Geochemical survey data cube: A useful tool for lithological classification and geochemical anomaly identification

Abstract

Geochemical survey data play a critical role in geological studies, mineral exploration, and environmental applications by providing information on geological events and processes such as mineralization and pollution. A typical geochemical survey dataset contains the analysis of multiple elements. For example, the National Geochemical Mapping Project of China comprises 39 major and trace element concentrations. Multiple geochemical maps can be generated by interpolating geochemical samples into raster maps to constitute a geochemical survey data cube in which elements are sorted by their atomic numbers. A geochemical spectrum can be created using these geochemical maps in which each pixel that records geochemical characteristics. In this study, a convolutional neural network (CNN) that considers the geochemical spectrum and spatial pattern of geological objects was employed to mine a geochemical survey data cube, aiming of geological mapping and geochemical anomalies identification associated with mineralization in the eastern part of Hubei Province of China. The results showed that (1) a geochemical survey data cube which built based on various geochemical exploration data provided vital information on mineralization process and the formation of geological features; and (2) a CNN had a strong ability to recognize high-level features in the geochemical survey data cube, and it showed excellent performance in mineral exploration and related geological studies.