Comparison of MLP and Bayesian approaches on mineral prospectivity mapping tasks

Skabar, Andrew 2004, Comparison of MLP and Bayesian approaches on mineral prospectivity mapping tasks, in IC-AI & MLMTA 2004 : Proceedings of the International Conference on Artificial Intelligence & Proceedings of the International Conference on Machine Learning : Models, Technologies & Applications, CSREA Press, [Las Vegas, Nev.], pp. 946-952.

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Title Comparison of MLP and Bayesian approaches on mineral prospectivity mapping tasks
Author(s) Skabar, Andrew
Conference name International Conference on Artificial Intelligence and International conference on Machine Learning; Models, Technologies and Applications (2004 : Las Vegas, Nev.)
Conference location Las Vegas, Nev.
Conference dates 21-24 Jun. 2004
Title of proceedings IC-AI & MLMTA 2004 : Proceedings of the International Conference on Artificial Intelligence & Proceedings of the International Conference on Machine Learning : Models, Technologies & Applications
Editor(s) Arabnia, Hamid
Mun, Youngson
Publication date 2004
Start page 946
End page 952
Publisher CSREA Press
Place of publication [Las Vegas, Nev.]
Keyword(s) Neural networks
Mineral exploration
Expectation maximization
Summary Mineral Prospectivity Mapping is the process of combining maps containing different geoscientific data sets to produce a single map depicting areas ranked according to their potential to host mineral deposits of a particular type. This paper outlines two approaches for deriving a function which can be used to assign to each cell in the study area a value representing the posterior probability that the cell contains a deposit of the sought-after mineral. One approach is based on estimating probability density functions (pdfs); the second uses multilayer perceptrons (MLPs). Results are provided from applying these approaches to geoscientific datasets covering a region in North Western Victoria, Australia. The results demonstrate that while both the Bayesian approach and the MLP approach yield similar results when the number of input dimensions is small, the Bayesian approach rapidly becomes unstable as the number of input dimensions increases, with the resulting maps displaying high sensitivity to the number of mixtures used to model the distributions. However, despite the fact that Bayesian assigned values cannot be interpreted as posterior probabilities in high dimensional input spaces, the pixel favorability rankings produced by the two methods is similar.
ISBN 1932415327
9781932415322
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2004, CSREA Press
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005284

Document type: Conference Paper
Collection: School of Information Technology
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