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Phoebe L. Hauff Phoebe L. Hauff
Cari Deyell-Wurst Cari Deyell-Wurst
William KerbyWilliam Kerby
PRIZE
INTRODUCTION
This has also led to many programs that attempt to automate the sample handling and mineral identification processes.
There are numerous libraries and algorithms available.
They are briefly described here.
We have also done a Round Robin to demonstrate effectiveness of some of the algorithms.
Expanded summaries (www.spectral-international.com)
“Spectral geology” applications (including satellite, airborne, core scanning and field measurements) have become very common due to significant technological advancements and improved instrumentation.
OVERVIEWOVERVIEW
SPECTRAL LIBRARIESSPECTRAL LIBRARIES
Data BasesData Bases
contain spectra, ancillary information, physical contain spectra, ancillary information, physical properties, references, associated species, properties, references, associated species, locationlocation.
CHLORITES
CRYSTALLINITY
• Spectral libraries contain reference spectra, which are Spectral libraries contain reference spectra, which are compared against an unknown spectrum using compared against an unknown spectrum using computer computer automated ID techniques.automated ID techniques.
• Without them, it would be difficult to do interpretationWithout them, it would be difficult to do interpretation
Minerals are highly variable –Minerals are highly variable –composition, wavelength, composition, wavelength, profile, crystallinityprofile, crystallinity
Difficult to automateDifficult to automate
SpecMIN:SpecMIN:SPECMIN is a mineral identification system for spectroscopy that includes an extensive and dynamic SPECMIN is a mineral identification system for spectroscopy that includes an extensive and dynamic library of reference spectra for minerals, wavelength search/match tables, physical properties of each library of reference spectra for minerals, wavelength search/match tables, physical properties of each species in the database, and literature references for the infrared active mineral phases. The spectral species in the database, and literature references for the infrared active mineral phases. The spectral library includes a minimum of two different samples per mineral that show compositional differences library includes a minimum of two different samples per mineral that show compositional differences within mineral species. In addition to mining applications, SPECMIN can also be used in remote within mineral species. In addition to mining applications, SPECMIN can also be used in remote
sensing applications for ground truthingsensing applications for ground truthing.. www.spectral-international.com
CSIRO:CSIRO:
Inbuilt reference library of spectra of common minerals. As well as the mineral spectra, the library Inbuilt reference library of spectra of common minerals. As well as the mineral spectra, the library also includes some artifact materials such as vegetation, plastic and marker pen which could also also includes some artifact materials such as vegetation, plastic and marker pen which could also
potentially contribute to your project spectrapotentially contribute to your project spectra .. www.csiro
USGS:USGS: The concept and identification work basic to this library was started by Dr Graham Hunt in the 1970’s.The concept and identification work basic to this library was started by Dr Graham Hunt in the 1970’s.The library is used as a reference for materials identification in remote sensing images. It can also be The library is used as a reference for materials identification in remote sensing images. It can also be used for identification of laboratory and field spectrometer data. The software used to manage this used for identification of laboratory and field spectrometer data. The software used to manage this library is specPR. The library is available without cost as a download from the internet. library is specPR. The library is available without cost as a download from the internet. http://speclab.cr.usgs.gov/spectral.lib06
KNOWN VIS-SWIR LIBRARIESKNOWN VIS-SWIR LIBRARIESThe libraries listed here are the better known ones. There are innumerable little The libraries listed here are the better known ones. There are innumerable little ones targeted at one mineral, one mineral group, vegetation. The addresses for ones targeted at one mineral, one mineral group, vegetation. The addresses for the common ones are included and will be on the SII websitethe common ones are included and will be on the SII website.
