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© ioGlobal 2009 Resource Analytics & Data Systems Automation
Deriving Quantitative Geological
Information From Assay Data
Dave Lawie
Managing Director
ioGlobal
www.ioglobal.net
2
© ioGlobal Resource Analytics & Data Systems Automation
Introduction
● Multi-element assaying of samples is common in exploration,
resource and grade control work
● Other phase-specific data such as ‘extractable’ Cu, sulphide sulphur,
silicate Ni and carbonate carbon may also be collected.
● Using relatively simple tools, such data may be used to derive
quantitative information for
● Rocktype identification,
● Stratigraphic correlation
● Hydrothermal alteration identification and quantification.
● Assessment of exploration ‘fertility’
● Estimates of key metallurgical performance parameters
3
© ioGlobal Resource Analytics & Data Systems Automation
Exploratory Data
Analysis
Does not require statistics – or hypothesis testing
4
© ioGlobal Resource Analytics & Data Systems Automation
Exploratory Analysis
Point Density
“don’t show correlation coefficients without the scatterplots”
5
© ioGlobal Resource Analytics & Data Systems Automation
Exploratory Analysis
6
© ioGlobal Resource Analytics & Data Systems Automation
Classification Diagrams
● Can be used to push classification onto data, or verify field
relations and geological information
● For example, some are used to assess tectonic setting,
while others are used for rock-type assignment
● Classification diagrams are commonly applied to igneous
rocks, however many other diagrams exist.
● Importantly, you can create your own classification diagram
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© ioGlobal Resource Analytics & Data Systems Automation
Classification Diagrams
- Rock type identification in deeply
weathered terrain (after Hallberg 1984)
Andesite
Basalt
Dacite
Rhyolite
Based on the relatively immobile
elements Ti, Zr and Cr, it was
possible to discriminate between
volcanic rock types in the Yilgarn
The plot works because although
mobile constituents are lost from the
rock, immobile elements are
conserved.
The concentration of the immobile
elements changes, but their ratio
does not.
8
© ioGlobal Resource Analytics & Data Systems Automation
Classification Diagrams
- TAS Plot
• Provides a classification
scheme for volcanic
rocks
• Vulnerable to alkali loss,
prone to closure effects
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© ioGlobal Resource Analytics & Data Systems Automation
• ‘Immobile’ trace element
formulation
• Classification scheme for
volcanic rocks
• More robust than TAS
against mobility of alkalis
• Zr/Ti is a proxy for Si, Nb/Y
is a proxy for total alkalis
• Boundary positions
debatable
Classification Diagrams
- Rock type identification (Winchester and Floyd 1977)
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© ioGlobal Resource Analytics & Data Systems Automation
Custom Classification Diagram
Hand Drawn Line
Distinguishing
UFW from HW
P2
O5
vs Zr - Century, Stratigraphic Groupings
Upper Footwall Samples Segregate from Hangingwall
Zr
P
2
O
5
0.0
0.2
0.4
0.6
0.8
1.0
0 40 80 120 160 200 240
Background
UFW
UFW Distal
HW
HW Distal
Weath HW
Weath PHW
Weath Distal HW
P2O5
Data from Whitbread
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© ioGlobal Resource Analytics & Data Systems Automation
Classification - Caveats
The processes that have led to their derivation should
preferable by understood. Different diagrams can give
different classification results for the same sample
Things to watch out for: weathering, alteration, plotting
irrelevant rock types on the diagram, closure effects
of tri-plots, inappropriate data quality (eg, 4 acid vs
fusion for Zr), adequate precision (esp. for divisors)
and data too near dl.
12
© ioGlobal Resource Analytics & Data Systems Automation
Mineral Chemistry based Classification
● Data becoming mare available, MLA –type technologies
● Adapt a “whole – rock” chemistry diagram
● Show spinel compositional variation
● Derive an empirical classification scheme for new data
13
© ioGlobal Resource Analytics & Data Systems Automation
Jensen cation plot (left) and modified version (right) with
spinel nodes shown.
