Big Data Analytics of Identifying Geochemical Anomalies Supported by Machine Learning Methods

Big data analytics brings a novel way for identifying geochemical anomalies in mineral exploration because it involves processing of the whole geochemical dataset to reveal statistical correlations between geochemical patterns and known mineralization. Traditional methods of processing exploration geochemical data mainly involve the identification of positive geochemical anomalies related to mineralization, but ignore negative geochemical anomalies. Therefore, the identified geochemical anomalies do not completely reflect the sought geochemical signature of mineralization, leading to uncertainty in geochemical prospecting. In this study, data for 39 geochemical variables from a regional stream sediment geochemical survey of southwest Fujian Province of China were subjected to big data analytics for identifying geochemical anomalies related to skarn-type Fe polymetallic mineralization through deep autoencoder network. The receiver operating characteristic (ROC) and areas under curve (AUC) were applied to evaluate the performance of big data analytics. The AUC of the anomaly map obtained using all the geochemical variables is larger than the AUC of the anomaly map obtained using only five selected elements known to be associated with the mineralization (i.e., Fe2O3, Cu, Pb, Zn, Mn). This indicates that big data analytics, with the support of machine learning methods, is a powerful tool for identifying multivariate geochemical anomalies related to mineralization.