Enhancing Mineral Processing with Deep Learning: Automated Quartz Identification Using thin section Images

The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance. Traditional methods of quartz identification in thin sections are labor-intensive and require significant expertise, often complicated by the coexistence of other minerals. This study presents a novel approach leveraging deep learning techniques combined with hyperspectral imaging to automate the identification process of quartz minerals. Utilizing four advanced deep learning models—PSPNet, U-Net, FPN, and LinkNet—this method demonstrates significant advancements in efficiency and accuracy. Among these, PSPNet exhibited superior performance, achieving the highest Intersection over Union (IoU) scores and demonstrating exceptional reliability in segmenting quartz minerals, even in complex scenarios. The study involved a comprehensive dataset of 120 thin sections, encompassing 2470 hyperspectral images prepared from 20 rock samples. Expert-reviewed masks were used for model training, ensuring robust segmentation results. This automated approach not only expedites the recognition process but also enhances reliability, providing a valuable tool for geologists and advancing the field of mineralogical analysis.