Hyperspectral Mineralogical Characterization for Geometallurgy

The integration of hyperspectral characterization into the mining industry represents a transformative shift in how mineralogical and geometallurgical data are utilized for exploration, processing, and operational optimization. This review highlights the advancements in hyperspectral imaging technologies and their applications, including mineral identification, core logging, ore sorting, and geometallurgical modeling. Technologies such as HyLogger, Corescan, and SisuRock have proven instrumental in capturing spectral data across VNIR, SWIR, and TIR ranges, enabling detailed mineralogical mapping and enhanced process control. Case studies demonstrate the effectiveness of hyperspectral techniques in predicting key variables such as grinding media consumption, tailings rheology, and ore classification, with notable contributions from advanced spectral databases like TSG and HIDSaG. Despite challenges, including data variability and integration into workflows, the combination of hyperspectral imaging with machine learning offers significant opportunities to improve decision-making and sustainability in mining operations. This review underscores the importance of hyperspectral characterization as a critical tool in addressing the complex demands of modern mining.