Presentation to GIS society of South Africa.

1. Some Examples of Remote Sensing & 2-/3-D
GIS-based Mineral Exploration
Applications in Africa.
Dr Iúma Martinez
GISSA (19 June 2013)
by

2. Acknowledgements
Disclaimer
I would like to acknowledge kind permission by Tsodilo Resources Pty Limited and Goldstone
Resources who have allowed me to include a number of technical slides produced as a
result of working on their projects. Similarly one or two general background slides have been
generated which refer to work conducted by AEON (Prof de Wit) /CIGC and MIRA
Geoscience.
None of the views expressed in this presentation reflect the views of above mentioned
parties referred to.

3. In the beginning….
GISSA (19 June 2013)

4. Key differences: Exploration Geology >< Urban GIS
GISSA (19 June 2013)
•you may not be able to see what your trying to map (sand cover)
•There may be no previous data
•The area may be remote or difficult to access etc
Greenfields Exploration Projects may rely heavily on Remote sensing techniques
in the initial phases
Satellite Imagery Airborne Geophysical Surveys + (Historical data)
As the project progresses, Ground-based surveys followed by Drilling data enter
the exploration equation.
Ground magnetics Soil sampling
Drill logs
Geology
Magnetic
susceptibilityStructural Mapping
Assay data & 3d Modelling Presentation
The Data Flow:

5. Today’s a la carte Menu:
GISSA (19 June 2013)
1. Image processing of Satellite data for Mineral Exploration (2D)
2. Filtering Geophysical data for Kimberlite Exploration(2.5D)
3. Surface modelling (pseudosurfaces & isosurfaces) (2.5D)
4. GIS-based Spatial Selection Rules /Proximity Analyses (2D)
5. Determining Anomalous Geochemical Assay data (2D)
6. Estimating Resource volumes / geological modelling (3D)
7. Resource Modelling and drillhole planning (3D)
8. Making the data beautiful (Fly-through,3D)
9. Regional Context Analysis (2D) (MAB)
10. Gondwana & Mineral Exploration (3/4D)
Remote Sensing
Ground Surveys
Drilling
Presentation
Spatial- Temporal
contextualisation
(Pricing based on dimensions involved)

6. 1 Image processing of Satellite data for Mineral
Exploration
GISSA (19 June 2013)
(like Aliens we start by looking at the Earth from Space)

7. 1 Image processing of Satellite data for Mineral
Exploration
•ASTER / ASTERGDEM
•HYPERION
•LANDSAT
•SRTM
Many Imagery types: GeoEye , DigitalGlobe, Spot Image, RapidEye, ImageSat
International, Eros (-A,-B), Meteosat; Meteosat Second Generation (MSG).
Different Resolutions: Spatial, Spectral, Temporal & Radiometric

8. ASTER data - What it looks like (Ngamiland,Botswana)

9. Mineral Ratios: ASTER DATA
•3 groups of bands (14 in total)
•MINERAL ABUNDANCE RATIOS
•COMPOSITE images

10. Gossan & Phengitic Alteration Composites
Amphibole-MgOH
Mineral Ratio Image

11. Hyperspectral data & Mineral Mapping
•Similar calculations to ASTER but working with wavelength not band number
•Many more wavelength intervals > 250, higher spectral resolution
•un-mixing techniques can be used to produce mineral maps for unique minerals unlike ASTER.
•Alunite –Kaolinite-Pyrophyllite can be separated as can dolomite and calcite etc.
ENVI:
Mineral
Library
Spectrum
Reflectance of unknown mineral
Reflectance of best fit mineral from SLI
Best fit mineral has the highest score
Reflectance spectrum for
this pixel sample of the
of the unknown mineral

12. Mineral Mapping South Australia (Primary Industries & Resources SA, CSIRO
Exploration & Mining & University of Adelaide)(Pine Creek - HyMap) – Airborne
Hyperspectral Data
True-color RGB
False color composite
Spectral end
member
mapping
Dolomite -
Kimberlite
Absorption lines of interest:
2.31 and 2.39

13. Compare Hyperion-ALI data
footprint to ASTER
Hyperion / ALI Satellite Hyperspectral
Data (Ngamiland, Botswana)
A kimberlitic spectral signature
was created by sampling the
imagery at known kimberlite
locations
The satellite data is too coarse to
apply Mineral Spectra Library tests
Concentrations along
channels and inter-
dune ridge areas
detected

14. Landsat ETM+ Angola: Iron oxides band 5 / band 4
•PCI Geomatica
•Automated edge extraction (lithological boundaries)
•Automated lineament extraction (lineaments, faults)
Camec (2007/8)
Higher ratios
on:
Alluvial Fan
&
Channels

