Seeing Ore Before Breaking Rock: Geophysical–Metallurgical Fusion for Predicting Mineral Processability in the Subsurface

Abstract

Mineral exploration success is traditionally measured by ore discovery and grade estimation, while metallurgical performance grindability, liberation, recovery efficiency, and energy demand is assessed only after extensive drilling and sampling. This sequential workflow introduces substantial technical and economic risk, particularly where ores exhibit refractory behavior despite favorable grades. This study presents a geophysical–metallurgical fusion framework aimed at predicting mineral processability directly from subsurface geophysical responses, prior to physical excavation. The methodology integrates multi-physics geophysical imaging (seismic, electrical resistivity, induced polarization, and magnetic data) with mineralogical and metallurgical descriptors through data-driven correlation and physics-informed interpretation. Geophysical attributes sensitive to mineral texture, alteration intensity, grain connectivity, and fracture networks are identified and quantitatively linked to key metallurgical indicators including Bond Work Index, liberation size, recovery efficiency, and reagent consumption. Results demonstrate that distinct geophysical signatures correspond to predictable processability classes, enabling the delineation of easy-to-process, moderately refractory, and highly refractory ore zones within the subsurface. The proposed framework establishes a pathway for incorporating metallurgical intelligence into early-stage exploration, reducing uncertainty in mine planning, energy demand estimation, and processing route selection. The study advances exploration geophysics from ore detection toward process-aware subsurface characterization.