Recognizing multivariate geochemical anomalies for mineral exploration by combining deep learning and one-class support vector machine

Abstract

The recognition of multivariate geochemical anomalies is important for mineral exploration. Big data analytics, which involves the whole data and variables, is an alternative manner to delineate multivariate geochemical anomalies in support of machine learning algorithms due to their strong ability to capture the complex intrinsic and diverse links between geochemical characteristics and mineralization. However, this method faces the issue of data redundancy and calculation complexity, and high-dimensional problems raise great challenges for anomaly detection. This is the curse of dimensionality problem, which hinders the development of a variety of techniques for anomaly detection. In this study, a hybrid model that combines unsupervised deep belief networks (DBNs) and one-class support vector machine (OCSVM) is adopted to address the high-dimensional geochemical anomalies detection problem. In this model, the relevant features first extracted through the DBN are used as the input of the OCSVM. The decision function values of the hybrid method are employed to map the geochemical patterns related to iron mineralization. The comparative results on the performance of the hybrid model and the other three anomaly detection models (deep autoencoder model, OCSVM, and hybrid model with principal component analysis and OCSVM) in terms of the area under curve(AUC) values, suggest that the hybrid method of the DBN and OCSVM can efficiently recognize the geochemical anomalies related to iron mineralization. The DBN can extract the geochemical information, reduce the redundant features, and further enhance the scalability of the OCSVM for processing high-dimensional geochemical data. The extracted geochemical anomalies, which show a close spatial relationship with the Yanshanian intrusions, can provide significant guidance for the next round of mineral exploration.