Machine-Learning Integration of Geophysical Responses for Predicting Metallurgical and Environmental Indicators in Mineral Systems

Abstract

The sustainability of mineral development increasingly depends on early understanding of ore processability and environmental behavior rather than ore grade alone. This study presents a data-driven framework that integrates multi-physics geophysical data (magnetic, electrical resistivity, induced polarization, and electromagnetic) with machine-learning models to predict metallurgical and environmental indicators at the exploration stage. Geophysical attributes were derived from datasets covering 187 mining locations within a Precambrian basement terrain in southwestern Nigeria. Proxy indicators for mineral liberation, recovery potential, comminution behavior, and environmental risk were developed based on established geophysical–mineralogical relationships. Supervised machine-learning models (random forest and gradient boosting) were trained and evaluated using cross-validation. The models achieved classification accuracies ranging between approximately 75% and 82% across key indicator classes, demonstrating that geophysical signatures—particularly chargeability and resistivity contrasts—provide meaningful predictive insight into subsurface processability and environmental response. However, predictions remain proxy-based and do not replace direct metallurgical testing. The framework offers a scalable approach for integrating processability and environmental considerations into early-stage mineral exploration, especially in data-scarce and artisanal mining contexts.