Geostatistically Enhanced Learning for Supervised Classification of Wall-Rock Alteration Using Assay Grades of Trace Elements and Sulfides

The spatial zoning of wall-rock alteration is a useful guide for exploration of porphyry deposits. The current techniques to typify and quantify alteration types have a component of subjectivity and may not reconcile with mineralogical observations. An alternative is to apply machine learning (ML) to classify alteration based on geochemical and mineralogical feature variables. However, classification loses accuracy because of natural and artificial short-scale variability and missing information, or because it ignores the spatial correlations of the feature variables. Here we show that these inconveniences can be overcome by replacing these variables with proxies obtained through geostatistical simulation. The use of such proxies improves the accuracy scores by eight percentual points by removing the noise affecting the feature variables and infilling their missing values. Furthermore, the uncertainty in the classification predictions can be quantified accurately. Our results demonstrate how geostatistics enriches ML to achieve higher predictive performance and handle incomplete and noisy data sets in a spatial setting. This synergy has far-reaching consequences for decision making in mining exploration, geological modeling, and geometallurgical planning. Beyond the presented pioneering application, we expect our approach to be used in supervised classification problems that arise in varied disciplines of natural sciences and engineering and involve regionalized data.