Spinel names abbreviated Mcr=magnesiochromite, Spl=spinel, Hcy=hercynite,
Ghn=gahnite, Mag=magnetite.
Mineral Chemistry based Classification - Spinels
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© ioGlobal Resource Analytics & Data Systems Automation
Spinel data from Barnes and Roeder (2001) plotted on the modified Jensen
cation plot.
Mineral Chemistry based Classification - Spinels
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© ioGlobal Resource Analytics & Data Systems Automation
Creating a category from a point density contour
Mineral Chemistry based Classification - Spinels
16
© ioGlobal Resource Analytics & Data Systems Automation
Mineral Chemistry based Classification - Spinels
Creating a category from a point density contour
Contour @
50th
percentile
of point
density
17
© ioGlobal Resource Analytics & Data Systems Automation
Mineral Chemistry based Classification - Spinels
Empirically Derived Classification Fields –
Classify New Data
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© ioGlobal Resource Analytics & Data Systems Automation
Assessment of Exploration Potential
● Fertility Indicators
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© ioGlobal Resource Analytics & Data Systems Automation
Data Analysis - Lithogeochemistry - Fertility
Data: literature and internet
Average Ni content for
0.5 % MgO interval
Anomalous?
Global Ni-MgO Background
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© ioGlobal Resource Analytics & Data Systems Automation
Data Analysis - Lithogeochemistry - Fertility
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© ioGlobal Resource Analytics & Data Systems Automation
http://www.ga.gov.au/ausgeonews/ausgeonews200509/gold.jsp
Source: The Ishihara Symposium: Granites and
Associated Metallogenesis
Palaeozoic Granite Metallogenesis of Easter Australia
– Phil Blevin (@ Geoscience Australia, 2004)
Fertility
Assessment
-
Model/Process
Driven
Such diagrams are useful
for rapidly assessing
fertility of large areas, but
„zones‟ on such diagrams
commonly vary greatly for
different terrains
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© ioGlobal Resource Analytics & Data Systems Automation
Poster: IGES 2005 - “Application of sulphur isotopes to discriminate Cu-Zn VHMS mineralization from barren Fe sulphide
mineralization in the greenschist to granulite facies Flin Flon – Snow Lake – Hargrave River region, Manitoba, Canada.” by Paul
Polito1,2, Kurt Kyser1, David Lawie3, Steven Cook4, Chris Oates5
Isotopes – Fertility Assessment
● Application of
Sulphur Isotopes to
assessing Fertility
of ‘Sulphide’
Discoveries
● Simple to apply,
robust IF adequate
orientation carried
out
23
© ioGlobal Resource Analytics & Data Systems Automation
Modelling Alteration
24
© ioGlobal Resource Analytics & Data Systems Automation
Alteration Vector
Traces +ve
Bulk alt -ve
Traces +ve
Bulk alt +ve
ORE
Veins, Fractures etc.
uc
cover
+ve
indications in
scout drilling
gw?
