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

The methods for geochemical anomaly detection are usually based on statistical models, and it needs to assume that the sample population satisfies a specific distribution, which may reduce the performance of geochemical anomaly detection. In this paper, the isolation forest model is used to detect geochemical anomalies and it does not require geochemical data to satisfy a particular distribution. By constructing a tree to traverse the average path length of all data, anomaly scores are used to characterize the anomaly and background fields, and the optimal threshold is selected to identify geochemical anomalies. Taking 1:200 000 geochemical exploration data of Fusong area in Jilin Province, NE China as an example, Fe₂O₃ and Pb were selected as the indicator elements to identify geochemical anomalies, and the results were compared with traditional statistical methods. The results show that the isolation forest model can effectively identify univariate geochemical anomalies, and the identified anomalies results have significant spatial correlation with known mine locations. Moreover, it can identify both high value anomalies and weak anomalies.