Comparative machine learning analysis for gold mineral prediction using random forest and XGBoost: A data-driven study of the Greater Bendigo Region, Victoria

Azirahtul Atifah Mohamed Sabri, Sarath Tomy, Choiru Za’in,
Comparative machine learning analysis for gold mineral prediction using random forest and XGBoost: A data-driven study of the Greater Bendigo Region, Victoria,
Geomatica,
Volume 77, Issue 2,
2025,
100066,
ISSN 1195-1036,
https://doi.org/10.1016/j.geomat.2025.100066.
(https://www.sciencedirect.com/science/article/pii/S1195103625000229)
Abstract: Gold mineral exploration remains critical to supporting global industries, yet traditional methods relying on manual interpretation of geophysical data are increasingly inefficient and prone to error, particularly when targeting undercover deposits. In Australia, most exploration research has focused on Western Australia, while the Greater Bendigo region in Victoria remains underexplored using modern data-driven approaches, despite its rich mining history and availability of high-resolution geophysical datasets. This study aims to demonstrate that a geospatial analysis methodology based on a machine learning approach enables high-accuracy prediction of gold mineral deposits in Bendigo. The methodology integrates geophysical data, including gravity, total magnetic intensity, and radiometric surveys, combined with geospatial preprocessing, scalable multi-resolution modelling, spatial labelling, and ensemble machine learning techniques, using Random Forest as the primary algorithm and XGBoost as a comparative model. Model performance was assessed using accuracy scores, ROC-AUC metrics, and spatial validation methods, including checkerboard and cluster-based cross-validation, across different spatial scales. Results showed that gravity and magnetic features were the strongest predictors, while radiometric features provided supporting information. Coarser spatial resolutions produced more stable predictions, reflecting regional geological patterns. The study presents a reproducible and adaptable machine learning methodology that addresses key exploration challenges and advances mineral prospectivity analysis using open-access geophysical data.
Keywords: Machine learning; Random Forest; XGBoost; Gold mineralisation; Bendigo; Geophysical data; Mineral exploration