The document discusses using ASTER satellite imagery and GIS for mineral exploration. It provides details on two case studies: 1) identifying gold deposits in Nevada using ASTER data integrated into a geodatabase, and 2) mapping alteration zones in India using ASTER shortwave infrared bands. The case studies demonstrate how ASTER imagery can be processed and analyzed using GIS and techniques like principal component analysis to produce geological maps and identify target areas for further mineral exploration.

1. MINERAL EXPLORATION USING
GIS & ASTER IMAGES
PRAVEEN KUMAR.CH
131857
1

2. CONTENTS
• INTRODUCTION
• ASTER IMAGES FOR MINERAL EXPLORATION
• HOW GIS USED
• LITERATURE REVIEW
• METHODOLOGY WITH CASE STUDIES
• SUMMARY
• REFERENCES
2

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.
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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).
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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. • 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.
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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.
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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.
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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.
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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. 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. • 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. • 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.
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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
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• 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.

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Fig: showing interface from ASTER image to Google Earth image

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.
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CASE STUDY 2: ( B.K.Bhadra, 2012)

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.
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ASTER Image
(May, 2007)
Geological Map Geological map of Sawar-Bajta area with
location of base metal concentration (>500
ppm).

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.
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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.
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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.
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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. 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..,
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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
• 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. THANK YOU
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