phoebe l. hauff cari deyell-wurst william kerby prize

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


Mineral ID Using General Library

SpecMIN - FeatureSearch Top matches not relevant


Top matches correct! SpecMIN - FeatureSearch

Mineral ID Using Deposit-Specific Library


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??