cell based associations - evren pakyuz-charrier (cet/uwa)
Post on 24-Jun-2015
82 Views
Preview:
DESCRIPTION
TRANSCRIPT
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
13/04/2023 2Cell Based Associations
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
13/04/2023 Cell Based Associations 3
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
13/04/2023 Cell Based Associations 4
Design a method to avoid/solve those issues
Study lithological associations without mixing up MO or estimating scores
Cell Based (Lithological) Associations
Aim
13/04/2023 Cell Based Associations 5
#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
13/04/2023 Cell Based Associations 6
Lithological associations sorting
Hierarchical Ascendant Clustering
Progressively merges cells according to their lithological
proximity
13/04/2023 Cell Based Associations 7
Sorted and labeled lithological environments
13/04/2023 Cell Based Associations 8
Holding classes are highlighted
MO containing cells are close enough to their lithological
family
Family is marked as favorable similar
13/04/2023 Cell Based Associations 9
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
13/04/2023 Cell Based Associations 10
Local density anomalies extend as far as the point density inflexion lines that surround the MO
Basis to produce the grid
13/04/2023 Cell Based Associations 11
Zn MO
Montagne Noire
Montpellier geological map
(1/250 000)
+
Case study
13/04/2023 Cell Based Associations 12
13/04/2023 Cell Based Associations 13
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
13/04/2023 Cell Based Associations 14
•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
13/04/2023 Cell Based Associations 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.
13/04/2023 Cell Based Associations 16
Annexes
13/04/2023 Cell Based Associations 17
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
13/04/2023 Cell Based Associations 18
13/04/2023 19Cell Based Associations
• αtot as close as possible to 100
• as high as possible
• Best threshold possible
• Greatest cell size possible
Maximize representativity
Maximize potential diversity
VS
13/04/2023 Cell Based Associations 20
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
13/04/2023 Cell Based Associations 21
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
ÒB3_4µC3 µÒ Ù
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
13/04/2023 Cell Based Associations 22
13/04/2023 Cell Based Associations 23
13/04/2023 Cell Based Associations 24
13/04/2023 Cell Based Associations 25
top related