Published Paper Details:

Mineral exploration in India continues to face the dual challenge of balancing economic urgency with the complexity of its vast and geologically diverse terrain. This study introduces a spatial predictive modeling framework using Geographic Information Systems (GIS) to identify high-potential mineral prospecting zones across selected regions in India. By integrating multi-layered spatial datasets--ranging from lithological, structural, and topographical variables to satellite-derived remote sensing indices--and employing a Random Forest classification model, the research develops a high-resolution mineral potential map calibrated against known mineral occurrences. The results reveal statistically significant spatial patterns, highlighting the predictive value of factors such as proximity to fault lines, lithological transitions, and elevation gradients. This approach not only reduces exploratory costs and environmental disruption but also provides a replicable model for mineral intelligence in resource-rich but data-fragmented environments. Ultimately, the study supports a paradigm shift toward data-driven, precision exploration strategies within India's mineral governance landscape.