Geochemistry at the End of a Drill Rig
● Enlarging the size of the target by quantitatively measuring alteration and/or
searching for palaeo-dispersion at the cover - host-rock uc, ie interface sampling
● With sensible use of the above, drill spacing can be enlarged to the extent that
many undercover areas can be prospected with a sufficient degree of reliability
25
© ioGlobal Resource Analytics & Data Systems Automation
0
1
2
3
4
5
2 1 1 5 O Q 1 O 2 1 2 3 3 5 5 3 3 2 5 1 1 5 1 O Q O O 1 O S S 2 Q 1 O 5 1
Lithology
Sericitization
Intensity
(0
-
5)
A
B
C
D
E
F
M
S
Data: Cliff Stanley
completely partly entirely mostly pervasive fully speckled
relatively wholly partially kind of totally absolutely spotted
extreme none sort of severe dappled flooded unaltered
intense reminiscent of feeble strong incipient absent fresh
somewhat patchy moderate persistent utterly mottled weak
Identifying and Quantifying Alteration
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© ioGlobal Resource Analytics & Data Systems Automation
Quantifying Alteration – 2 Diagram Types
• General Element Ratio (GER) diagrams Do Not use a
Conserved Element, but the denominator is chosen based on
mineral stoichiometry e.g. K/Al vs. Na/Al
• Use the position of points relative to mineral nodes on a GER
diagram
• design your plots to put minerals found in background rocks in different
areas to the minerals found only in altered rocks
• Pearce Element Ratio (PER) diagrams use a Conserved
Element as the denominator e.g. K/Ti vs Al/Ti
• Use the slope on a PER diagram to represent minerals
• Distance from the origin is proportional to loss/gain
27
© ioGlobal Resource Analytics & Data Systems Automation
Lithogeochemistry – Alteration Modeling
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© ioGlobal Resource Analytics & Data Systems Automation
COMMENT qtz kaol musc kspar albite zircon Ilmenite
Kspar 50 45 2.5 2.5
Monz 50 22.5 22.5 2.5 2.5
Albite 50 45 2.5 2.5
Granite Musc 35 20 20 20 2.5 2.5
Kaol 50 45 2.5 2.5
Feldspar clay 15 20 20 20 20 2.5 2.5
ModalMineralogy
29
© ioGlobal Resource Analytics & Data Systems Automation
Rock
SiO2_
%
TiO2_
%
Al2O3_
%
Na2O_
% K2O_% H2O_%
ZrO2_
%
Fe2O3
_%
Kspar 77.8 2.3 7.8 0.0 7.2 0.0 2.9 2.3
Monz 78.6 2.3 8.1 2.6 3.6 0.0 2.9 2.3
Albite 79.4 2.3 8.4 5.1 0.0 0.0 2.9 2.3
Granite Musc 68.9 2.2 14.6 2.4 5.2 1.8 2.9 2.3
Kaol 66.5 2.2 15.5 0.0 0.0 11.0 2.8 2.2
Feldspar clay 57.4 2.2 21.4 2.4 5.2 6.7 2.8 2.2
Chemical Composition
30
© ioGlobal Resource Analytics & Data Systems Automation
COMMENT qtz kaol musc kspar albite zircon Ilmenite
Kspar 50 45 2.5 2.5
Monz 50 22.5 22.5 2.5 2.5
Albite 50 45 2.5 2.5
Granite Musc 35 20 20 20 2.5 2.5
Kaol 50 45 2.5 2.5
Feldspar clay 15 20 20 20 20 2.5 2.5
Monz
Kspar
Albite
Gran Musc
Kaol
Feld Clay
31
© ioGlobal Resource Analytics & Data Systems Automation
CCPI (chlorite-carbonate-pyrite index) vs AI (alteration index)
Mineral names; ep-epidote, ca-calcite, chl-chlorite, py-pyrite, il-illite, ab-albite, adl-adularia..
Diagram after Gemmel 2007 and Large et al. 2001 Point size proportional to Cu content.
Lithogeochemistry – Alteration Modeling
32
© ioGlobal Resource Analytics & Data Systems Automation
Lithogeochemistry – Alteration Modeling
33
© ioGlobal Resource Analytics & Data Systems Automation
Quantifying Alteration – 2 Diagram Types
• General Element Ratio (GER) diagrams Do Not use a
Conserved Element, but the denominator is chosen based on
mineral stoichiometry e.g. K/Al vs. Na/Al
