13/04/2023 1Cell Based Associations

Cell Based AssociationsA 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

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Frame

Geological map(1/50 000 to 1/250 000)

Field data

Mineral occurences

Aim

Occurrences/lithologies link

=Polygon/points link

+

Strategical and tactical mining predictivity

Weight of EvidenceBoolean logic

Fuzzy LogicLogistic Regression

Neural Network

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Current methods assumptions

MO data set is unique and representative

Extremely sensitive to uncertainty, noise,

stupidity

MO are individuals

Difficult to distinguish « types » of favorability

sensitive to sampling bias

Formation’s areas/proportions are considered relevant

input data

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Design a method to avoid/solve those issues

Study lithological associations without mixing up MO or estimating scores

Cell Based (Lithological) Associations

Aim

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#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  

Area is considered as an irrelevant data input

Spot lithological associations linked to mineral occurrences

Search and point those associations within the area of

study

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Lithological associations sorting

Hierarchical Ascendant Clustering

Progressively merges cells according to their lithological

proximity

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Sorted and labeled lithological environments

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Holding classes are highlighted

MO containing cells are close enough to their lithological

family

Family is marked as favorable similar

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How to choose an appropriate grid?

Blind to the geological map

Wait !

Only the location of the MO should be considered

Point density map

Lithological environments

Point density local anomalies

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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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Zn MO

Montagne Noire

Montpellier geological map

(1/250 000)

+

Case study

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Conclusion

•Easy•Few artifacts•Successfully distinguishes families•Can be generalized to multivariate datasets•Observational method•Deals with sampling issues

•Room for further development•Immediate usefulness

•sensitive to lithological over-resolution •Scale between MO and geological map must be compatible•Gridding method is imperfect/debatable•Concept of lithological environment

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•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

CBA should be used when

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•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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Annexes

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Gridding/RasterizationAs 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

• Threshold• Cell size• Position

Geometric parameters

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• αtot as close as possible to 100

• as high as possible

• Best threshold possible

• Greatest cell size possible

Maximize representativity

Maximize potential diversity

VS

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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 29 300

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Classes CAH

Proportion de cel-lules de suscepti-

bilité dans la classe

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00.10.20.30.40.50.60.70.80.9

1

Lithologies (notations)

Proportion

Formations cambriennes

Formations ordoviciennes

Formations sédimentaires tertiaires et quaternaires

Formations volcaniques ré-centes

Formations méta-sédimentaires de la zone axiale

Bvc6

_7 dÔe1_2

e3Ce4_6

e4a_1_e5C

e6G FwFx_

y gh1_2 jD k2

k2a_1_

k2b k3

k4_6_o_

l3_5 LK m

MÒL2_3

o1_2_1_

oµ3_1_ pMr1_1_

r2_b_Sa1

Sa3SPc

SPf

SPg_j_1_ t ÔÒ2_3

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¾_1_0

0.10.20.30.40.50.60.70.80.9

1

Lithologies (notations)

Proportion

Formations sédimentaires tertiaires et quaternaires

Formations cambriennes

Formation volcaniques récentes

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