Brown University RELABBrown University RELAB
The public domain spectral library is supported by NASA at Brown University. The public domain spectral library is supported by NASA at Brown University. WWW.PLANETARY.BROWN.EDU/RELAB/. It contains thousands of spectra from NASA researchers and WWW.PLANETARY.BROWN.EDU/RELAB/. It contains thousands of spectra from NASA researchers and
othersothers. . Data cannot be used for commercial applications. It contains many project specific data sets Data cannot be used for commercial applications. It contains many project specific data sets including planetary projects.including planetary projects.
Arizona State UniversityArizona State UniversityMINESpectra is a data management program that interfaces with the USGS, JHU and special purpose MINESpectra is a data management program that interfaces with the USGS, JHU and special purpose libraries. It is free. www.geologynet.com/minspectra.htmlibraries. It is free. www.geologynet.com/minspectra.htm
Cal Tech VISCal Tech VISThis extensive library concentrates on the VIS range . There does not appear to be an identification This extensive library concentrates on the VIS range . There does not appear to be an identification program. It does not say if this is digital or not. program. It does not say if this is digital or not. Minerals.gps.caltech.edu\index.html Minerals.gps.caltech.edu\index.html
Johns HopkinsJohns HopkinsFTIR , SWIR Incorporated by JPL into ASTER library. Available from JPLFTIR , SWIR Incorporated by JPL into ASTER library. Available from JPL
KNOWN VIS-SWIR LIBRARIESKNOWN VIS-SWIR LIBRARIES
JPLThe spectral library available from the Jet Propulsion laboratory contains 160 mineral spectra in 3 different grain sizes. The references are well characterized. This library was incorporated into the “ASTER” Library along with the Johns Hopkins mid-infrared library growing to 2400 spectra of different infrared materials. It is available from JPL. Speclib.JPL.nasa.gov/documents/jpl_desc
MINEO (www2.brgm.fr/mineo/spectral.htm)MINEO (www2.brgm.fr/mineo/spectral.htm)
All spectra collected during field campaigns, lab analysis and image analysis are gathered into a single spectral library. This MINEO specific spectral library will constitute a European scale spectral data base for mining related contaminated areas. It is able to manage large amounts of spectra collected from laboratory analysis, field spectrometry, as well as spectra extracted from hyperspectral imagery.
KNOWN VIS-SWIR LIBRARIESKNOWN VIS-SWIR LIBRARIES
THE MINEO PROJECTTHE MINEO PROJECT∗Chevrel S., BRGM, Orléans – France, Kuosmannen V., GTK, Espoo – Finland; Belocky R., GBA, Wien – Austria; Marsh S., BGS, Nottingham, United Kingdom; Tukiainen T., GEUS, Copenhagen – Denmark; Mollat H., BGR, Hanover – Germany; Quental L., IGM, Lisbon – Portugal; Vosen P., DSK, Bottrop – Germany, Schumacher V., JRC/SAI, Ispra – Italy,Kuronen E., Mondo Minerals, Kajaani – Finland, and Aastrup P., NERI, Copenhagen – Denmark
ABSTRACTABSTRACT
MINEO is a European Research and Technological Development project which aims at developing tools and methods for assessing and monitoring the environmental impact of mining activities by means of combined Earth Observation and other relevant environmental data set. MINEO is designed to improve the already proven hyperspectral imagery capabilities in mineral mapping for use in the mapping of mining-related contaminated areas in European vegetated environments.