• Use the position of points relative to mineral nodes on a GER
diagram
• design your plots to put minerals found in background rocks in different
areas to the minerals found only in altered rocks
• Pearce Element Ratio (PER) diagrams use a Conserved
Element as the denominator e.g. K/Ti vs Al/Ti
• Use the slope on a PER diagram to represent minerals
• Distance from the origin is proportional to loss/gain
34
© ioGlobal Resource Analytics & Data Systems Automation
Lithogeochemistry – Alteration Modelling
● Separate chemical variations due to
fractionation from those due to alteration
● Avoid closure
Pearce Element Ratio Plot - Displacement Vectors
Al/Ti
K/Ti
0
2
4
6
8
10
12
14
0 4 8 12 16 20 24
Muscovite Control
Illite Control
K-Feldspar Control Line
Al Bearing Phases with no K
K Bearing Phases with no Al
35
© ioGlobal Resource Analytics & Data Systems Automation
Monz
Kspar
Albite
Kaol
Gran Musc
Feld Clay
PER Diagram
36
© ioGlobal Resource Analytics & Data Systems Automation
Lithogeochemistry – Carbonate Alteration
37
© ioGlobal Resource Analytics & Data Systems Automation
Lithogeochemistry – Carbonate Alteration
Carbonate Carbonate
Ca_pct
Ca_pct
Calcite Line
Dolomite/Ankerite
Line
Ca/C Ratio Thematic Colouring
38
© ioGlobal Resource Analytics & Data Systems Automation
Integrated Workflow Example
39
© ioGlobal Resource Analytics & Data Systems Automation
Complex Workflow Enabled Interpretation
40
© ioGlobal Resource Analytics & Data Systems Automation
Altered
basalts
Complex Workflow Enabled Interpretation
41
© ioGlobal Resource Analytics & Data Systems Automation
Complex Workflow Enabled Interpretation
42
© ioGlobal Resource Analytics & Data Systems Automation
Metallurgical Applications
43
© ioGlobal Resource Analytics & Data Systems Automation
Geochemistry – Geometallurgy
Carlin Style Mineralisation
Diagram from: SEG Newsletter, No. 73, 2008
Whole rock chemistry
Immobile traces
Classification diagrams
Consistent classification
Phase specific assaying
Quantitative alteration mapping from assay
data
Each block is assigned a
predictive processing value that
can be input into the block
model
44
© ioGlobal Resource Analytics & Data Systems Automation
Geochemistry - Geometallury
Diagram: SEG Newsletter, No. 73, 2008
45
© ioGlobal Resource Analytics & Data Systems Automation
Bye*, A (2007) The application of multi-parametic block models to the mining process. The Journal of The Southern African Institute of Mining and Metallurgy, 107
46
© ioGlobal Resource Analytics & Data Systems Automation
Some Important Metallurgical Parameters
Measurable by Assaying at the Sample Scale
● Refractoriness (e.g. CN- leachable to non-leachable metal)
● Preg robbing (e.g. activated carbon)
● Mineralogical variability (e.g. cassiterite vs. stannite)
● Clay content (affects material handling properties)
● Acid-forming & acid-consuming minerals (e.g. carbonates)
● Cyanicides (e.g. cyanide consumers such as Cu, Fe, Zn, Hg)
● Oxygen consumers (e.g. sulphide phases)
● Deleterious or toxic elements (e.g. P distribution in iron ore)
● Coarse Au distribution (nugget effect)
● Sulphate producers (e.g. oxidation of sulphides)
47
© ioGlobal Resource Analytics & Data Systems Automation
Metallurgical Applications - Molar Ratio Diagrams
● Copper assay data plotted on a molar Cu vs S diagram. Molar ratios of Cu
to S and their ‘slopes’ for the diagram are provided in the table. The
plotted data are shown as coloured point density regions.
