Application of one-class support vector machine to quickly identify multivariate anomalies from geochemical exploration data

Identifying multivariate anomalies from geochemical exploration data in a complex geological setting is very challenging because the complex geological setting may lead to an unknown high-dimensional distribution of the geochemical exploration data. One-class support vector machine (OCSVM) can give useful results in outlier detection in high-dimension or without any assumptions on the distribution of data. Thus, we applied the OCSVM model to identify multivariate geochemical anomalies from stream sediment survey data of the Lalingzaohuo district, an area with complex geological setting, in Qinghai Province, China. The performance of the OCSVM model was compared with that of continuous restricted Boltzmann machine (CRBM) in terms of receiver operating characteristic (ROC) curve, area under curve ( AUC ) and data-processing efficiency. The results show that the two models perform similarly well in terms of ROC and AUC ; while their data-modeling processes spent 6.06 and 279.36 s, respectively. The anomalies identified by the OCSVM model occupy 19% of the study area and contain 82% of the known mineral deposits; and the anomalies identified by the CRBM model occupy 35% of the study area and contain 88% of the known mineral deposits.