15. Mineral occurrences & Iron oxides band 5 / band 4
Ring-like features
faulted instrusive / dyke
Camec (2007/8)

16. The Shuttle Radar Topography Mission (SRTM)
•Mapping current geomorphology
•Mapping lineaments and other
structural features
•Landsat shows surface variations while
SRTM has a penetration depth (reveals
subsurface information)
SRTM hillshaded view Cape Fold Belt near Montague
Paleochannels – recent versus ancient
Drainage / Streams

17. 2 Image enhacement of Geophysics data
for Mineral Exploration GISSA (19 June 2013)

18. Location:
NW Ngamiland
Botswana
Software:
ERDAS Imagine Virtual GIS
Crux of the matter….
IMPROVED DEFINITION OF
FEATURES IN AEROMAGNETIC DATA
IMAGE FILTERING TECHNIQUES
2.5-D SURFACE MODELING OF
AEROMAGNETIC DATA
PSEUDO -SURFACE FOR DRAPING THE
ENHANCED IMAGE
AEROMAGNETICS DATA TERRAIN IMPRESSION

19. Kimberlite
DIPOLE Target
Start: RAW
Aeromagnetic Data
image
It’s all about :
FEATURE DEFINITION /
IMAGE ENHANCEMENT
OR…..
More simply:
How can we make
these dimples &
warts stand out?

20. CONVOLUTION FILTERS & PROCESSING ALGORITHMS
Aim: Improve
definition of potential
Kimberlite targets
Tools in ERDAS & ERMAPPER to
remove the “makeup” of
overburden and geophysical
noise

21. A large number of filters exist

22. Structure enhanced & hillshaded aeromagnetic data
Dykes Dipoles

23. Paleochannel
s
Previous targets
Precious Stones – Target
selection

24. Crater or Skydiving accident?

26. Impact crater – plan view (ERDAS Imagine VirtualGIS)

27. Impact crater – terrain view (ERDAS Imagine VirtualGIS)
(Crater confirmed by: Impact crater Research Group, Wits, G. Cooper (2009))

28. •Regional Aeromag tiles
•Ground mag relative to
Aeromag
X
Y
Z (ΔTMF)
E (X)
ΔTmf (Z)
N (Y)

29. Xaudom Ironstone Mag Anomaly & VTEM voxels
VTEM = versatile time domain electromagnetic system
(Encom)

30. VTEM isosurface in 2D section

31. 4 GIS-based Spatial Selection Rules /Proximity Analyses
GISSA (19 June 2013)

32. GIS-based Spatial Selection Rules & Proximity Analyses
•Distribution of base metal sulphide rich cores + geophysical exploration datasets
favourable target zones.
•VTEM CDI conductors, possible faults / fault junctions, ironstone & other lithology – conductor
relationships, BA gravity, aeromagnetics data
•Selections Rules:
1. lie within VTEM 400m CDI conductor
2. lie within lithological unit bearing conductor
3. lie within: 1 - 3 km of ironstone occurrences & within ferruginous quartzite
4. lie within: 1 km of ironstone occurrences
5. lie within: 1 km of possible fault / fault junction occurrences
6. lie within: 2 km of possible faults
7. lie within: – 4.3 and 6.3 wrt the BA gravity value

33. Magnetic
break /
possible
fault
junction
Xaudum Group Ironstone – interpreted geology & structurally enhanced aeromagnetic data
GIS-based Spatial Selection Rules & Proximity Analyses

34. GIS-based Selection Rules/ Proximity Analyses
Drilled sulphides not on Ironstone but
within 1-3 km of the ironstone. Bouger Anomaly gravity data

35. GIS-based Selection Rules/ Proximity Analyses
VTEM data: sulphides are within or closely associated with a conductive zone in a lithology.
(Kgothang, 2009).
Faults Fault intersections Lithology /Conductor

36. GIS-based Selection Rules/ Proximity Analyses

37. 5 Determining & Depicting
Anomalous Assay data
GISSA (19 June 2013)
What is an anomaly?
Sometimes it’s easy to see an anomaly
1. Deviation or departure from the normal
or common order, form, or rule.
2. One that is peculiar, irregular, abnormal,
or difficult to classify
•Something that “sticks out” in the data
And may be worthy of closer inspection in
terms of mineral exploration work.
•Ideally you want to detect an anomaly
across several data sets
•Once found one must ask:“Why is it there?”

