Application of Gaussian Mixture Models for Geochemical Anomaly Detection

Geochemical prospecting is a fundamental tool in mineral exploration. Traditionally, the interpretation of geochemical data has relied on classical statistical methods, which in many cases are univariate or linear in nature and may fail to adequately capture the complex multivariate relationships among geochemical parameters. In this context, machine learning approaches offer an alternative framework for the integrated analysis of multivariate data and the identification of hidden patterns. 

This study evaluates the application of a Gaussian Mixture Model (GMM) as an unsupervised method for the identification of geochemical anomalies of potential geological interest. The analysis was conducted on a dataset of 114 soil samples collected from the southwestern sector of the province of Ciudad Real. Before the application of the GMM, an exploratory statistical analysis was performed, including the Kaiser–Meyer–Olkin (KMO) test and the Measure of Sampling Adequacy (MSA), aimed to assess the suitability of the variables for multivariate analysis. 

After conducting several experiments, the results indicate that the Gaussian Mixture Model can identify zones with anomalous values consistent with geological interest, highlighting its potential as a supportive tool in geochemical prospecting.