Comparison of MLP and Bayesian approaches on mineral prospectivity mapping tasks

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.