Cuprite
48
© ioGlobal Resource Analytics & Data Systems Automation
ACID PRODUCING
ACID CONSUMING
49
© ioGlobal Resource Analytics & Data Systems Automation
Magnesite
Forsterite
Antigorite
Talc
Metallurgical Applications –
Alteration of Ultramafic Rocks
50
© ioGlobal Resource Analytics & Data Systems Automation
Alteration of ultramafic rocks
Carbonation of a hydrated
Mg-silicate phase (serpentine)
Hydration of olivine to form serp
51
© ioGlobal Resource Analytics & Data Systems Automation
Magnetic model isosurface colour modulated by gravity
3D Integration
52
© ioGlobal Resource Analytics & Data Systems Automation
CONCLUSIONS
• Quantitative geological information is easily
extracted from assay data
• Applying existing methods in new areas yields
useful results
• If you have the data – use it
• If you don’t have the assay data– it may be worth
getting
• Cost is trivial cf that of obtaining the samples

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Lawie Ti vs Zr.pdf

  • 1. © ioGlobal 2009 Resource Analytics & Data Systems Automation Deriving Quantitative Geological Information From Assay Data Dave Lawie Managing Director ioGlobal www.ioglobal.net
  • 2. 2 © ioGlobal Resource Analytics & Data Systems Automation Introduction ● Multi-element assaying of samples is common in exploration, resource and grade control work ● Other phase-specific data such as ‘extractable’ Cu, sulphide sulphur, silicate Ni and carbonate carbon may also be collected. ● Using relatively simple tools, such data may be used to derive quantitative information for ● Rocktype identification, ● Stratigraphic correlation ● Hydrothermal alteration identification and quantification. ● Assessment of exploration ‘fertility’ ● Estimates of key metallurgical performance parameters
  • 3. 3 © ioGlobal Resource Analytics & Data Systems Automation Exploratory Data Analysis Does not require statistics – or hypothesis testing
  • 4. 4 © ioGlobal Resource Analytics & Data Systems Automation Exploratory Analysis Point Density “don’t show correlation coefficients without the scatterplots”
  • 5. 5 © ioGlobal Resource Analytics & Data Systems Automation Exploratory Analysis
  • 6. 6 © ioGlobal Resource Analytics & Data Systems Automation Classification Diagrams ● Can be used to push classification onto data, or verify field relations and geological information ● For example, some are used to assess tectonic setting, while others are used for rock-type assignment ● Classification diagrams are commonly applied to igneous rocks, however many other diagrams exist. ● Importantly, you can create your own classification diagram
  • 7. 7 © ioGlobal Resource Analytics & Data Systems Automation Classification Diagrams - Rock type identification in deeply weathered terrain (after Hallberg 1984) Andesite Basalt Dacite Rhyolite Based on the relatively immobile elements Ti, Zr and Cr, it was possible to discriminate between volcanic rock types in the Yilgarn The plot works because although mobile constituents are lost from the rock, immobile elements are conserved. The concentration of the immobile elements changes, but their ratio does not.
  • 8. 8 © ioGlobal Resource Analytics & Data Systems Automation Classification Diagrams - TAS Plot • Provides a classification scheme for volcanic rocks • Vulnerable to alkali loss, prone to closure effects
  • 9. 9 © ioGlobal Resource Analytics & Data Systems Automation • ‘Immobile’ trace element formulation • Classification scheme for volcanic rocks • More robust than TAS against mobility of alkalis • Zr/Ti is a proxy for Si, Nb/Y is a proxy for total alkalis • Boundary positions debatable Classification Diagrams - Rock type identification (Winchester and Floyd 1977)
  • 10. 10 © ioGlobal Resource Analytics & Data Systems Automation Custom Classification Diagram Hand Drawn Line Distinguishing UFW from HW P2 O5 vs Zr - Century, Stratigraphic Groupings Upper Footwall Samples Segregate from Hangingwall Zr P 2 O 5 0.0 0.2 0.4 0.6 0.8 1.0 0 40 80 120 160 200 240 Background UFW UFW Distal HW HW Distal Weath HW Weath PHW Weath Distal HW P2O5 Data from Whitbread
  • 11. 11 © ioGlobal Resource Analytics & Data Systems Automation Classification - Caveats The processes that have led to their derivation should preferable by understood. Different diagrams can give different classification results for the same sample Things to watch out for: weathering, alteration, plotting irrelevant rock types on the diagram, closure effects of tri-plots, inappropriate data quality (eg, 4 acid vs fusion for Zr), adequate precision (esp. for divisors) and data too near dl.
