Geochemical anomaly mapping using sparse principal component analysis in Jining, Inner Mongolia, China

Abstract

Mapping geochemical anomalies is one of the most important tasks in mineral exploration. Sparse principal component analysis (SPCA) can be applied to investigate the correlations among geochemical elements and geological variables, which forces the loading values of the partial elements to zero. In this paper, the robust SPCA algorithm was used to process geochemical exploration data on a scale of 1:50,000 for identifying geochemical anomalies related to mineralization. The results show SPC1 and SPC3 indicate the spatial distribution of the Hannuoba basalt and Jining Group formation, respectively. The outcropped Hannuoba formations are located in high-value area of scores map of SPC1. The element association of W-Mo on SPC2 is related to molybdenum (Mo) mineralization. The spatial distribution of SPC2 scores suggests the distribution of Mo mineralization. The discovered Mo polymetallic deposits are located in the anomalous area of SPC2 scores. The SPCs identify localized geochemical anomalies and disambiguate the effects of outliers, which improve interpretability. These results indicate SPCA is a powerful tool for mapping geochemical anomalies.