Identification of mineralization in geochemistry based on the spatial curvature of log-ratios

Abstract

Detecting subcropping mineralizations but also deeply buried mineralizations is one important goal in geochemical exploration. The identification of useful indicators for mineralization is a difficult task as mineralization might be influenced by many factors, such as location, investigated media, depth, etc. We propose a statistical method which indicates chemical elements related to mineralization. The identification is based on GAM models for the element concentrations across the spatial coordinate(s). The log-ratios of the GAM fits are taken to compute the curvature, where high curvature is supposed to indicate mineralization. By defining a measure for the quantification of high curvature, the log-ratios can be ranked, and elements can be identified that are indicative of the anomaly patterns