Generation of an European scale spectral library of contaminated areas
MINE WASTE LIBRARYMINE WASTE LIBRARY
APPLICATIONSAPPLICATIONS
AMDAMD
SPECIAL-PURPOSE LIBRARIES SPECIAL-PURPOSE LIBRARIES vs. ONE MAIN LIBRARYvs. ONE MAIN LIBRARY
BY ALTERATION TYPEBY ALTERATION TYPE
BY SPECIFIC DEPOSIT TYPEBY SPECIFIC DEPOSIT TYPE
SITE- SPECIFICSITE- SPECIFIC
USGS
GOLD EPITHERMALGOLD EPITHERMAL
HSS, LSSHSS, LSS
GOLD OROGENIC, VEINGOLD OROGENIC, VEIN
PORPHYRYPORPHYRY
IRON MINERALSIRON MINERALS
IOCGIOCG
URANIUM - UNCONFORMITYURANIUM - UNCONFORMITY
SKARNSSKARNS
REEREE
EXISITING DEPOSIT-SPECIFIC EXISITING DEPOSIT-SPECIFIC LIBRARIESLIBRARIES
SPECIAL-PURPOSE LIBRARIES: SPECIAL-PURPOSE LIBRARIES: ALTERATIONALTERATION TYPESTYPES
PROPYLLITIC - ZEOLITICPROPYLLITIC - ZEOLITIC
ARGILLICARGILLIC
INTERMEDIATE ARGILLICINTERMEDIATE ARGILLIC
ADVANCED ARGILLICADVANCED ARGILLIC
SILICICSILICICADVANCED ARGILLICADVANCED ARGILLIC
POTASSICPOTASSIC
PHYLLICPHYLLIC
IRON OXIDESIRON OXIDES
SKARNSSKARNS
SERICITIC-CHLORITESERICITIC-CHLORITE
QSP (QUARTZ-SERICITE-QSP (QUARTZ-SERICITE-PYRITEPYRITE))
CARBONATECARBONATE
TOURMALINETOURMALINE
ADVANTAGES OF SPECIAL PURPOSE LIBRARIES
FEWER WRONG CHOICES = MORE ACCURATE MATCHESFEWER WRONG CHOICES = MORE ACCURATE MATCHES
Example: Example: Porphyry Deposit – Chile – Lithocap ZonePorphyry Deposit – Chile – Lithocap Zone
50 Random samples selected through mineralized zone50 Random samples selected through mineralized zone
Samples run against:Samples run against: SPECMIN Database – SPECMIN Database – SII library SII library 1536 reference samples1536 reference samples
Deposit Library Deposit Library – 557 samples from porphyry– 557 samples from porphyry
Environments + selected deposit referencesEnvironments + selected deposit references
TSG (TSA) DatabaseTSG (TSA) Database
Mineral suite seen through zone, alunite, dickite, kaolinite, smectite, illite, muscovite, Mineral suite seen through zone, alunite, dickite, kaolinite, smectite, illite, muscovite, chlorite, epidote, gypsum, silicification, goethite, hemitite, jarositechlorite, epidote, gypsum, silicification, goethite, hemitite, jarosite
Procedure:Procedure:
11 - - Samples run against large SII librarySamples run against large SII library
2 – Samples run against a location-specific porphyry library2 – Samples run against a location-specific porphyry library
Results:Results:
SII vs. Porphyry librarySII vs. Porphyry libraryResults between SII vs. porphyry database identified 14% of Results between SII vs. porphyry database identified 14% of first order minerals were misidentified with SII Main libraryfirst order minerals were misidentified with SII Main library
Minerals causing largest issue were smectite-illite-muscovite Minerals causing largest issue were smectite-illite-muscovite presence.presence.
