Identification of Mineralization in Geochemistry for Grid Sampling Using Generalized Additive Models

Abstract

The important goals of mineral exploration geochemistry are detection and identification of underlying mineralization. This paper deals with element concentration data analyzed of surface geochemical samples acquired from soil horizons or plants. A new unsupervised procedure is proposed for this purpose when the samples have been taken on a regular or irregular grid in the area under investigation. The methodology is based on Generalized Additive Model fits on element concentration data. Then new data points are taken of the surface of the smooth fits across the entire sampling area as a regular grid. Pairwise log-ratios of elements are then calculated of these points, and curvature of the log-ratio pairs is computed. High curvature indicates abrupt spatial changes, which could point at locations of mineralized zones. A measure called c-value evaluates the overall curvature and thus serves as an importance measure of a log-ratio pair. The methodology is tested on two real surface geochemical data sets collected in areas with known underlying mineralization, and the results confirm existing knowledge of the underlying mineralization.