Data mining for real mining: A robust algorithm for prospectivity mapping with uncertainties

Abstract

Mineral prospectivity mapping is an emerging application for machine learning algorithms which presents a series of practical difficulties. The goal is to learn the mapping function which can predict the existence or absence of economic mineralization from a compilation of geoscience datasets (ie: bedrock type, magnetic signature, geochemical response etc). The challenges include sparse, imbalanced labels (mineralization occurrences), varied label reliability, and a wide range in data quality and uncertainty. In order to address these issues an algorithm was developed based on total least squares and support vector machine regression which incorporates both data and label uncertainty into the objective function. This was done without losing sparsity in the residuals, thus maintaining minimal support vectors. Mineral prospectivity mapping is an application for machine learning which presents a series of practical difficulties. The goal is to learn the mapping function which can predict the existence of mineralization from a compilation of geoscience datasets. Challenges include sparse, imbalanced labels, varied label reliability, and a wide range in data uncertainty. To address this, an algorithm was developed based on TLS and SVM which incorporates both data and label uncertainty into the objective function.