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  3. 3. INTRODUCTION • Mineral exploration is the process of finding ores (commercially viable concentrations of minerals) ex: coal, quartz, feldspar, mica, oxides of iron, precious metals.ect… • The risk of developing mineral resources need to be known as accurately as possible. • Because, it influenced the cost of the project due to wrong estimation of presence of minerals and their quality, quantity, area of extent of the mineral resource.etc…, • Over the past decade, the mining industries used remote sensing imagery data for mineral explorations. • Satellite multi-spectral systems offer consistent image data sets that provide a wealth of geological and logistical information, especially for poorly mapped and remote locations. 3
  4. 4. REMOTE SENSING APPLICATIONS IN MINERAL EXPLORATION • It includes three broad categories 1. structural/geomorphic interpretation: estimating strike/dip, drainage analysis, identifying folds & faults, predicting geologic hazards. 2. Compositional mapping: rock/soil type prediction, alteration mineral modeling, vegetation-stress mapping, environmental monitoring. 3. Logistical information: accurate map preparation, integration with GPS & GIS, real time mapping with palm-pc computers and generation of digital elevation models(DEM). 4
  5. 5. ASTER IMAGES FOR MINERAL EXPLORATION • The Advanced Space born Thermal Emission and Reflection Radiometer(ASTER) is a NASA instrument on the earth observing system(EOS) Terra platform. • It provides visible & near infrared(VNIR), shortwave-infrared (SWIR), long-wave infrared(LWIR) Earth observations in 14 spectral bands (plus one backward looking band). 5 Table-1: Characteristics of ASTER
  6. 6. • 4 VNIR Bands: provide information about Iron mineralogy and Rare earth minerals(fluoride, apatite, titanite, ect..,). • 6 SWIR Bands: mapping of molecular vibration absorption features. Ex:- carbonates and clays. • 5 LWIR Bands: mapping of silica content in surface exposures • ASTER has 95% coverage of earth’s landmass, so ASTER images are a readily available data set that can provide timely and accurately positioned spatial information to facilitate fast mining activities around the world. 6
  7. 7. • The basic tool for geologists in all aspects is a map representing the distribution and identify of rock units exposed at the earth’s surface. • Economic geologists- search for metal & petroleum deposits. • Hydro-geologists - look for ground water. • Structural geologists - classify faults as active or inactive. • ASTER provides data that will greatly improve geologists abilities to produce more accurate geologic maps at a fraction of the cost of conventional ground-based methods.  ASTER data have characteristics that are particularly useful for geologic studies, especially where the rocks are well exposed. 7
  8. 8. HOW GIS USED • GIS can help in many aspects of the mineral exploration activities: data collection, management, analysis, and reporting. • Field geologists capture field data electronically using ArcPad and GPS receivers. • Data sources for mineral exploration vary from geologic maps, multispectral satellite images, and geophysical images to databases in many formats. • GIS is an ideal platform to integrate the all above data sources together and deliver meaningful outcomes. 8
  9. 9. LITERATURE REVIEW 1. B.K.Bhadra et.al, (2012) explained how SWIR bands are used in identification of alteration zones which have developed during hydrothermal activity. 2. Bob Agar (2005) discussed on processing of ASTER level 1B images for radiance at the sensor & locating different minerals by developing algorithms and compared with reference spectra are taken from the USGS spectral library. 3. Carlos A. Torres (2007), identified mineral deposits in area targeted by preparing a Geodatabase. 9
  10. 10. CASE STUDY:1 (Carlos A. Torres, 2007) • OBJECTIVE: Exploration of gold mineral using GIS and ASTER images • STUDY AREA: north eastern part of the sate of Nevada, United States. • DATA USED: – Geo-database containing State & County Boundaries, Existing Mines, Topography ,Geologic data, DEM’s – ASTER Level-1B images 10
  11. 11. METHODOLOGY: • PROCESSING OF ASTER DATA: Corrections applied on – Geometric and radiometric errors – parallax errors for SWIR bands 11 Fig1:ASTER images in VNIR & SWIR bands
  12. 12. • DESIGN OF GEO-DATABASE: the raw data sets were put through the different analysis tools in Arc Toolbox to get only the data needed.  Bring the different datasets within the geodatabase along with processed raster images.  database contains lithological & magnetic field data, information about current mines, maps and ASTER images, geophysical images, other datasets. 12Lithologic contacts, magnetic field, Au >= 10ppb
  13. 13. • SPECTRAL LIBRARY: creating a spectral library from the USGS digital spectral library. The purpose for this is to create a spectral library with known mineral in the target area and then compare the spectral library with the image and find possible sites for further investigations • MINIMUM NOISE FRACTION: – used to show the variation b/w bands in an image. – Is a statistical method which works out in an image based on pixel DN’s in various bands – the noise is separated from the data based on the principal component analysis. 13
  14. 14. • SPATIAL CORRELATION: – Done by using IDW (a deterministic interpolator) – Used to create a spatial correlation between the samples taken and the gold content of the samples taken IDW – Exact deterministic interpolator 14
  15. 15. 15 • DEFINING REGION OF INTEREST: – created using a shape file from Arc GIS with specific points where gold has been identified so that we can perform a supervised classification. RESULTS - Prepared a geodatabase with all available information. - Created a websites in ArcIMS (Arc Internet Map Server) and customized using VBScripts. - For each sample point created a link to the location on Google Earth so the person looking at the data can have a better understanding of the data. - The result was an ArcIMS tool that can help in mineral exploration to make smart decisions about mineral exploration.
