Recognition of multivariate geochemical anomalies associated with mineralization using an improved generative adversarial network

Abstract

Deep learning (DL) algorithms have a strong ability to recognize high-level features in geochemical exploration data and have been widely employed for the recognition of multivariate geochemical anomalies linked to mineralization. In this study, the adversarially learned anomaly detection (ALAD) algorithm, an improved generative adversarial network (GAN), was employed to detect multivariate geochemical anomalies related to mineralization. Compared with other unsupervised deep learning algorithms, ALAD significantly improves anomaly detection performance by combining the advantages of deep variational autoencoder and generative adversarial network. Various experiments were performed to construct a well-designed network structure to process high-dimensional geochemical data to identify geochemical anomalies linked to tungsten (W) polymetallic mineralization in the south of Jiangxi Province of China. The extracted geochemical anomalies have a high spatial distribution correction with the locations of the discovered W polymetallic mineralization. Both the area under the receiver operating characteristic curve and the prediction-area plot indicate a good performance of the ALAD model. Furthermore, the extracted geochemical anomalies are spatially correlated with granites which control the spatial distribution of W polymetallic mineralization. These observations indicate that, as an unsupervised deep learning algorithm, ALAD is useful for detecting geochemical anomalies associated with mineralization.