How geoinformatics can be used to locate mineral resources and exploit them to their full potential.

1. The Use of Geoinformatics in Mineral
Exploration and Exploitation
Marguerite Walsh
MSc Geographical Information Systems and Remote Sensing
18th March 2015
Van der Meer, et al, 2014

2. Introduction
 Benefits geologists, scientists and
exploration managers
 Mineral exploration and exploitation is a
huge source of employment around the
world
 Main focus on remote sensing

3. History of Remote Sensing in
Geology
 Graham Hunt & John Salisbury
(1970s/1980s)
 Based on laboratory spectral analysis of
minerals and rocks
 Geologic Remote Sensing
 Mineral exploration
 Hyperspectral geology
 Mineral resource mapping
 Seismic activity
 Dr. F. van der Meer

4. Remote Sensing (1)
Advantages
 Classification for
mapping
 Target identification
 “bird’s eye view” – can cover large
areas quickly
 Can see any patterns or trends –
differences in tone, texture and
structure

5. Remote Sensing (2)
Issue
 Cloud cover
 Features on the
ground can be
hidden beneath
vegetation
 Sub surface
features
Solution
 Radar
 Radar
 Radio Echo
Sounding

6. Satellite sensors
1000s of options.
 Archive of data
 Temporal
resolution
 Orbit of satellite
 Spectral
resolution
 Spatial
resolution
 Cost

7. Spectral Signatures (1)
• Multiple bands that
show what the human
eye cannot see
• Visible, near infrared, short-
wave infrared and thermal
infrared
http://www.akitarescueoftulsa.com/label-the-electromagnetic-
wave-diagram/
“Many minerals have unique and diagnostic
spectral properties, and features such as the band
centre, strength, shape, and width are used to
identify species with high confidence”
(Calvin et al, 2015)

8. Spectral Signatures (2)
USGS Spectral Library
• Multispectral imaging and thematic
mapping
• Reflection data and absorption properties
• Photogeology
• USGS Spectral Library

9. “Spectrally Active” minerals can be
mapped with Remote Sensing
Environment of formation Main spectrally active alteration minerals
High sulphidation epithermal Alunite, pyrophyllite, dickite, kaolinite,
diaspore, zunyite, smectite, illite
Low sulphidation epithermal Sericite, illite, smectite, chlorite, cabonate
Porphyry: Cu, Cu-Au Biotite, anhydrite, chlorite, sericite,
pyrophyllite, zeolite, smectite, canbonate,
tourmaline
Carlin-type Illite, dickite, kaolinite
Volcanogenic massive sulphide Sericite, chlorite, chloritoid, carbonates,
anhydrite, gypsum, amphiobole
Archean Lode Gold Carbonate, talc, tremolite, muscovite,
paragonite
Calcic skarn Garnet, clinopyroxene, wollastonite, actinlite
Retrograde skarn Calcite, chlorite, hematite, illiteVan der Meer, et al, 2014.

10. Landsat (1)
 “Landsat represents the world's
longest continuously acquired
collection of space-based moderate-
resolution land remote sensing data. ”
(USGS, 2013)
 Operational 1972-present
U.S.G.S., 2014.

11. Landsat (2)
 Joint project of the U.S. Geological
Survey (USGS) and the National
Aeronautics and Space Administration
(NASA)
 Data every 16/18 days
 11 Bands
 Resolution 30-60m
 Free images
 Easily accessible
(U.S.G.S., 2012)

12. Case Study 1: USGS National
Map of Surficial Mineralogy
• Mapping exposed surface mineral groups
• 3 applications:
• undiscovered mineral deposits
• environmental effects associated with mining and
• unmined, hydrothermally-altered rocks
• Done using:
• 180 Landsat scenes and
• 1630 ASTER scenes

13. Case Study 1: USGS National
Map of Surficial Mineralogy
• Already done for the western
part of the US – extending
eastwards
• More detailed and accurate
mineral and vegetation maps
• Active & abandoned mining
districts
• Done using:
• ASTER
• (AVIRIS)
• HyMap or
• SpecTIR
• An algorithm was developed to
automatically analyse Landsat
8 imagery Rockwell, 2013

