Enhancing mineral processing with deep learning: Automated quartz identification using thin section images

Abstract

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.

Keywords

quartz mineral identification / deep learning / hyperspectral imaging / deep learning in geology

References

[1] ChenWT, LiXJ, WangLZRemote Sensing Intelligent Interpretation for Mine Geological Environment: From Land Use and Land Cover Perspective, 2022, Singapore, Springer Nature

[2] M. Tian, K. Ma, Z.H. Liu, Q.J. Qiu, Y.J. Tan, and Z. Xie, Recognition of geological legends on a geological profile via an improved deep learning method with augmented data using transfer learning strategies, Ore Geol. Rev., 153(2023), art. No. 105270.

[3] AgrawalN, GovilH, ChatterjeeS, MishraG, MukherjeeS. Evaluation of machine learning techniques with AVIRIS–NG dataset in the identification and mapping of minerals. Adv. Space Res., 2024, 73(2): 1517

[4] WangHJ. Intelligent identification of logging cuttings based on deep learning. Energy Rep., 2022, 8: 1

[5] AgrawalN, GovilH, MishraG, GuptaM, SrivastavaPK. Evaluating the performance of prisma shortwave infrared imaging sensor for mapping hydrothermally altered and weathered minerals using the machine learning paradigm. Remote Sens., 2023, 15(12): 3133

[6] A. Gomez-Flores, S. Ilyas, G.W. Heyes, and H. Kim, A critical review of artificial intelligence in mineral concentration, Miner. Eng., 189(2022), art. No. 107884.

[7] X. Liu, V. Chandra, A.I. Ramdani, R. Zuhlke, and V. Vahrenkamp, Using deep-learning to predict Dunham textures and depositional facies of carbonate rocks from thin sections, Geoenergy Sci. Eng., 227(2023), art. No. 211906.

[8] R. Pires de Lima, D. Duarte, C. Nicholson, R. Slatt, and K.J. Marfurt, Petrographic microfacies classification with deep convolutional neural networks, Comput. Geosci., 142(2020), art. No. 104481.

[9] N. Saxena, R.J. Day-Stirrat, A. Hows, and R. Hofmann, Application of deep learning for semantic segmentation of sandstone thin sections, Comput. Geosci., 152(2021), art. No. 104778.

[10] ZuoRG, XiongYH, WangJ, CarranzaEJM. Deep learning and its application in geochemical mapping. Earth Sci. Rev., 2019, 192: 1

[11] ChenWW, TongDQ, ZhangSC, ZhangXL, ZhaoHM. Local PM₁₀ and PM_2.5 emission inventories from agricultural tillage and harvest in northeastern China. J. Environ. Sci., 2017, 57: 15

[12] XuZH, MaW, LinP, HuaYL. Deep learning of rock microscopic images for intelligent lithology identification: Neural network comparison and selection. J. Rock Mech. Geotech. Eng., 2022, 14(4): 1140

[13] ChenWT, OuyangSB, YangJW, LiXJ, ZhouGD, WangLZ. JAGAN: A framework for complex land cover classification using Gaofen-5 AHSI images. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022, 15: 1591

[14] AliD, FrimpongS. Artificial intelligence, machine learning and process automation: Existing knowledge frontier and way forward for mining sector. Artif. Intell. Rev., 2020, 53(8): 6025

[15] T. Long, Z.B. Zhou, G. Hancke, Y. Bai, and Q. Gao, A review of artificial intelligence technologies in mineral identification: Classification and visualization, J. Sens. Actuator Network, 11(2022), No. 3, art. No. 50.

[16] T. Sun, H. Li, K.X. Wu, F. Chen, Z. Zhu, and Z.J. Hu, Datadriven predictive modelling of mineral prospectivity using machine learning and deep learning methods: A case study from southern Jiangxi province, China, Minerals, 10(2020), No. 2, art. No. 102.

[17] H.J. Zhao, K.W. Deng, N. Li, Z.W. Wang, and W. Wei, Hierarchical spatial-spectral feature extraction with long short term memory (LSTM) for mineral identification using hyperspectral imagery, Sensors, 20(2020), No. 23, art. No. 6854.

[18] AgrawalN, GovilH. A deep residual convolutional neural network for mineral classification. Adv. Space Res., 2023, 71(8): 3186

[19] ChenYS, LinZH, ZhaoX, WangG, GuYF. Deep learning-based classification of hyperspectral data. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2014, 7(6): 2094

[20] LiDY, LiuZD, ZhuQQ, ZhangCX, XiaoP, MaJY. Quantitative identification of mesoscopic failure mechanism in granite by deep learning method based on SEM images. Rock Mech. Rock Eng., 2023, 56(7): 4833

[21] LiuZD, LiDY, ZhuQQ, ZhangCX, MaJY, ZhaoJJ. Intelligent method to experimentally identify the fracture mechanism of red sandstone. Int. J. Miner. Metall. Mater., 2023, 30(11): 2134

[22] E.J.Y. Koh, E. Amini, G.J. McLachlan, and N. Beaton, Utilising convolutional neural networks to perform fast automated modal mineralogy analysis for thin-section optical microscopy, Miner. Eng., 173(2021), art. No. 107230.

[23] LiuH, RenYL, LiX, et al.. Rock thin-section analysis and identification based on artificial intelligent technique. Pet. Sci., 2022, 19(4): 1605

[24] ChenWL, JiCN, XuD, SrinilN. Wake patterns of freely vibrating side-by-side circular cylinders in laminar flows. J. Fluids Struct., 2019, 89: 82

[25] T.E. Oliphant, Guide to NumPy, [2024–08–20], https://csc.ucdavis.edu/~chaos/courses/nlp/Software/NumPyBook.pdf

[26] HunterJD. Matplotlib: A 2D graphics environment. Comput. Sci. Eng., 2007, 9(3): 90

[27] Keras-Resources, GitHub [2024–08–20], https://github.com/fchollet/keras-resources.

[28] Segmentation Models, GitHub [2024–08–20], https://github.com/qubvel/segmentation_models.

[29] ZhaoHS, ShiJP, QiXJ, WangXG, JiaJY. Pyramid scene parsing network. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 20176230Honolulu

[30] RonnebergerO, FischerP, BroxT. U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-assisted Intervention—MICCAI 2015: 18th International Conference, 2015234Munich

[31] LinTY, DollárP, GirshickR, HeKM, HariharanB, BelongieS. Feature pyramid networks for object detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017936Honolulu

[32] ChaurasiaA, CulurcielloE. LinkNet: Exploiting encoder representations for efficient semantic segmentation. 2017 IEEE Visual Communications and Image Processing (VCIP), 20171St. Petersburg

[33] HuJX, LiL, LinYJ, WuFG, ZhaoJS. A comparison and strategy of semantic segmentation on remote sensing images. 15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, 201921Kunming

[34] HeKM, ZhangXY, RenSQ, SunJ. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016770Las Vegas

RIGHTS & PERMISSIONS

University of Science and Technology Beijing