Mapping geochemical anomalies related to Fe–polymetallic mineralization using the maximum margin metric learning method

Abstract

Geochemical anomaly identification is an important task in mineral exploration targeting. This task can be regarded as a binary classification problem whereby the aim is to discriminate between anomalous and not anomalous (i.e., background). We can analyze geochemical data from the aspects of frequency distributions, correlations and variances, geometrical properties of geochemical anomalies, and scale independence of geochemical patterns. In this study, we recognize geochemical anomalies based on the intrinsic relationship between geochemical elements which can be addressed by measuring their separation distance. As a machine learning method, metric learning aims at exploiting the statistical information between the geochemical features in limited training samples, and developing a more suitable distance to evaluate the similarity of samples without priori distribution assumptions. Accordingly, a metric learning method based on the maximum margin frame was applied to identify multivariate geochemical anomalies related to Fe-polymetallic mineralization in the southwestern Fujian Province of China. The geochemical exploration data were firstly translated into a dimensional reduced subspace with the help of maximum margin metric learning (MMML). The adaptive coherence estimator (ACE) detector, from the field of target detection, was then employed to identify geochemical anomalies. The anomaly results obtained by this integrated procedure using a combination of MMML and ACE were compared to those obtained by ACE without metric learning using a receiver operating characteristic (ROC) curve. The area under the curve value implied superior performance using the combination of MMML and ACE, suggesting that this hybrid method can be effectively applied to recognize geochemical anomalies linked to mineralization.