14. Case Study 1: USGS National
Map of Surficial Mineralogy
This data is available as GIS shapefiles to
add into ArcMap Rockwell, 2013

15. ASTER
 The “work horse” for geologic
Remote Sensing (van der Meer, 2014).
 Mapping of surface mineralogy
 ASTER band ratios as proxies
 14 different wavelengths

16. Spectral signatures of different minerals
shown through 9 ASTER spectral bands
(Beiranvand Pour & Hashim, 2012)
ASTER spectral
signatures
• ASTER has 5 thermal
bands – different
outcrops of minerals
can be identified due to
differences in specific
heat capacity
• Algorithms to extract
the spectral
information

18. Case Study 2: ASTER &
Detecting areas of high-
potential gold mineralization
 Hydrothermal alteration zones (gold
and copper)
 Methods: band ratio & mineral
extraction method
 Field mapping was also undertaken
 Gabr et al, 2010

19. Study Site:
Abu-Marawat, the Eastern Desert of
Egypt
• Abu Marawat Deposit is a gold rich,
polymetallic deposit
• Historical area of gold and copper
mining dating back to the time of
Pharaohs and Pyramids
Alexander Nubia Inc., 2011

20. Spectral Signatures
Gabr et al, 2010

21. Result:
ASTER band ratio image
The white colour
represents
mineralized parts of
the alteration zone –
potential for
significant,
undiscovered gold ore

22. Case Study 3: ASTER & Morenci
Mine, Arizona
 ASTER (15m) Satellite Image of Morenci Mine,
Arizona - USA
Satellite Imaging Corporation, 2001-2014.

23. ASTER Summary
 Issues of cloud cover and vegetation
 Each terrain is different and so
algorithms and ratios will vary
 Do not look at the ASTER data in
isolation

24. Integration with other
geoinformatics technologies
 GIS data layers
– to get a better
understanding of
the site
◦ Topographical
◦ Geophysical
◦ Geochemical data
 Adding layers on
transport, relief,
elevation etc
Some of the GIS data layers
used by the USGS in their
geological studies
http://woodshole.er.usgs.gov/project-
pages/longislandsound/data/gis.html

25. Case Study 4: GIS analyses and satellite data
in northern Chile to improve exploration for
copper mineral deposits
• La Escondida
mining District
• Atacama Desert,
Northern Chile
• The highest
producing copper
mine in the world.
• Also produces
some silver and
gold
La Escondida mine
(left) 1975 before extraction began
(right) 2008 with huge expansion
UNEP, CATHALAC., 2015.

26. Data integration and
analyses within a
geographic information
system
 Different thematic layers
of the database in the
vicinity of La Escondida
mining district.
 Upper layers represent
optimized Landsat data
derived from band
ratioing, principal
component analysis
(PCA), and inverse PCA.
 Lower layers represent
topographic data,
lithology, and
aeromagnetic data.
 Bottom layer is one of
the calculated
favourability maps.Ott et al., 2006.

27. The End Result
Favourability map of altered rocks at La Escondida mining district

28. Case Study 5: Geothermal
Resources in Nevada

29.  ASTER imagery
 Used both remote sensing and
geographical information systems
 Thermal properties as surface indicators
of geothermal resources
 Spectral data taken in the field using a
spectrometer to validate results
 Integration into GIS databases with other
relevant geologic information
“to make comparisons and site
assessments.”
 However blind geothermal systems may
have very little or no surface expression at

30. Thermal
Anomalies at the
Brady’s Site,
Fernley, Nevada.

31. The End Result
Mineral Map of 4 different areas
• Successful in Nevada
where there is sparse
vegetation cover
• In vegetated areas –
LiDAR may be more
appropriate
• UAVs with imaging
spectrometers will
also help map small
scale features

32. Furgo
 Furgo is one of the leading
companies when it comes
to mining projects.
 Mining Development and Management
– Fugro supports mine information
systems by delivering accurate
geospatial knowledge over the entire
lifecycle of a mine.
 aerial surveying data - baseline data for
feasibility studies, mine mapping and
permitting, stock pile calculations and
volumes, rehabilitation and waste dump
mapping.

