Applications of Deep Learning in Mineral Discrimination:A Case Study of Quartz,Biotite and K-Feldspar from Granite

作     者：Wei Lou Dexian Zhang Wei Lou;Dexian Zhang

作者机构：Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitor(Central South University)MinistryofEducationChangsha 410083China School of Geosciences&Info-PhysicsCentral SouthUniversity Changsha 410083China 

出 版 物：《Journal of Earth Science》 (地球科学学刊(英文版))

年 卷 期：2025年第36卷第1期

页      面：29-45页

核心收录：

学科分类：0709[理学-地质学] 070901[理学-矿物学、岩石学、矿床学] 07[理学] 

基　　金：funded by the National Natural Science Foundation of China(Nos.41672082 42030809) 

主　　题：deep learning mineral discrimination Inception-v3 CNN transfer learning convolutional neural network 

摘      要：Mineral identification and discrimination play a significant role in geological *** mineral discrimination based on deep learning has the advantages of automation,low cost,less time consuming and low error *** this article,characteristics of quartz,biotite and Kfeldspar from granite thin sections under cross-polarized light were studied for mineral images intelligent classification by Inception-v3 deep learning convolutional neural network(CNN),and transfer learning *** images from multi-angles were employed to enhance the accuracy and reproducibility in the process of mineral *** results show that the average discrimination accuracies of quartz,biotite and K-feldspar are 100.00%,96.88%and 90.63%.Results of this study prove the feasibility and reliability of the application of convolution neural network in mineral images *** study could have a significant impact in explorations of complicated mineral intelligent discrimination using deep learning methods and it will provide a new perspective for the development of more professional and practical mineral intelligent discrimination tools.