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Research Article | Publishing Language: Chinese | Open Access

Research on fine-grained classification of rare metal granite lithology based on deep learning

Hengqian ZHAO1,2,3, Pan WANG1, Zhiguo LIU1( ), Qunfeng MIAO4, Zhibin LI5, Guanglong TANG1, Yunfei QI5, Yu XIE1, Mengmeng WANG1
College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology, Xuzhou Jiangsu 221116, China
Hebei Key Laboratory of Mineral Resources and Ecological Environment Monitoring, Baoding Hebei 071051, China
Fourth Geological Brigade of Hebei Bureau of Geology and Mineral Resources Exploration (Hebei Provincial Water Conservation Research Center), Chengde Hebei 067000, China
Eighth Geological Brigade of Hebei Bureau of Geology and Mineral Resources Exploration (Hebei Center of Marine Geological Resources Survey), Qinhuangdao Hebei 066000, China
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Abstract

Fine-grained image classification has high research value and application prospects in practical applications. At present, the traditional lithology fine-grained classification method is highly subjective and time-sensitive, depending on the experience of researcher and the quality of experimental equipment. Therefore, in this paper, the technology of fine-grained image classification is introduced into the field of granite lithology identification. The RGB image datasets of four types of lithology, namely flesh-red, grayish-white, iron-manganese, and amazonite-bearing alkali feldspar granites, are systematically constructed. Comparative experiments were carried out using typical deep learning models such as AlexNet, VGG16, ResNet50 and Vision Transformer. The results show that the classification accuracy of all models exceeds 82 %, and the VGG16 model is the best, which is 88.57 %, an increase of 5.71 % over the AlexNet model; the amazonite-bearing alkali feldspar granite has a recognition accuracy of 100 % due to its significant characteristic minerals, while the grayish-white alkali-feldspar granite has the worst recognition effect; the model accuracy is positively correlated with the amount of training samples, and the model performance is optimal when the training set is complete. In the future, the accuracy of fine-grained classification of rare metal granite lithology can be further improved by improving the quantity and quality of rock samples and optimizing the model algorithm.

Keywords

rare metal granitelithology classificationfine-grained classificationdeep learningRGB image
CLC number: TD15;P588 Document code: A Article ID: 2096-2193(2025)03-0408-10

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Journal of Mining Science and Technology
Volume 10 Issue 3,
June 2025
Pages 408-417
DOI: 10.19606/j.cnki.jmst.2025012
Cite this article:
ZHAO H, WANG P, LIU Z, et al. Research on fine-grained classification of rare metal granite lithology based on deep learning. Journal of Mining Science and Technology, 2025, 10(3): 408-417. https://doi.org/10.19606/j.cnki.jmst.2025012

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Received: 17 May 2024
Revised: 21 December 2024
Published: 30 June 2025
© The Author(s) 2025

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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