33.  Regional geochemical and geological
surveys
 Airborne geophysics
 Satellite monitoring and mapping
 optical
 radar
 Multispectral
 Mapping
 Site selection
 Emergency response
 Aerial mapping
 Geophysics
 Photography
 LiDAR
 Management and mapping

34. The Future
UAVs – Unmanned Aerial Vehicles
• Unmanned Aerial Systems will improve the ability to map small-scale
surface features associated with geothermal systems in remote,
rugged or vegetated terrain.
(Calvin et al, 2015)
• Can also be used to monitor mines for maintenance and efficient
business management.
• As with all UAV applications there may be different issues with
standards, ethics and regulations.
On the left is an aerial view of a mine in the USA
captured using the INTEGRATOR UAV pictured
above on the right

35. The Future: Sentinel-2
E.S.A., n.d.

36. Sentinel-2 Specifications
 Sentinel-2A and Sentinel-2B
 2A - April 2015
 2B - 1st half of 2016
 To ensure the continuity of SPOT,
Landsat and ASTER imagery
 High resolution optical imagery
 Spectral resolution: 13bands
 Spatial resolution: 10m, 20m and 60m
 Temporal resolution: 5days

37. Sentinel-2 Methods
• Band ratios serve as proxies to derive
different minerals
• A dataset was simulated from a
reflectance-at-surface airborne
hyperspectral image
• Simulation studies

38. Case Study 6:
Cabo de
Gata, SE
Spain
A volcanic field which consists of
calc-alkaline volcanic rocks
(andesites & rhyolites)(Van der Meer, et al, 2014.)

39.  Case study to test the potential of
Sentinel-2
 Cabo de Gato Volcanic field
 Metamorphic minerals

40. Process
Input
(airborne
hyperspectral
data from the
HyMAP
sensor)
Geometric
correction
Spatial subset
Spectral
resampling
Spatial
degradation
Comparison
of scatterplots
Output
(Scatter plots)

41. Scatterplots between simulated
Sentinel-2 and simulated
ASTER bands

42. CABO DE GATA
A. Photograph of the study site
B. Interpretation of the geology in the area
C. 3D perspective with a natural colour composite image derived from HyMAP
D. HyMAP band ratio image showing hydrothermal alteration mineralogy.
Van der Meer, et al, 2014.

43. Van der Meer, et al, 2014.
The End
Result
Band Ratio Products
• Simulated Sentinel-2
• Simulated ASTER
• Real ASTER

44. Geological & Mineral Interpretation
Van der Meer, et al, 2014.

45. Results
 Ratio mapping
 Scatterplots
 Good correspondence between the ASTER
and Sentinel-2 ratios for ferric/ferrous iron,
ferric oxides, ferrous silicates, gossan and
NDVI
 Geologic mapping
 The simulated Sentinel-2 was visually
compared to a geological map & mineral maps.
 Simulated image products demonstrate a good
correspondence between ASTER and Sentinel-
2 VNIR and SWIR bands

46. Conclusion
Issues
• Cloud cover and vegetation
 Reproducibility
 Expense – software and datasets /
raw images
 The gap between academia and
industry
 Further study into use of radar in
mineral geology

47. Conclusion
Positives
• Geoinformatics – many applications
and uses
• Long and reliable history
• So many different dimensions and
components can be considered at
once
• UAVs and Sentinel-2 in the future

