The document presents a mining predictivity method called cell-based associations, which focuses on lithological associations to enhance mineral exploration efficiency while addressing common sampling issues. The method employs various data analysis techniques, including hierarchical clustering, to identify and sort lithological environments linked to mineral occurrences. It is designed as a guide for exploration rather than a standalone method, particularly useful when mineral occurrence data is scarce or biased.

1. Cell Based Associations
A mining predictivity method based on
Cell Based Lithological Associations
By Evren Pakyuz-Charrier
PhD Student Center for Exploration Targetting
evren.pakyuz-charrier@research.uwa.edu.au
06/11/2014 Cell Based Associations 1

2. Frame
Field data
Mineral occurences
+
Geological map
(1/50 000 to 1/250 000)
Aim
Strategical and tactical mining
Occurrences/lithologies
link
=
Polygon/points link
predictivity
Weight of Evidence
Boolean logic
Fuzzy Logic
Logistic Regression
Neural Network
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3. Current methods
assumptions
MO data set is unique
and representative
Extremely sensitive to
uncertainty, noise,
stupidity
Formation’s
areas/proportions are
considered relevant
input data
MO are individuals
Difficult to distinguish
« types » of favorability
sensitive to sampling
bias
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4. Aim
Design a method to avoid/solve those
issues
Study lithological associations without
mixing up MO or estimating scores
Cell Based (Lithological) Associations
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5. Spot lithological associations
linked to mineral occurrences
Search and point those
associations within the area of
study
Area is considered as an irrelevant data input
#Cell Lithological spectra
A B C D E
1-1 1 0 1 0 1
1-2 1 1 1 0 1
1-3 0 0 1 1 1
1-4 0 0 1 1 0
1-5 0 0 1 0 1
2-1 0 1 1 0 1
2-2 0 1 1 0 1
2-3 0 0 1 1 1
2-4 0 0 1 1 1
2-5 0 1 1 0 1
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6. Lithological associations sorting
Hierarchical Ascendant
Clustering
Progressively merges cells
according to their lithological
proximity
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7. Sorted and labeled lithological environments
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8. Holding classes are highlighted
MO containing cells are close
enough to their lithological
family
Family is marked
as favorable similar
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9. Wait !
How to choose an
appropriate grid?
Blind to the geological
map
Only the location of the
MO should be considered
Point density map
Lithological
environments
Point density local
anomalies
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10. Local density anomalies extend as far as the point
density inflexion lines that surround the MO
Basis to produce the
grid
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11. Montagne Noire
Zn MO
+
Montpellier
geological map
(1/250 000)
Case study
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13. Conclusion
•Easy
•Few artifacts
•Successfully distinguishes families
•Can be generalized to multivariate datasets
•Observational method
•Deals with sampling issues
•sensitive to lithological over-resolution
•Scale between MO and geological map must be compatible
•Gridding method is imperfect/debatable
•Concept of lithological environment
•Room for further development
•Immediate usefulness
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14. CBA should be used when
•The MO are scarce
•Sampling bias is suspected
•Strong clustering of MO
•Formation’s areas are irrelevant or small area
formations are suspected to be the most relevant
ones relating to MOs
•The existence of different types of mineral
deposits (for the same MO data set) are suspected
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15. •CBA is a guide to mining exploration NOT a
standalone mining predictivity method.
•The results will presumably not be better than other
methods when the input data is free (enough) of bias
and resolution is high.
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16. Annexes
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17. Gridding/Rasterization
As close possible to the
actual lithological
environments
Increase lithological
diversity = increase cell
size
Grid has to be geometrically close to the lithological
environments shapes and cell size has to be as large as
possible
Geometric parameters
• Threshold
• Cell size
• Position
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19. • αtot as close as possible to 100
•  as high as possible
• Best threshold possible
• Greatest cell size possible
Maximize representativity
VS
Maximize potential
diversity
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20. 1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Proportion de
cellules de
susceptibilité
dans la classe
Classes CAH
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
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21. Proportion
Proportion
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1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Lithologies (notations)
Formations cambriennes
Formations
ordoviciennes
Formations sédimentaires tertiaires et
quaternaires
Formations volcaniques
récentes
Formations méta-sédimentaires
de la zone axiale
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Lithologies (notations)
Formations sédimentaires tertiaires
et quaternaires
Formations cambriennes
Formation volcaniques
récentes

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