38. 5 Determining & Depicting Anomalous Assay data
GISSA (19 June 2013)
Sometimes it’s not so easy to see an anomaly
(It kind of blends into the background)

39. Ni
Geochemical Anomalies Geophysical Anomalies
Zn
Magnetics
Soil Geochemistry &
Regional Anomalies
Magnetic Dipoles
Ground-survey level Anomalies

40. Correlations in the Southern Base Metal PLs
A (VTEM)
B (soil geochemistry)
C (trends, BA gravity)
D (structures, RTP-TMI)
(Along whole VTEM line)
Aeromagnetics

41. - Vertical downhole component (Z)
- Skewed assay distributions (normalization may be required)
- Multiple assaying techniques for same element (varying LODs)
- Assay – lithology correlations suggested regionally in 2D need to be Verified in 3D
& relative to drilled hole geology logs.
Anomalies in Drill hole assay data - tricky stuff
Whichever targeting or modelling approach one follows, one must
always remain vigilant to the inaccuracies of individual datasets.

42. Otherwise you
might
just
end
up
in
the wrong
place….

43. 6 Estimating Resource volumes (3D)
GISSA (19 June 2013)
1 2
3

44. GISSA (19 June 2013)
4 5
6
6 Estimating Resource volumes (3D)

45. Volume of iron ore
Between two lines
(150m):
2 075 750 m3
Using an average
density of 4.0g / cm3
this is:
8.3 mil tonnes
6 Estimating Resource volumes (3D)

46. A quantitative 3D model of the Earth that is consistent with all exploration data
and is testable by drilling.
Components of a CEM:
• DEM
• Geological mapping
• Interpretive sections
• Drilling
• Surface gravity and
inversion
• Airborne magnetics
• MT data & inversion
• Spatial properties
• Landsat/Airphoto images
•“Region” membership
-Lithology code
-(Formation code)
-(Alteration code)
•Rock properties
-Physical properties (Mag sus)
-Geochemical properties (Assays)
-(Engineering properties)
•Distance properties
-from faults,
-from geological contacts
-from drillholes
Each cell in the geological model / voxet has:
What is a Common Earth Model? (CEM)

47. .
Above basement (L)
Querying the Model - A Boolean Query
What is the exploration model?
Target = {(R>400) (D<0.25)  (Lbasement)  (DI<500)}
Query Results
Resistive (R) Low density (D)
near structural
intersections (DI)

48. 7 Resource Modelling and drillhole planning (3D)
GISSA (19 June 2013)
•Homase (Ghana); Ashanti Gold Belt
•248 boreholes (trenches and rip logs
another 138 logs); 3 Km strike length.
•significant intersects:
7.3 – 19.7 Au grade (g/t) (over 6 to 7 m).
•Total gold resource is:
6.32 million tonnes of ore at an average
grade of 1.4 g/t gold (282,608 ounces)

49. GISSA (19 June 2013)
7 Resource Modelling and drillhole planning (3D)

50. GISSA (19 June 2013)
7 Resource Modelling and drillhole planning (3D)

51. GISSA (19 June 2013)
•DEM pre- and post-mining surface
•Pierce points
•Cross-sections
7 Resource Modelling & drillhole
planning (3D)

52. 8 Making
the
data
Beautiful
(Fly-through)
GISSA (19 June 2013)

53. 8 Making the data beautiful (Fly-through, 3D)
GISSA (19 June 2013)
Why?
Investors like pretty
things! 
You can communicate
the data more strongly
(see Au variations in the
assay data)
You can use fly-throughs
to contextualize the data
visually and dynamically
See: the Homase Fly through video at:
www.linkedin.com/in/iuma-martinez-a7835332

54. 9 Regional Context Analyses (2D)
.
As with overlapping architectural eras in a modern city, in Geo-Exploration
we have overlapping eons, geological interpretations, imprints & varying datasets
Multiple faulting &
deformation
histories , lithological
variations etc….
All add up to create a
Complex Geological
Fabric

55. Tsodilo Resources drills extension of Zambian copper belt-like
mineralisation in Pan African Basement of northwest Botswana.
(De Wit, 2009, Tsodilo Resources Press Release)
Regional Vision of Mineralisation
A platform for testing hypotheses….(regional extrapolation?)

56. 10 Gondwana & Mineral Exploration
(source: De Wit, 2009)
Today’s Mineral Deposit locations are a
Function of “yesterday’s” Geological History.
Contextualise exploration efforts within
this broader Spatial Temporal context.

57. Conclusions

58. So Data
Visualisation can…..
•Open a Window to
our data
•Enable us to be
more Calculating &
Analytical
•Inspire Curiosity
& Sometimes……
•Lead to Astounding
Revelations!

59. “The only real
Voyage of
Discovery
consists
not
in
Seeking New
Landscapes, but
in having New Eyes.”