Results:Results:
Porphyry Library vs. TSGPorphyry Library vs. TSG
22% of first order minerals misidentified with TSG22% of first order minerals misidentified with TSG
5% of first order minerals should have been identified as 5% of first order minerals should have been identified as second order presencesecond order presence
34% of second order minerals were identified as NULL in TSG 34% of second order minerals were identified as NULL in TSG (mineral presence did exist in 90%)(mineral presence did exist in 90%)
37.5% of remaining second order minerals were misidentified37.5% of remaining second order minerals were misidentified
Only 23% of second order minerals correctly identifiedOnly 23% of second order minerals correctly identified
Example: Porphyry Cu-Au DepositExample: Porphyry Cu-Au Deposit
Mineral ID Using General LibraryMineral ID Using General Library
SpecMIN - FeatureSearch Top matches not relevant
Mineral ID Using Deposit-Specific Library
Top matches correct! SpecMIN - FeatureSearch
Example: Porphyry Cu-Au DepositExample: Porphyry Cu-Au Deposit
Mineral ID Using General Library
SpecMIN - FeatureSearch Top matches not relevant
Example: Porphyry Cu-Au DepositExample: Porphyry Cu-Au Deposit
Top matches correct! SpecMIN - FeatureSearch
Mineral ID Using Deposit-Specific Library
Example: Porphyry Cu-Au DepositExample: Porphyry Cu-Au Deposit
SPECIAL PURPOSE LIBRARIES WILL SPECIAL PURPOSE LIBRARIES WILL PROVIDE BETTER MATCHING STATISTICSPROVIDE BETTER MATCHING STATISTICS
THEY ELIMINATE WRONG CHOICESTHEY ELIMINATE WRONG CHOICES
The Spectral Geologist (CSIRO, Australia) The Spectral Geologist (CSIRO, Australia)
The Corescan Interpretation Software (CoreScan) The Corescan Interpretation Software (CoreScan)
FeatureSearch (Steve Mackin, specMIN) FeatureSearch (Steve Mackin, specMIN)
SpecPR (USGS) - SpecPR (USGS) - NOT ID PROGRAMNOT ID PROGRAM
The original hyperspectral ID program - The original hyperspectral ID program - NOT AN ID PROGRAMNOT AN ID PROGRAM(Neil Pendock, Phil Harris, Paul Linton, Anglo American-DeBeers)(Neil Pendock, Phil Harris, Paul Linton, Anglo American-DeBeers)
Newmont – Dave Coulter - Newmont – Dave Coulter - NO LONGER USEDNO LONGER USED
Rio Tinto - Alistair LAMB - Rio Tinto - Alistair LAMB - NO LONGER USEDNO LONGER USED
MINEO – French Consortium - MINEO – French Consortium - PROJECT ENDEDPROJECT ENDED
BHP - PROJECT STOPPED WHEN PIMA APPEAREDBHP - PROJECT STOPPED WHEN PIMA APPEARED
““AUTOMATED” MINERAL ID ALGORITHMSAUTOMATED” MINERAL ID ALGORITHMS
Wavelength-basedWavelength-based
Least squaresLeast squares
Profile basedProfile based
Linear regressionLinear regression
Neural netsNeural netsNon-linear regressionNon-linear regression
““AUTOMATED” MINERAL ID ALGORITHMS:AUTOMATED” MINERAL ID ALGORITHMS:
TYPES of PROGRAMSTYPES of PROGRAMS
SHAPE BASED SHAPE BASED – – Feature PositionFeature Position
simple lookup tablesimple lookup table
example: Feature Search; Tetracorder (USGS)example: Feature Search; Tetracorder (USGS)
SHAPE MATCHINGSHAPE MATCHING
Pearson correlation MatrixPearson correlation Matrix
Matched filteringMatched filtering
VECTOR SPACE ALGORITHMSVECTOR SPACE ALGORITHMS
remote sensing classification methodremote sensing classification method
TSG/TSA appears to use this type of algorithmTSG/TSA appears to use this type of algorithm
““AUTOMATED” MINERAL ID ALGORITHMS:AUTOMATED” MINERAL ID ALGORITHMS:
TYPES of PROGRAMSTYPES of PROGRAMS
The Spectral Geologist (CSIRO, Australia)The Spectral Geologist (CSIRO, Australia)
Specialist processing Specialist processing and analysis software and analysis software package designed for package designed for
analysis of field or analysis of field or laboratory laboratory
spectrometer data.spectrometer data.
