Visualization and interpretation of geochemical exploration data using GIS and machine learning methods

Abstract

Geochemical exploration has provided significant clues for mineral exploration and has helped discover many mineral deposits. Although various methods, including classic statistics, multivariate statistics, geostatistics, fractal/multifractal models, and machine learning algorithms, have been successfully employed to process geochemical exploration data, efficient interpretation and visualization of geochemical exploration data in support of the discovery of mineral deposits remain challenging. In this study, a workflow for intelligent interpretation and visualization of geochemical exploration data, defined as processing geochemical survey data with support of a geographical information system (GIS) and machine learning algorithms, was proposed. The effectiveness of the intelligent interpretation and visualization of geochemical exploration data supported by GIS and machine learning algorithms was demonstrated using a case study of processing a regional-scale geochemical survey dataset collected from Sichuan Province, China. Future research should add more advanced mathematical and statistical models, such as deep learning algorithms, into GIS to support the intelligent interpretation and visualization of geochemical exploration data.