This study reveals high-potential metallogenic zones in Iran through machine learning and geospatial data integration, enhancing exploration strategies.
Key Points
The ensemble bagged trees method achieved the best predictive accuracy for mineral deposits, achieving low RMSE and high R².
A training dataset with 69 geological features helped predict areas of high mineral density across the Iranian plateau.
Residual spatial density anomalies highlighted several promising ore zones, particularly near the Malayer-Isfahan Pb-Zn zone.
This AI-enhanced approach provides a strategic foundation for national-scale mining exploration in Iran, aiming to improve efficiency.