  16. 16. 16 Fig: showing interface from ASTER image to Google Earth image
  17. 17. • OBJECTIVE : identification of alteration zones which have developed during hydrothermal activity using ASTER SWIR band data. • STUDY AREA: The study area lies around Sawar-Malpura area in parts of Ajmer and Tonk districts of Rajasthan. 17 CASE STUDY 2: ( B.K.Bhadra, 2012)
  18. 18. • DATA USED: - Satellite images of ASTER SWIR bands. - GIS layers have been created for lithology, structures, geomorphology, soil and base map information by image interpretation from ASTER images. 18 ASTER Image (May, 2007) Geological Map Geological map of Sawar-Bajta area with location of base metal concentration (>500 ppm).
  19. 19. GEOLOGY OF THE STUDY AREA: • SAWAR area: sawar metasedimentary rocks(marble,mica, schist, quartz reef & silicified quartzite) from the proterozoic cover over the basement gneissic rocks • The sawar area comprised of marble and schist rock exposures with known deposits of Pb-Zn-Cu minerals. • MALPURA area: malpura area is mostly covered with soil with numerous tiny exposures of granite gneiss which is present as basement rock. • Sulphide mineral deposits such as Pb-Zn-Cu have very complex alteration systems containing several types and combination of alteration suits. 19
  20. 20. METHODOLOGY  ASTER level 1B images are corrected both geometrically and radiomerically. • Principal Component Analysis(PCA ) technique: - statistical technique. - selects uncorrelated spectral bands from highly correlated ASTER bands. - pc bands are linear combination of the original spectral bands. - 1st PC band is have highest data variance, 2nd PC band is having 2nd highest data variance..so on. Thus the last PC band appear noise. • For propylitic minerals, the spectral pattern shows good absorption in B7 & B8, high reflection in B5 & B6. • For phyllic minerals, the spectral pattern shows good absorption in B6, high reflection in B5 & B7. • B6 is unique band for discrimination of phyllic and propylitic zones. 20
  21. 21. • Relative absorption-band depth (RBD) ratio technique: - it involves band arithmetic calculations. - simple operation with division of two bands of highest reflectance and highest absorption of the same feature. - The RBD ratio images to distinguish Al-O-H, Mg-O-H and CO3 are limestone (caco3 ) - (B7 + B9)/B8 dolomite(ca,mg-co3) - (B6 + B8)/B7 muscovite(Al-O-H) - (B5 + B7)/B6 lematite/goethite(Fe+3) - B2/B1. 21
  22. 22. • RESULTS: - SWIR bands (ASTER-6,4 and 8) are suitable in discriminating marble and quartz. - SWIR bands shows absorption features by the OH-bearing minerals. - PC band that has the highest eigen vector is important for discrimination of alteration zones such as propylitic and phyllic. - highest loading range for propylitic zone is PC5-(1.110) & phyllic zone is PC4- (0.932). 22Eigenvector Statistics for PC of ASTER SWIR Bands for Alteration Zones
  23. 23. SUMMARY • Mineral exploration & mineral potential mapping using the ASTER images gives satisfactory results when used alongwith other data sets. • Case study 1 shows the identification of target minerals using the GIS & ASTER images along with other data sets like geophysical images, information about current mines ect.., • Case study 2 shows that importance of ASTER SWIR band data in potential alteration mineral zones mapping. • Case studies reveals that the use of ASTER data in mineral exploration for preparation of geological mapping, alteration mineral zones ect.., 23
  24. 24. 24 References • Amin Beiranvnd Pour, Mazlan Hashim., & Maged Marghany (2011). “Using spectral mapping techniques on short wave infrared bands of ASTER remote sensing data for alteration mineral mapping in SE Iran”, International Journal of the Physical Sciences Vol. 6(4), pp. 917-929. • Azizi, H., Rsaouli, A. A., &Babaei, K. (2007). “Using SWIR Bands from ASTER for discrimination of Hydrothermal Altered Minerals in the Northwest of Iran”, Research Jour. of Applied Sciences, 2(6), 763– 768. • B. K. Bhadra, SuparnPathak ,G. Karunakar., & J. R. Sharma (2012). “ASTER Data Analysis for Mineral Potential Mapping AroundSawar-Malpura Area, Central Rajasthan”, Research jour. Of Indian Soc RemoteSens , 41(2):391–404. • Bob Agar (2005). “ASTER Alteration Mineral Mapping; Las Pampas, Cajamarca, Peru”, Research jour. Of Australian Geological & Remote Sensing Services, Vol. VI, pp 2501-2503. • Carlos A. Torres (2007). “Mineral Exploration Using GIS and Processed Aster Images”, Research jour. of Advance GIS EES 6513 (Spring 2007) University of Texas at San Antonio.
  25. 25. 25 • Enton Bedini (2011). “Mineral mapping in the Kap Simpson complex, central East Greenland, using ASTER remote sensing data”. Research jour. Of Advances in Space Research 47 (2011) 60–73. • Mutasim Sami Osman (2012). “Mineral Exploration Using GIS”, Research jour. Of King Fahad University of Petroleum and Minerals. • R. Greg Vaughan, Simon J. Hook, Wendy M. Calvin, & James V. Taranik (2005). “Surface mineral mapping at Steamboat Springs, Nevada, USA, with multi-wavelength thermal infrared images”, Research jour. Of Remote Sensing of Environment 99 (2005) 140–158. • Xianfeng Zhang, Micha Pazner, Norman Duke (2007). “Lithologic and mineral information extraction for gold exploration using ASTER data in the south Chocolate Mountains (California)”, ISPRS Journal of Photogrammetry & Remote Sensing 62 (2007) 271–282. • Y. Yamaguchi & C. Naito (2003). “Spectral indices for lithologic discrimination and mapping by using the ASTER SWIR bands”, International Journal of Remote Sensing, 24:22, 4311- 4323.
  26. 26. THANK YOU 26