Detection of Multivariate Geochemical Anomalies Using the Bat-Optimized Isolation Forest and Bat-Optimized Elliptic Envelope Models

Isolation forest and elliptic envelope are used to detect geochemical anomalies, and the bat algorithm was adopted to optimize the parameters of the two models. The two bat-optimized models and their default-parameter counterparts were used to detect multivariate geochemical anomalies from the stream sediment survey data of 1:50 000 scale collected from the Helong district, Jilin Province, China. Based on the data modeling results, the receiver operating characteristic (ROC) curve analysis was performed to evaluate the performance of the two bat-optimized models and their default-parameter counterparts. The results show that the bat algorithm can improve the performance of the two models by optimizing their parameters in geochemical anomaly detection. The optimal threshold determined by the Youden index was used to identify geochemical anomalies from the geochemical data points. Compared with the anomalies detected by the elliptic envelope models, the anomalies detected by the isolation forest models have higher spatial relationship with the mineral occurrences discovered in the study area. According to the results of this study and previous work, it can be inferred that the background population of the study area is complex, which is not suitable for the establishment of elliptic envelope model.

Automated summary

Isolation forest and elliptic envelope models were used to detect geochemical anomalies, with the bat algorithm optimizing the parameters. The bat-optimized models showed improved performance in geochemical anomaly detection, with the optimal threshold determined by the Youden index. Compared to anomalies detected by the elliptic envelope models, anomalies detected by the isolation forest models exhibited higher spatial relationship with mineral occurrences.