Hybrid Harris hawks-optimized random forest model for detecting multi-element geochemical anomalies related to mineralization.

Reliable recognition of geochemical anomalies linked to ore deposits is one of the most significant challenges in mineral exploration. Several advanced machine learning (AML) algorithms have recently been applied to recognize multi-element geochemical anomalies. Performance of the AML algorithms are extremely dependent to values of their hyperparameters. Because, conclusions of their application can significantly be differed tuning hyperparameters. Tuning hyperparameters through trial-and-error way is a labor-intensive and time-consuming procedure which is not mostly eventuated to reliable results. In this regard, applying an AML model decreases training time and assists to achieve optimized values of hyperparameters yielding reasonable potential maps. Hence, execution of an AML model mitigates the biasness problem and uncertainties with recognition of multi-element geochemical anomalies. In this study, Harris hawks optimization (HHO) algorithm was employed to optimize known hyperparameters of the random forest (RF) method for detecting multi-element geochemical anomalies related to mineralization occurrences in the Feyzabad district of the Razavi Khorasan province, NE Iran. This research demonstrates that Harris hawks optimized random forest (HHORF) model is a vigorous procedure to identify multi-element geochemical anomalies. Because, the HHORF model has recognized 86.53% mineralization occurrences through 30% corresponding area while the RF method has catched 80.14% mineralization occurrences up via same corresponding area.