Mineral exploration and regional surface geochemical datasets: An anomaly detection and k-means clustering exercise applied on laterite in Western Australia

Abstract

A comprehensive geochemical survey was conducted in the western Yilgarn Craton, Western Australia, in 2007, collecting 3142 surface samples of regolith. Our study used this data to target potential sites for undiscovered buried or concealed Cu-Zn-Pb, and NiCu deposits. The core approach used the singularity mapping technique for detecting anomalies at the local scale. This work proposes a procedure to create a composite multi-element singularity map by linearly combining individual element singularity maps, using element-to-element correlation coefficients as weights for the linear combination process. Furthermore, the k-means clustering algorithm was applied to combinations of sub-sets of data and singularity values. Expert validation indicated that the k-means clustering approach yielded the best results when using 4 or 5 clusters, separating the distinct sites of the MINEDEX database. In either case, the incorporation of the singularity values provided the most accurate outcomes, with a dominant cluster correctly classifying up to 60 to 80 % of identified Cu and Ni deposits and mines, respectively.Based on these results and on the range of computed singularity values, simple rules were established to identify sampled data points satisfying the following criteria: (i) meeting the defined threshold singularity value and belonging to the k-means cluster that include the mines and (ii) not being in the neighbourhood of any known mineralization site from the MINEDEX database. These locations thus represent potential mineralization sites that warrant further investigation and exploration follow-up. The outcomes of this study strongly support the efficiency of anomaly detection and k-means clustering method applied on a regional surface geochemical dataset for mineral exploration to detect and target mineral systems.