Recombinant DNA technology (Immunological screening)
Cell Based Associations - Evren Pakyuz-Charrier (CET/UWA)
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
06/11/2014 Cell Based Associations 2
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
06/11/2014 Cell Based Associations 3
4. Aim
Design a method to avoid/solve those
issues
Study lithological associations without
mixing up MO or estimating scores
Cell Based (Lithological) Associations
06/11/2014 Cell Based Associations 4
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
06/11/2014 Cell Based Associations 5
6. Lithological associations sorting
Hierarchical Ascendant
Clustering
Progressively merges cells
according to their lithological
proximity
06/11/2014 Cell Based Associations 6
7. Sorted and labeled lithological environments
06/11/2014 Cell Based Associations 7
8. Holding classes are highlighted
MO containing cells are close
enough to their lithological
family
Family is marked
as favorable similar
06/11/2014 Cell Based Associations 8
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
06/11/2014 Cell Based Associations 9
10. Local density anomalies extend as far as the point
density inflexion lines that surround the MO
Basis to produce the
grid
06/11/2014 Cell Based Associations 10
11. Montagne Noire
Zn MO
+
Montpellier
geological map
(1/250 000)
Case study
06/11/2014 Cell Based Associations 11
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
06/11/2014 Cell Based Associations 13
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
06/11/2014 Cell Based Associations 14
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.
06/11/2014 Cell Based Associations 15
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
06/11/2014 Cell Based Associations 17
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
06/11/2014 Cell Based Associations 19
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
06/11/2014 Cell Based Associations 20