  • 12. 12 © ioGlobal Resource Analytics & Data Systems Automation Mineral Chemistry based Classification ● Data becoming mare available, MLA –type technologies ● Adapt a “whole – rock” chemistry diagram ● Show spinel compositional variation ● Derive an empirical classification scheme for new data
  • 13. 13 © ioGlobal Resource Analytics & Data Systems Automation Jensen cation plot (left) and modified version (right) with spinel nodes shown. Spinel names abbreviated Mcr=magnesiochromite, Spl=spinel, Hcy=hercynite, Ghn=gahnite, Mag=magnetite. Mineral Chemistry based Classification - Spinels
  • 14. 14 © ioGlobal Resource Analytics & Data Systems Automation Spinel data from Barnes and Roeder (2001) plotted on the modified Jensen cation plot. Mineral Chemistry based Classification - Spinels
  • 15. 15 © ioGlobal Resource Analytics & Data Systems Automation Creating a category from a point density contour Mineral Chemistry based Classification - Spinels
  • 16. 16 © ioGlobal Resource Analytics & Data Systems Automation Mineral Chemistry based Classification - Spinels Creating a category from a point density contour Contour @ 50th percentile of point density
  • 17. 17 © ioGlobal Resource Analytics & Data Systems Automation Mineral Chemistry based Classification - Spinels Empirically Derived Classification Fields – Classify New Data
  • 18. 18 © ioGlobal Resource Analytics & Data Systems Automation Assessment of Exploration Potential ● Fertility Indicators
  • 19. 19 © ioGlobal Resource Analytics & Data Systems Automation Data Analysis - Lithogeochemistry - Fertility Data: literature and internet Average Ni content for 0.5 % MgO interval Anomalous? Global Ni-MgO Background
  • 20. 20 © ioGlobal Resource Analytics & Data Systems Automation Data Analysis - Lithogeochemistry - Fertility
  • 21. 21 © ioGlobal Resource Analytics & Data Systems Automation http://www.ga.gov.au/ausgeonews/ausgeonews200509/gold.jsp Source: The Ishihara Symposium: Granites and Associated Metallogenesis Palaeozoic Granite Metallogenesis of Easter Australia – Phil Blevin (@ Geoscience Australia, 2004) Fertility Assessment - Model/Process Driven Such diagrams are useful for rapidly assessing fertility of large areas, but „zones‟ on such diagrams commonly vary greatly for different terrains
  • 22. 22 © ioGlobal Resource Analytics & Data Systems Automation Poster: IGES 2005 - “Application of sulphur isotopes to discriminate Cu-Zn VHMS mineralization from barren Fe sulphide mineralization in the greenschist to granulite facies Flin Flon – Snow Lake – Hargrave River region, Manitoba, Canada.” by Paul Polito1,2, Kurt Kyser1, David Lawie3, Steven Cook4, Chris Oates5 Isotopes – Fertility Assessment ● Application of Sulphur Isotopes to assessing Fertility of ‘Sulphide’ Discoveries ● Simple to apply, robust IF adequate orientation carried out
  • 23. 23 © ioGlobal Resource Analytics & Data Systems Automation Modelling Alteration
  • 24. 24 © ioGlobal Resource Analytics & Data Systems Automation Alteration Vector Traces +ve Bulk alt -ve Traces +ve Bulk alt +ve ORE Veins, Fractures etc. uc cover +ve indications in scout drilling gw? Geochemistry at the End of a Drill Rig ● Enlarging the size of the target by quantitatively measuring alteration and/or searching for palaeo-dispersion at the cover - host-rock uc, ie interface sampling ● With sensible use of the above, drill spacing can be enlarged to the extent that many undercover areas can be prospected with a sufficient degree of reliability
