LIBRARIES AND ALGORITHMS : Automated Interpretation of VIS-SWIR Data and

1. LIBRARIES AND ALGORITHMS: Automated Interpretation of VIS-SWIR Data and the Advantage of Special Purpose Reference Databases Phoebe L. Hauff Cari Deyell-Wurst William Kerby PRIZE

2. INTRODUCTION “Spectral geology” applications (including satellite, airborne, core scanning and field measurements) have become very common due to significant technological advancements and improved instrumentation. 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)

3. OVERVIEW • REVIEW OF SPECTRAL LIBARIES • REVIEW OF ID ALGORITHMS • INTERACTION OF BOTH • ROUND ROBIN RESULTS

4. SPECTRAL LIBRARIES • Spectral libraries contain reference spectra, which are compared against an unknown spectrum using computer automated ID techniques. • Without them, it would be difficult to do interpretation Minerals are highly variable – composition, wavelength, profile, crystallinity Difficult to automate CRYSTALLINITY Data Bases contain spectra, ancillary information, physical properties, references, associated species, location. CHLORITES

5. KNOWN VIS-SWIR LIBRARIES 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 the common ones are included and will be on the SII website. SpecMIN: 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 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 within mineral species. In addition to mining applications, SPECMIN can also be used in remote sensing applications for ground truthing. www.spectral-international.com CSIRO: 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 potentially contribute to your project spectra.www.csiro USGS: 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 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. http://speclab.cr.usgs.gov/spectral.lib06

6. KNOWN VIS-SWIR LIBRARIES Brown University RELAB 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 others. Data cannot be used for commercial applications. It contains many project specific data sets including planetary projects. Arizona State University MINESpectra is a data management program that interfaces with the USGS, JHU and special purpose libraries. It is free. www.geologynet.com/minspectra.htm Cal Tech VIS 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. Minerals.gps.caltech.edu\index.html Johns Hopkins FTIR , SWIR Incorporated by JPL into ASTER library. Available from JPL

7. KNOWN VIS-SWIR LIBRARIES JPL The 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) 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.

8. THE 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 ABSTRACT 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 LIBRARY

9. APPLICATIONS AMD

10. SPECIAL-PURPOSE LIBRARIES vs. ONE MAIN LIBRARY BY ALTERATION TYPE BY SPECIFIC DEPOSIT TYPE SITE- SPECIFIC USGS

11. EXISITING DEPOSIT-SPECIFIC LIBRARIES GOLD EPITHERMAL HSS, LSS GOLD OROGENIC, VEIN PORPHYRY IRON MINERALS IOCG URANIUM - UNCONFORMITY SKARNS REE

12. SPECIAL-PURPOSE LIBRARIES: ALTERATIONTYPES PROPYLLITIC - ZEOLITIC ARGILLIC INTERMEDIATE ARGILLIC ADVANCED ARGILLIC SILICIC SERICITIC-CHLORITE TOURMALINE SKARNS ADVANCED ARGILLIC POTASSIC PHYLLIC IRON OXIDES CARBONATE QSP (QUARTZ-SERICITE-PYRITE)

13. ADVANTAGES OF SPECIAL PURPOSE LIBRARIES FEWER WRONG CHOICES = MORE ACCURATE MATCHES Example: Porphyry Deposit – Chile – Lithocap Zone 50 Random samples selected through mineralized zone Samples run against: SPECMIN Database – SII library 1536 reference samples Deposit Library – 557 samples from porphyry Environments + selected deposit references TSG (TSA) Database Mineral suite seen through zone, alunite, dickite, kaolinite, smectite, illite, muscovite, chlorite, epidote, gypsum, silicification, goethite, hemitite, jarosite

14. Procedure: 1 - Samples run against large SII library 2 – Samples run against a location-specific porphyry library Results: SII vs. Porphyry library Results between SII vs. porphyry database identified 14% of first order minerals were misidentified with SII Main library Minerals causing largest issue were smectite-illite-muscovite presence.

15. Results: Porphyry Library vs. TSG 22% of first order minerals misidentified with TSG 5% of first order minerals should have been identified as second order presence 34% of second order minerals were identified as NULL in TSG (mineral presence did exist in 90%) 37.5% of remaining second order minerals were misidentified Only 23% of second order minerals correctly identified

16. Example: Porphyry Cu-Au Deposit Mineral ID Using General Library SpecMIN - FeatureSearch Top matches not relevant

17. Example: Porphyry Cu-Au Deposit Mineral ID Using Deposit-Specific Library SpecMIN - FeatureSearch Top matches correct!

18. Example: Porphyry Cu-Au Deposit Mineral ID Using General Library SpecMIN - FeatureSearch Top matches not relevant

19. Example: Porphyry Cu-Au Deposit Mineral ID Using Deposit-Specific Library SpecMIN - FeatureSearch Top matches correct!

