College of Earth Sciences, Jilin University, Changchun 130061, China

The programs for multivariate geochemical anomaly detection with isolation forest and one-class support vector machine were developed based on the Python source codes of Sklearn. The geochemical anomalies were extracted from the stream sediment survey data of 1:50 000 scale collected from the Helong area, Jilin Province. By using the spatial locations of known mineral occurrences in the study area as the ground truth data, the ROC curves of the two algorithms were plotted and the AUC values were computed for comparing the performance of the two algorithms in geochemical anomaly detection. The results show that the two algorithms can properly identify geochemical anomalies, and the extracted geochemical anomalies are significantly spatially associated with the known mineral occurrences. Isolation forest slightly outperforms one-class support vector machine in terms of data modeling efficiency and geochemical anomaly detection performance.