  • 25. 25 © ioGlobal Resource Analytics & Data Systems Automation 0 1 2 3 4 5 2 1 1 5 O Q 1 O 2 1 2 3 3 5 5 3 3 2 5 1 1 5 1 O Q O O 1 O S S 2 Q 1 O 5 1 Lithology Sericitization Intensity (0 - 5) A B C D E F M S Data: Cliff Stanley completely partly entirely mostly pervasive fully speckled relatively wholly partially kind of totally absolutely spotted extreme none sort of severe dappled flooded unaltered intense reminiscent of feeble strong incipient absent fresh somewhat patchy moderate persistent utterly mottled weak Identifying and Quantifying Alteration
  • 26. 26 © ioGlobal Resource Analytics & Data Systems Automation Quantifying Alteration – 2 Diagram Types • General Element Ratio (GER) diagrams Do Not use a Conserved Element, but the denominator is chosen based on mineral stoichiometry e.g. K/Al vs. Na/Al • Use the position of points relative to mineral nodes on a GER diagram • design your plots to put minerals found in background rocks in different areas to the minerals found only in altered rocks • Pearce Element Ratio (PER) diagrams use a Conserved Element as the denominator e.g. K/Ti vs Al/Ti • Use the slope on a PER diagram to represent minerals • Distance from the origin is proportional to loss/gain
  • 27. 27 © ioGlobal Resource Analytics & Data Systems Automation Lithogeochemistry – Alteration Modeling
  • 28. 28 © ioGlobal Resource Analytics & Data Systems Automation COMMENT qtz kaol musc kspar albite zircon Ilmenite Kspar 50 45 2.5 2.5 Monz 50 22.5 22.5 2.5 2.5 Albite 50 45 2.5 2.5 Granite Musc 35 20 20 20 2.5 2.5 Kaol 50 45 2.5 2.5 Feldspar clay 15 20 20 20 20 2.5 2.5 ModalMineralogy
  • 29. 29 © ioGlobal Resource Analytics & Data Systems Automation Rock SiO2_ % TiO2_ % Al2O3_ % Na2O_ % K2O_% H2O_% ZrO2_ % Fe2O3 _% Kspar 77.8 2.3 7.8 0.0 7.2 0.0 2.9 2.3 Monz 78.6 2.3 8.1 2.6 3.6 0.0 2.9 2.3 Albite 79.4 2.3 8.4 5.1 0.0 0.0 2.9 2.3 Granite Musc 68.9 2.2 14.6 2.4 5.2 1.8 2.9 2.3 Kaol 66.5 2.2 15.5 0.0 0.0 11.0 2.8 2.2 Feldspar clay 57.4 2.2 21.4 2.4 5.2 6.7 2.8 2.2 Chemical Composition
  • 30. 30 © ioGlobal Resource Analytics & Data Systems Automation COMMENT qtz kaol musc kspar albite zircon Ilmenite Kspar 50 45 2.5 2.5 Monz 50 22.5 22.5 2.5 2.5 Albite 50 45 2.5 2.5 Granite Musc 35 20 20 20 2.5 2.5 Kaol 50 45 2.5 2.5 Feldspar clay 15 20 20 20 20 2.5 2.5 Monz Kspar Albite Gran Musc Kaol Feld Clay
  • 31. 31 © ioGlobal Resource Analytics & Data Systems Automation CCPI (chlorite-carbonate-pyrite index) vs AI (alteration index) Mineral names; ep-epidote, ca-calcite, chl-chlorite, py-pyrite, il-illite, ab-albite, adl-adularia.. Diagram after Gemmel 2007 and Large et al. 2001 Point size proportional to Cu content. Lithogeochemistry – Alteration Modeling
  • 32. 32 © ioGlobal Resource Analytics & Data Systems Automation Lithogeochemistry – Alteration Modeling
  • 33. 33 © ioGlobal Resource Analytics & Data Systems Automation Quantifying Alteration – 2 Diagram Types • General Element Ratio (GER) diagrams Do Not use a Conserved Element, but the denominator is chosen based on mineral stoichiometry e.g. K/Al vs. Na/Al • Use the position of points relative to mineral nodes on a GER diagram • design your plots to put minerals found in background rocks in different areas to the minerals found only in altered rocks • Pearce Element Ratio (PER) diagrams use a Conserved Element as the denominator e.g. K/Ti vs Al/Ti • Use the slope on a PER diagram to represent minerals • Distance from the origin is proportional to loss/gain
  • 34. 34 © ioGlobal Resource Analytics & Data Systems Automation Lithogeochemistry – Alteration Modelling ● Separate chemical variations due to fractionation from those due to alteration ● Avoid closure Pearce Element Ratio Plot - Displacement Vectors Al/Ti K/Ti 0 2 4 6 8 10 12 14 0 4 8 12 16 20 24 Muscovite Control Illite Control K-Feldspar Control Line Al Bearing Phases with no K K Bearing Phases with no Al