20. SPECIAL PURPOSE LIBRARIES WILL PROVIDE BETTER MATCHING STATISTICS THEY ELIMINATE WRONG CHOICES

21. “AUTOMATED” MINERAL ID ALGORITHMS The Spectral Geologist (CSIRO, Australia) The Corescan Interpretation Software (CoreScan) FeatureSearch (Steve Mackin, specMIN) SpecPR (USGS) - NOT ID PROGRAM The original hyperspectral ID program - NOT AN ID PROGRAM (Neil Pendock, Phil Harris, Paul Linton, Anglo American-DeBeers) Newmont – Dave Coulter - NO LONGER USED Rio Tinto - Alistair LAMB - NO LONGER USED MINEO – French Consortium - PROJECT ENDED BHP - PROJECT STOPPED WHEN PIMA APPEARED

22. “AUTOMATED” MINERAL ID ALGORITHMS: TYPES of PROGRAMS Wavelength-based Least squares Profile based Linear regression Neural nets Non-linear regression

23. “AUTOMATED” MINERAL ID ALGORITHMS: TYPES of PROGRAMS SHAPE BASED – Feature Position simple lookup table example: Feature Search; Tetracorder (USGS) SHAPE MATCHING Pearson correlation Matrix Matched filtering VECTOR SPACE ALGORITHMS remote sensing classification method TSG/TSA appears to use this type of algorithm

24. The Spectral Geologist (CSIRO, Australia) Specialist processing and analysis software package designed for analysis of field or laboratory spectrometer data. It is automated It uses a spectral library developed by CSIRO

25. The Corescan Interpretation Software (CoreScan) Corescan is a global services company specialising in the hyperspectral scanning, processing and analysis of drill core, rock chips and other geological samples for the mining, oil and gas, and geotechnical industries. 

26. 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.

27. 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. SPECMIN SPECMIN incorporates options from the FeatureSearch program, which allows it to unmix spectral components, do mineral percentages, and access user-created custom libraries.

28. Example: Mineral Spectral Analysis Software Potential to change mineral list? Potential to be effective in localized environments! Total Sums to 100%

29. WHY SO FEW??? IT IS HARD

30. CHALLENGES FOR AUTOMATED MINERAL ID PROGRAMS CHEMICAL VARIABILITY OF MINERALS ABSORPTION CO-EFFICIENTS ARE UNKNOWN NON-LINEAR ASSOCIATIONS MIXTURES USER INEXPERIENCE

31. Ex. Absorption Coefficients In A high (illite) and low (chlorite) reflectance mixture, the low reflector is difficult to see. In this example, there has to be nearly 40% Fe-chlorite present before it can be detected Fe-Chlorite & illte

32. OBJECTIVES: ROUND ROBIN TEST STUDY • SURVEY OF AUTOMATED MINERAL ID PROGRAMS • S:N. • Quality of spectrum accepted • What the algorithms are prejudiced towards • Where they do not do well • Poor libraries? • Special purpose data libraries • Artifacts of the spectrum • Mixtures • Experienced user required? Or “out of the box”?

33. ROUND ROBIN: SUMMARY OF PROCEDURE • 48 spectra total (natural samples, computer-generated mineral mixtures) • specTERRA, TerraSpec, FieldSpec Pro • Anonymous participants • Samples were run through automated mineral ID programs of participants’ choice • The entries were scored • A winner determined based on: • Greatest number of correct minerals identified • Penalized for wrong answers

34. MINERALS IN THE STUDY

35. KEY Kaolinite Dickite Alunite pyrophyllite Diaspore Topaz Dumortierite Xxxxxxxxxxxxxwm Illite Smectite Mixed Layer I/S Saponite Scapolite Silica Dolomite Calcite Chlorite Amphibole Pyroxene Tourmaline Biotite Diopside Apophylite Prehnite Pumpellyite REE Hematite Goethite MINERALS IN THE ROUND ROBIN

36. ROUND ROBIN SPECTRA: EXAMPLES Szmolnokite Elbiate + Lepidolite Monazite Dickite + alunite + pyrophyllite

37. ROUND ROBIN: • MINERAL ID PROGRAMS ENTERED • GRAMS • TSG (several versions) • FEATURE SEARCH • TNT-MIPS • IN-HOUSE C+ • MSA (MINERAL SPECTRAL ANALYSIS)

38. Summary of Programs Submitted to Round Robin: Strengths and Weaknesses

39. ROUND ROBIN: Participants and winner

40. OBSERVATIONS & COMMENTS BIGGEST OBSERVATION not a lot of progress made over the last 10 years TSG CAN GENERATE HIGHLY VARIABLE RESULTS Specialized libraries definitely improve matching statistics Library must be comprehensive to provide accurate answers The less complicated the procedure, the more accurate will be the results Less choices, better results – i.e. special purpose libraries provide the best answers

41. ROUND ROBIN: RESULTS Computer program: The Spectral Geologist v. 5.03 Database: The Spectral Assistant (TSA) Dr. F.J.A. (Frank) van Ruitenbeek University of Twente, Netherlands PRIZE

42. IF USER DOES NOT KNOW WHAT ANSWERS ARE WRONG, HOW CAN ANSWERS FROM AUTOMATED PROGRAMS BE EVALUATED??