A method to identify geochemical mineralization on linear transect

TL;DR

This paper evaluates a GAM-based method for detecting mineralization in geochemical soil samples along linear transects, aiming to improve mineral exploration by identifying pathfinder elements and optimal sampling materials.

Contribution

It tests and validates a curvature-based GAM method for identifying mineralized zones and determining suitable sampling materials in geochemical surveys.

Findings

The method successfully identifies mineralized zones in geochemical data.

It determines the most effective sampling material for mineral exploration.

The approach enhances detection of pathfinder elements along transects.

Abstract

Mineral exploration in biogeochemistry is related to the detection of anomalies in soil, which is driven by many factors and thus a complex problem. Mik\v{s}ov\'a, Rieser, and Filzmoser (2019) have introduced a method for the identification of spatial patterns with increased element concentrations in samples along a linear sampling transect. This procedure is based on fitting Generalized Additive Models (GAMs) to the concentration data, and computing a curvature measure from the pairwise log-ratios of these fits. The higher the curvature, the more likely one or both elements of the pair indicate local mineralization. This method is applied on two geochemical data sets which have been collected specifically for the purpose of mineral exploration. The aim is to test the technique for its ability to identify pathfinder elements to detect mineralized zones, and to verify whether the method can indicate which sampling material is best suited for this purpose. Reference: Mik\v{s}ov\'a D., Rieser C., Filzmoser P. (2019). "Identification of mineralization in geochemistry along a transect based on the spatial curvature of log-ratios." arXiv, (1912.02867).