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

TL;DR

This paper introduces a statistical approach using General Additive Models and curvature analysis of log-ratios to identify mineralization zones in geochemical transects, aiding exploration of both surface and buried deposits.

Contribution

It presents a novel method combining GAMs and curvature of log-ratios to detect and locate mineralization areas along geochemical transects.

Findings

Effective identification of mineralization zones based on curvature analysis.

Ability to rank elements by their indication strength.

Potential to distinguish between surface and buried mineral deposits.

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 along a transect. Moreover, the method determines along a transect the potential area of the deposit. The identification is based on General Additive Models (GAMs) for the element concentrations across the spatial coordinate(s). The log-ratios of the GAM fits are taken to compute the curvature, where high and narrow curvature is supposed to indicate the mineralization area. 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.