  • 35. 35 © ioGlobal Resource Analytics & Data Systems Automation Monz Kspar Albite Kaol Gran Musc Feld Clay PER Diagram
  • 36. 36 © ioGlobal Resource Analytics & Data Systems Automation Lithogeochemistry – Carbonate Alteration
  • 37. 37 © ioGlobal Resource Analytics & Data Systems Automation Lithogeochemistry – Carbonate Alteration Carbonate Carbonate Ca_pct Ca_pct Calcite Line Dolomite/Ankerite Line Ca/C Ratio Thematic Colouring
  • 38. 38 © ioGlobal Resource Analytics & Data Systems Automation Integrated Workflow Example
  • 39. 39 © ioGlobal Resource Analytics & Data Systems Automation Complex Workflow Enabled Interpretation
  • 40. 40 © ioGlobal Resource Analytics & Data Systems Automation Altered basalts Complex Workflow Enabled Interpretation
  • 41. 41 © ioGlobal Resource Analytics & Data Systems Automation Complex Workflow Enabled Interpretation
  • 42. 42 © ioGlobal Resource Analytics & Data Systems Automation Metallurgical Applications
  • 43. 43 © ioGlobal Resource Analytics & Data Systems Automation Geochemistry – Geometallurgy Carlin Style Mineralisation Diagram from: SEG Newsletter, No. 73, 2008 Whole rock chemistry Immobile traces Classification diagrams Consistent classification Phase specific assaying Quantitative alteration mapping from assay data Each block is assigned a predictive processing value that can be input into the block model
  • 44. 44 © ioGlobal Resource Analytics & Data Systems Automation Geochemistry - Geometallury Diagram: SEG Newsletter, No. 73, 2008
  • 45. 45 © ioGlobal Resource Analytics & Data Systems Automation Bye*, A (2007) The application of multi-parametic block models to the mining process. The Journal of The Southern African Institute of Mining and Metallurgy, 107
  • 46. 46 © ioGlobal Resource Analytics & Data Systems Automation Some Important Metallurgical Parameters Measurable by Assaying at the Sample Scale ● Refractoriness (e.g. CN- leachable to non-leachable metal) ● Preg robbing (e.g. activated carbon) ● Mineralogical variability (e.g. cassiterite vs. stannite) ● Clay content (affects material handling properties) ● Acid-forming & acid-consuming minerals (e.g. carbonates) ● Cyanicides (e.g. cyanide consumers such as Cu, Fe, Zn, Hg) ● Oxygen consumers (e.g. sulphide phases) ● Deleterious or toxic elements (e.g. P distribution in iron ore) ● Coarse Au distribution (nugget effect) ● Sulphate producers (e.g. oxidation of sulphides)
  • 47. 47 © ioGlobal Resource Analytics & Data Systems Automation Metallurgical Applications - Molar Ratio Diagrams ● Copper assay data plotted on a molar Cu vs S diagram. Molar ratios of Cu to S and their ‘slopes’ for the diagram are provided in the table. The plotted data are shown as coloured point density regions. Cuprite
  • 48. 48 © ioGlobal Resource Analytics & Data Systems Automation ACID PRODUCING ACID CONSUMING
  • 49. 49 © ioGlobal Resource Analytics & Data Systems Automation Magnesite Forsterite Antigorite Talc Metallurgical Applications – Alteration of Ultramafic Rocks
  • 50. 50 © ioGlobal Resource Analytics & Data Systems Automation Alteration of ultramafic rocks Carbonation of a hydrated Mg-silicate phase (serpentine) Hydration of olivine to form serp
  • 51. 51 © ioGlobal Resource Analytics & Data Systems Automation Magnetic model isosurface colour modulated by gravity 3D Integration
  • 52. 52 © ioGlobal Resource Analytics & Data Systems Automation CONCLUSIONS • Quantitative geological information is easily extracted from assay data • Applying existing methods in new areas yields useful results • If you have the data – use it • If you don’t have the assay data– it may be worth getting • Cost is trivial cf that of obtaining the samples