Applying neural networks-based modelling to the prediction of mineralization: A case-study using the Western Australian Geochemistry (WACHEM) database

Abstract

Geochemical data collected and stored by government and private sectors are becoming increasingly complex and too large for manual interpretation. The use of automatic methods for identification of potentially spurious data and estimation of missing values in very sparse geochemical datasets can significantly improve our understanding of geological systems. Deep neural networks have recently achieved remarkable success in a wide range of applied problems. These methods do not require manual feature engineering and significantly outperform traditional machine learning algorithms when applied to large datasets. We present a deep learning based method for estimation of unknown sample analytes in geochemical data. This approach is entirely data-driven and, once the network is trained, delivers the results in real time by predicting the distribution of an unknown analyte in a single step. A case study on the Western Australian Geochemistry (WACHEM) database demonstrates the efficiency of the method. Base metals as well as many other metals show good predictive capability (average symmetric mean absolute percentage errors range from 20% to 26.1%). Silver, platinum and especially gold are found to be more difficult to predict. The estimation of rock types from geochemical data allows for validation of existing datasets and prediction of rock types directly from geochemical data when there is no other existing information. The results of this study can benefit mineral explorers by indicating exploration targets and highlighting gaps in existing data.