.7 V waveform which corresponds to a 9.4 V input to the
A/D. The low-pass filter is a single pole RC circuit with a time constant of 40 pusec.
A model of the transducer and signal processing was created to compare with
measured data from the Salisbury Beach experiment. It was hoped that it would
be possible to use this model to be able to better characterize the resolution of the
sub-bottom profiler. Unfortunately since the data did not clearly identify any of the
buried objects this was not possible. However, the model and pool experiments did
prove useful to explain some of the phenomena which were observed in the data,
such as the strong signal from the metal plate in the double bounce. Although the
experiment did not yield any information about the resolution of the the sensor it
did show that it is not possible to use the sensor in the near field and that is unlikely
that the sensor will be capable of detecting low contrast objects close to the surface
of the sea floor. Further research will be necessary to determine if the transducer is
more effective in the far field of the beam.
93
94
Appendix A
Schematics
A.1 Amplification and enveloping
Enveloping Circuit
+5
10k 10 kTVG Circuit low-pass filter
.-- AD605 9 10 k 68. 12 _echo in -wr T17L12 . T 2 D7
+c 1.2 k 10 + + 13 +2piLT13
(mrface nmrd) (%w k ~u 4.7 an F
In-mu Vaa ]acainert carl LT1127 Op Ampsk Itahave supply voltages
Sof +1- 15 VLT1127
MC1(42) D 22
MEO(43) 1 xLC1SPCSO(44) 3 L /
95
A.2 TVG suface mount board
Time Varying Gain Circuit
Echo In
(red+ white)
Co n 95kC cntwl -AWr
(black + white)0 to 12V 249k
20-
O.luF__O.luF
22nF 22 cl=
+5v
Fair-Rik
2551027O
16 2.5 V
1 li k2af14-
+ I UF+
Ole? 1.2k
- -M--- Vautleadedresistor (blue + white)
GND
0343
E In- = +5
C-d .wC2= EEM
EM +wut)Gain EC
(blck ZluW)
96
Gaini Vref
Chi- Out
Chl + FBK 1
OND I AD605 vOND 2 vCh2+ FBK 2
Ch2- Ot:
Gain2 V oc
2
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