Gökhan Külekçi, Kemal Hacıefendioğlu, and Hasan Basri Başağa, Enhancing mineral processing with deep learning: Automated quartz identification using thin section images, Int. J. Miner. Metall. Mater., 32(2025), No. 4, pp.802-816. https://dx.doi.org/10.1007/s12613-024-3048-8
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石英因其广泛的分布和重要的工业价值，其精确识别在矿物学和地质学中至关重要。传统的石英薄片鉴定方法耗时长且需要高度的专业知识，而且常常因其他矿物的共存而变得复杂。本研究提出了一种新颖的方法，利用深度学习与高光谱成像技术相结合来自动识别石英矿。采用四种先进的深度学习模型——PSPNet、U-Net、FPN和LinkNet，在效率和准确性方面取得了显著进步。这些模型中，PSPNet表现出优越的性能，获得了最高的IoU（Intersection over union）分数，并在复杂场景下也展现出在石英矿分割方面的可靠性。本研究的数据集来自20个岩石样本的120个薄片共2470张高光谱图像。模型训练采用了专家审核掩膜，确保了分割结果的鲁棒性。这种自动方法不仅加快了识别过程，还提高了可靠性，为地质学家提供了一个有实用价值的工具，推动矿物学分析领域的发展。
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. The utilizied four advanced deep learning models—PSPNet, U-Net, FPN, and LinkNet—has significant advancements in efficiency and accuracy. Among these models, 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.
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