It is automatedIt is automated
It uses a spectral It uses a spectral library developed by library developed by
CSIROCSIRO
The Corescan The Corescan Interpretation Software Interpretation Software
(CoreScan)(CoreScan) Corescan is a global services Corescan is a global services company specialising in the company specialising in the hyperspectral scanning, hyperspectral scanning, processing and analysis of drill processing and analysis of drill core, rock chips and other core, rock chips and other geological samples for the geological samples for the mining, oil and gas, and mining, oil and gas, and geotechnical industriesgeotechnical industries.
FeatureSearch (Steve Mackin, specMIN)FeatureSearch (Steve Mackin, specMIN)
FeatureSearch is a semi-automatic mineral identification package for determining mineralogy based on features observed in an "unknown"
spectrum collected by a field spectrometer.
Ideal for novice users with little experience in spectral identification or for advanced users trying to determine low proportion end-members in mixtures.
The software is spectrometer-independent and operates with data from specTERRA™, ASD, GER, SEI or PIMA spectrometers.
The users can select a spectral library created with any spectrometer.
Drag and drop a file or select a file from Plot Preview, click on the "Search Library" button and the chosen mineral library is searched in less than a
second. The results are displayed clearly to allow the user to extract and save the information of interest in History Libraries.
Use the extracted end-members to build a deposit or environment specific library to use in an automatic mineral identification algorithm such as SIMIS
Field 2.9, or save the results to directly import into Microsoft Excel.
SPECMIN incorporates options from the FeatureSearch program, which allows it to unmix spectral components, do mineral percentages, and access user-created custom libraries.
SPECMIN is a data management system that puts spectral data into an easy access format. It provides numerous spectral libraries including ASD, PIMA, USGS, and JPL mineral libraries. It contains spectra from hyperspectral imagery such as AVIRIS and SFSI. SPECMIN also contains libraries for soils, and libraries specific to precious metals deposit types.
SPECMINSPECMIN
Example: Mineral Spectral Analysis SoftwareExample: Mineral Spectral Analysis Software
Spectra Kaolinite Illite Dolomite Dickite Phlogopite Epidote Calcite Alunite Pyrophyllite Chlorite
sample 1 61.3 11.4 0.1 13 0.1 0.1 0.1 13.1 0.5 0.1
sample 2 39.7 59.3 0.1 0.1 0.1 0.1 0.4 0.1 0.1 0.1
sample 3 41.4 21.9 0.1 3.4 0.1 16.5 0.1 0.1 0.1 16.5
sample 4 3.9 1 17.7 0.1 30.3 3.4 0.1 0.1 0.1 43.2
sample 5 25.1 22.6 6 0.1 3.2 7.3 7.8 0.1 0.1 27.6
sample 6 0.2 0.2 98.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
sample 7 38.4 16.5 0.1 5.6 0.1 11.4 0.1 0.1 0.1 27.7
Total Sums Total Sums to 100%to 100%
Potential to Potential to change mineral change mineral
list?list?
Potential to be effective in localized environments!
WHY SO FEW???WHY SO FEW???
IT IS HARDIT IS HARD
CHALLENGES FOR AUTOMATED CHALLENGES FOR AUTOMATED MINERAL ID PROGRAMSMINERAL ID PROGRAMS
CHEMICAL VARIABILITY OF MINERALSCHEMICAL VARIABILITY OF MINERALS
ABSORPTION CO-EFFICIENTS ARE UNKNOWNABSORPTION CO-EFFICIENTS ARE UNKNOWN
NON-LINEAR ASSOCIATIONSNON-LINEAR ASSOCIATIONS
MIXTURESMIXTURES
USER INEXPERIENCEUSER INEXPERIENCE
Ex. Absorption Ex. Absorption CoefficientsCoefficients
Fe-Chlorite & illte
In A high (illite) and low (chlorite) reflectance mixture, In A high (illite) and low (chlorite) reflectance mixture, the low reflector is difficult to see. In this example, the low reflector is difficult to see. In this example, there has to be nearly 40% Fe-chlorite present before there has to be nearly 40% Fe-chlorite present before it can be detectedit can be detected
OBJECTIVES: ROUND ROBIN TEST STUDYOBJECTIVES: ROUND ROBIN TEST STUDY
SURVEY OF AUTOMATED MINERAL ID PROGRAMSSURVEY OF AUTOMATED MINERAL ID PROGRAMS
•S:N.S:N.
