Geological and Alteration Mapping of Pit Walls at Gold Bar Mine Using Data-Driven Classification of Hyperspectral Imaging

In open-pit mining, accurately mapping lithology, alteration, and mineralogy is vital for optimizing ore grade estimation, processing, and operational planning. Traditional geological pit-wall mapping is time-consuming, labor-intensive, and exposes technical staff to hazardous conditions. This study investigates a data-driven, hyperspectral classification workflow for pit-wall mapping at an open-pit gold mine. Three classification techniques were applied: Spectral Angle Mapper (SAM) using scene-derived reference spectra, Autoencoder + K-means (AE), and Band Ratio + K-means (BR), without relying on predefined training labels. Hyperspectral imagery was acquired using a tripod-mounted system integrating VNIR (400–1000 nm) and SWIR (970–2500 nm) sensors from two pit walls of contrasting geological complexity, with SWIR data driving primary mineral classification and VNIR data used separately for iron oxide mapping. Results were validated against laboratory hyperspectral scanning and XRD analyses of collected rock samples, as well as expert geological interpretation. Across both pit walls, Across both pit walls, SAM and BR produced closely consistent, geologically reliable classifications, with SAM showing the strongest correspondence to reference spectra and confirmed mineral assemblages; AE achieved comparable polygon-level agreement but with reduced mineralogical specificity, tending toward broader, less distinct class groupings. The study demonstrates that close-range tripod-based hyperspectral imaging with data-driven classification provides a practical, rapid alternative to manual pit-wall mapping, improving accuracy, reducing interpretation time, and minimizing personnel exposure to hazardous conditions.