Isolation Forest as an Alternative Data-Driven Mineral Prospectivity Mapping Method with a Higher Data-Processing Efficiency

Mineral exploration targets can be delineated through multivariate analysis. These targets are usually recognized as anomalies in the procedure of data mining using a detection algorithm. In this paper, isolation forest, which is a data-mining algorithm for anomaly detection, was adapted to map gold prospectivity in the Laotudingzi-Xiaosiping district in Jilin Province, northeastern China. The performance of the isolation forest model was compared with those of restricted Boltzmann machine and logistic regression models for mapping gold prospectivity. The isolation forest model outperforms the restricted Boltzmann machine model but not the logistic regression model based on the receiver operating characteristic curve and area under the curve; however, the isolation forest model is much more efficient than the other two models in terms of data-processing efficiency. It takes 15.48, 254.74, and 57.33s, respectively, to integrate the predictor map layers using the three models. Gold mineral exploration targets were optimally delineated by using the Youden index to maximize spatial association between delineated gold mineral exploration targets and the discovered gold deposits. Gold mineral exploration targets predicted by the three models occupy 10.97, 10.36, and 8.40% of the study area and contain 88, 81, and 81% of the discovered gold deposits. Therefore, the isolation forest algorithm is a potentially useful mineral prospectivity mapping method.