•Quality of spectrum accepted Quality of spectrum accepted
•What the algorithms are prejudiced towards What the algorithms are prejudiced towards
•Where they do not do wellWhere they do not do well
•Poor libraries?Poor libraries?
•Special purpose data librariesSpecial purpose data libraries
•Artifacts of the spectrumArtifacts of the spectrum
•Mixtures Mixtures
•Experienced user required? Or “out of the box”?Experienced user required? Or “out of the box”?
ROUND ROBIN: SUMMARY OF PROCEDUREROUND ROBIN: SUMMARY OF PROCEDURE
• 48 spectra total (natural samples, computer-48 spectra total (natural samples, computer-generated mineral mixtures)generated mineral mixtures)
• specTERRA, TerraSpec, FieldSpec ProspecTERRA, TerraSpec, FieldSpec Pro
• Anonymous participantsAnonymous participants
• Samples were run through automated mineral ID Samples were run through automated mineral ID programs of participants’ choiceprograms of participants’ choice
• The entries were scoredThe entries were scored
• A winner determined based on:A winner determined based on:
• Greatest number of correct minerals identifiedGreatest number of correct minerals identified
• Penalized for wrong answersPenalized for wrong answers
actinolite illitealunite-Na jarositealunite-K kaoliniteapophyllite lepidoliteberyl M.L. I\Sbiotite sheridanitebuddingtonite monazite REEcalcite?] montmorilllonitecerite REE muscovitechlorite natroliteChondrodite nephelineclinohumite opaldiaspore phlogopitedickite prehnitediopside pyrophyllitedolomite saponitedravite Scapoliteenstatite shorl dumortierite synchysite REEFe-chlorite szmolokiteelbaite topazgoethite tremolitehornblende
MINERALS IN THE STUDYMINERALS IN THE STUDY
RR kaol dik alun PYROdias TOPAZDUMOwm ill mont ML sap SCA sil dolo CAL chl AMP PYX tour BIO diop apop preh pumpREE hem goe ?
RR02 x x x x xRR03 x x xRR05 x x xRR06 x? xRR07 x x xRR08 x ? xRR10 x x ??RR11 x x xRR12 x x x xRR13 x op x xRR14 x x xRR17 x x xRR18 x x x xRR21 x x x x x xRR22
RR23 x x x xRR26RR27 X XRR28 X X X XRR29 X X XRR30 X X XRR31 X XRR35 X XRR37 X XRR38 X XRR40 X X XRR41 X XRR43 X XRR45 XRR46 X X X XRR47 X X XRR48 X X XRR49 X XRR54RR55 X XRR56 X X XRR57 X XRR60 X X XRR61 X X XRR66 SZMRR67 OP?RR68 X X XRR69 X X ? PHLGRR70 X X X XRR72 X XRR75 X X X X XRR76 X ZEORR84 X X X XRR85 X XRR86 X X XRR87 XRR88 X X XRR89 X XRR90 X X XRR91 X X NH4RR92 X BERYLRR96 X X X XRR98 X JARO
MINERALS IN THE ROUND ROBINMINERALS IN THE ROUND ROBINKEYKaoliniteKaolinite
DickiteDickite
AluniteAlunite
pyrophyllitepyrophyllite
DiasporeDiaspore
TopazTopaz
Dumortierite Dumortierite
Xxxxxxxxxxxxx wmXxxxxxxxxxxxx wm
Illite Illite
SmectiteSmectite
Mixed Layer I/SMixed Layer I/S
SaponiteSaponite
Scapolite Scapolite
Silica Silica
Dolomite Dolomite
Calcite Calcite
Chlorite Chlorite
Amphibole Amphibole
Pyroxene Pyroxene
Tourmaline Tourmaline
Biotite Biotite
Diopside Diopside