49. Bibliography
• Alexander Nubia Inc, 2011. Abu Marawat Gold-Copper. Available online at:
http://www.alexandernubia.com/cms/pages/13 [Accessed 28 February 2015 ]
• Beiranvand Pour,A., & Hashim, M., 2012, The application of ASTER remote sensing data to
porphyry copper and epithermal gold deposits, Ore Geology Reviews, Vol.44, P.1–9.
• Bedini,E., 2011. Mineral mapping in the Kap Simpson complex, central East Greenland,
using HyMap and ASTER remote sensing data, Advances in Space Research, Vol.47, P.60–
73.
• Calvin,W.M., Littlefield,E.F., & Kratt,C., 2015. Remote sensing of geothermal-related
minerals for resource exploration in Nevada, Geothermics, Vol.53, P.517–526.
• Drusch,M., Del Bello,U., Carlier,S., Colin,O., Fernandez,V., Gascon,F., Hoersch,B., Isola,C.,
Laberinti,P., Martimort,P., Meygret,A., Spoto Sy,O., Marchese,F., & Bargellini,P., 2012. Sentinel-
2: ESA's Optical High-Resolution Mission for GMES Operational Services, Remote
Sensing of Environment, Vol.120, P.25–36.
• E.S.A., n.d. ESA > Our Activities > Observing the Earth > Copernicus. Available online at:
http://www.esa.int/Our_Activities/Observing_the_Earth/Copernicus/Sentinel-2 [Accessed 02
February 2015 ]
• Furgo., 2015. EXPERTISE>OUR>SERVICES SURVEY>AERIAL MAPPING>Mining
Development and Management. Available online at: http://www.fugro.com/our-expertise/our-
services/survey/aerial-mapping#tabbed2 [Accessed 28 February 2015 ]
• Gabr,S.,Ghulam,A., & Kusky,T., 2010. Detecting areas of high-potential gold mineralization
using ASTER data, Ore Geology Reviews, Vol.38, P.59–69.
• Garrun,D., 2009. UAVs – Mining’s Eye in The Sky. Available online at: http://www.mining-
technology.com/features/feature60074/ [Accessed 28 February 2015 ]
• Ott,N., Kollersberger,T., and Tassara,A., 2006. GIS analyses and favorability mapping of
optimized satellite data in northern Chile to improve exploration for copper mineral
deposits. Geosphere, Vol.2., Issue.4., P.236-252.

50. Bibliography
• Satellite Imaging Corporation, 2001-2014. ASTER Satellite Image of Morenci Mine in
Arizona. Available online at: http://www.satimagingcorp.com/gallery/more-imagery/aster/aster-
arizona-morenci-mine-es/ [Accessed 02 February 2015 ]
• Rockwell, B.W. and Bonham, L.C., 2013, USGS National Map of Surficial Mineralogy: U.S.
Geological Survey Online Map Resource. Available online at:
http://cmerwebmap.cr.usgs.gov/usminmap.html [Accessed 14 March 2015]
• Rockwell, B.W., 2013, Automated mapping of mineral groups and green vegetation from
Landsat Thematic Mapper imagery with an example from the San Juan Mountains, Colorado:
U.S. Geological Survey Scientific Investigations Map 3252, 25-p. pamphlet, 1 map sheet, scale
1:325,000, http://pubs.usgs.gov/sim/3252/
• UNEP, CATHALAC., 2015. La Escondida, Chile. Latin America and the Caribbean – Atlas of
Our Changing Environment. Available online at:
http://www.cathalac.org/lac_atlas/index.php?option=com_content&view=article&id=22:la-
escondida-chile&catid=1:casos&Itemid=5 [Accessed 02 February 2015 ]
• U.S.G.S., 2012, Landsat-A Global Land-Imaging Mission: U.S. Geological Survey Fact Sheet
2012–3072, P.4.
• U.S.G.S., 2014. “Landsat Missions Timeline”, Available online at:
http://landsat.usgs.gov/about_mission_history.php [Accessed 14 March 2015]
• Van der Meer,F.D., Van der Werff,H.M.A., Van Ruitenbeek,F.J.A., Hecker,C.A., Bakker,W.H.,
Noomen,M.F.,Van der Meijde,M., Carranza,E.J.M., Boudewijn de Smeth,J., & Woldai,T., 2012.
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Applied Earth Observation and Geoinformation, Vol.14, P.112–128.
• Van der Meer,F.D., Van derWerff,H.M.A., & Van Ruitenbeek,F.J.A., 2014. Potential of ESA's
Sentinel-2 for geological applications, Remote Sensing of Environment, Vol.148, P.124–133.