Automated Mineral Grain Extraction for Geometallurgical Studies Using Segment Anything Model (SAM) and Core Scanning Techniques

In mineral exploration and geometallurgical studies, accurately segmenting mineral grains from core scanning datasets may be used to predict metal recovery. This study introduces the application of the Segment Anything Model (SAM), a cutting-edge deep learning tool, to automate the segmentation and extraction of mineral grains from Laser-Induced Breakdown Spectroscopy (LIBS) and hyperspectral core scanning datasets. SAM demonstrates high efficiency and precision in identifying mineral grains, forming the foundation for downstream analyses, including the evaluation of mineral associations, grain size distribution, and other key geometallurgical metrics. Through case studies on pegmatite deposits, this research showcases the potential of SAM to address challenges posed by mineralogically complex ore. By enabling detailed mineralogical characterisation and advancing geometallurgical methods, SAM-based grain extraction presents a transformative tool for supporting sustainable and efficient mining practices.