Apophylite Apophylite
Prehnite Prehnite
Pumpellyite Pumpellyite
REEREE
HematiteHematite
Goethite Goethite
ROUND ROBIN SPECTRA: EXAMPLESROUND ROBIN SPECTRA: EXAMPLES
Monazite
Szmolnokite
Elbiate + Lepidolite
Dickite + alunite + pyrophyllite
ROUND ROBIN: ROUND ROBIN: MINERAL ID PROGRAMS ENTEREDMINERAL ID PROGRAMS ENTERED
•GRAMSGRAMS
•TSG (several versions)TSG (several versions)
•FEATURE SEARCHFEATURE SEARCH
•TNT-MIPSTNT-MIPS
•IN-HOUSE C+IN-HOUSE C+
•MSA (MINERAL SPECTRAL ANALYSIS)MSA (MINERAL SPECTRAL ANALYSIS)
Program Strengths Weaknesses
The Spectral Geologist (TSG) – TSA (The Spectral Assistant)
Up to 5 minerals potentially identified
Minor components of mixtures poorly identified
TNT MIPS • Good attempt at mixtures – up to 5 phases
• Fe-phases identified
Second and third order phases of mixtures not well identified
MSA – Mineral Spectral Analyst
Mineral ‘matrix’ could work very well for major components
No measure of reliability – will always fit spectra to given minerals
“In-house” C Attempt to determine VNIR phases
Matches only 1 spectra
Summary of Programs Submitted to Round Robin: Strengths and Weaknesses
Participant # Program Version Data Base Country
8011 TNT MIPS PRO-3.1 SPECMIN Turkey
8014 TSG 7.1 TSG Australia
8015 GRAMS Specmin USA
8016-1 FS SPECMIN Argentina
8017 TSG unspecified USA
8018 in-house C Specmin USA ENVi format
8021 TSG 7.1 3.0 Australia
8022 MSA Mineral Spectral Analysis V3.6 China
8024 TSG 5.03 IN TSG Netherlands
8025 TSG 7.1 NG TSG? Australia
8030 TSG2
answers in TSG Chile
8035 proprietary 1 specmin USA
ROUND ROBIN: Participants and winner ROUND ROBIN: Participants and winner
OBSERVATIONS & COMMENTSOBSERVATIONS & COMMENTS
BIGGEST OBSERVATIONBIGGEST OBSERVATION
not a lot of progress made over the last 10 yearsnot a lot of progress made over the last 10 years
TSG CAN GENERATE HIGHLY VARIABLE RESULTSTSG CAN GENERATE HIGHLY VARIABLE RESULTS
Specialized libraries definitely improve matching statisticsSpecialized libraries definitely improve matching statistics
Library must be comprehensive to provide accurate answersLibrary must be comprehensive to provide accurate answers
The less complicated the procedure, the more accurate will be the The less complicated the procedure, the more accurate will be the resultsresults
Less choices, better results – i.e. special purpose libraries provide Less choices, better results – i.e. special purpose libraries provide the best answersthe best answers
Computer program:
The Spectral Geologist v. 5.03
Database: The Spectral Assistant (TSA)
Dr. F.J.A. (Frank) van RuitenbeekDr. F.J.A. (Frank) van RuitenbeekUniversity of Twente, Netherlands
PRIZE
IF USER DOES NOT KNOW IF USER DOES NOT KNOW
WHAT ANSWERS ARE WRONG, WHAT ANSWERS ARE WRONG,
HOW CAN ANSWERS FROM HOW CAN ANSWERS FROM
AUTOMATED PROGRAMS BE AUTOMATED PROGRAMS BE
EVALUATED??EVALUATED??