Mineral Prospectivity Prediction from High-Dimensional Geoscientific Data Using a Similarity-Based Density Estimation Model |
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| Authors: | Andrew A Skabar |
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| Institution: | (1) Department of Computer Science and Computer Engineering, La Trobe University, Bundoora, VIC, 3086, Australia |
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| Abstract: | Assuming a study region in which each cell has associated with it an N-dimensional vector of values corresponding to N predictor variables, one means of predicting the potential of some cell to host mineralization is to estimate, on the basis
of historical data, a probability density function that describes the distribution of vectors for cells known to contain deposits.
This density estimate can then be employed to predict the mineralization likelihood of other cells in the study region. However,
owing to the curse of dimensionality, estimating densities in high-dimensional input spaces is exceedingly difficult, and
conventional statistical approaches often break down. This article describes an alternative approach to estimating densities.
Inspired by recent work in the area of similarity-based learning, in which input takes the form of a matrix of pairwise similarities
between training points, we show how the density of a set of mineralized training examples can be estimated from a graphical
representation of those examples using the notion of eigenvector graph centrality. We also show how the likelihood for a test
example can be estimated from these data without having to construct a new graph. Application of the technique to the prediction
of gold deposits based on 16 predictor variables shows that its predictive performance far exceeds that of conventional density
estimation methods, and is slightly better than the performance of a discriminative approach based on multilayer perceptron
neural networks. |
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