Coal Geology & Exploration


The traditional on-site core identification and recording mainly rely on the experience of technicians, and there are many uncertain factors. Limited by the site conditions, using mobile phones or cameras to capture the natural images is the most convenient way to collect the core information. Therefore, it is necessary to study the feature information extraction technology of core image and apply it to the identification and prediction of core type and other information. Specifically, a large number of core samples were collected, the thin-section identification method was employed to determine the core types and names, and then the core images were taken under different lighting and scale conditions to form the training data sets of convolutional neural networks corresponding to the image and name markers. In order to solve the problem of the different identification accuracy generated by data augmentation and different training batches on different test datasets, a joint core identification method based on multiple training models was proposed, and multiple models were used simultaneously to identify the images so as to comprehensively determine the final identification results. Besides, 8 datasets were selected to test the model, and the accuracy of joint identification using multiple models (4 models were used herein) was improved by 20.34% at maximum (9.13% on average) compared to the single model without data augmentation, and 4.41% at maximum (2.75% on average) compared to the single model with data augmentation. In general, the identification accuracy for each test set is significantly improved, with a total identification accuracy of 91.56%. Hence, the proposed method effectively avoids the problem that a single model has the performance good for some datasets but poor for the others during the identification. In order to quickly use the core identification model in field drilling, a core identification mobile APP was developed using the TensorFlow Lite framework, so that the core images could be taken directly with the mobile phone at the scene for identification. The test results of geothermal exploration in Boye County, Baoding City, Hebei Province, show that APP has the field identification accuracy up to 85%, which is lower than that obtained in the laboratory test. This indicates that the shooting environment and core state in the field are more complex than that during laboratory testing, but it can still be used as an auxiliary tool to provide an important reference for field workers. The research shows that the identification accuracy of core images can be further improved by using the more complex convolutional neural networks, expanding the core image datasets, adopting the more effective data augmentation methods and strategies, and establishing a proprietary core identification model for a specific area, so as to provide more effective information for the decision-making of intelligent drilling.


natural image, core identification, deep learning, convolutional neural network, intelligent drilling




[1] 许振浩,马文,李术才,等. 岩性识别:方法、现状及智能化发展趋势[J]. 地质论评,2022,68(6):2290−2304.

XU Zhenhao,MA Wen,LI Shucai,et al. Lithology identification:Method,research status and intelligent development trend[J]. Geological Review,2022,68(6):2290−2304.

[2] MLYNARCZUK M,GORSZCZYK A,SLIPEK B. The application of pattern recognition in the automatic classification of microscopic rock images[J]. Computers & Geosciences,2013,60:126−133.

[3] 徐圣嘉,苏程,朱孔阳,等. 基于深度学习的岩石薄片矿物自动识别方法[J]. 浙江大学学报(理学版),2022,49(6):743−752.

XU Shengjia,SU Cheng,ZHU Kongyang,et al. Automatic identification of mineral in petrographic thin sections based on images using a deep learning method[J]. Journal of Zhejiang University (Science Edition),2022,49(6):743−752.

[4] 程国建,李碧,万晓龙,等. 基于SqueezeNet卷积神经网络的岩石薄片图像分类研究[J]. 矿物岩石,2021,41(4):94−101.

CHENG Guojian,LI Bi,WAN Xiaolong,et al. Research on classification of rock section image based on SqueezeNet convolutional neural network[J]. Mineralogy and Petrology,2021,41(4):94−101.

[5] FERREIRA A,GIRALDI G. Convolutional neural network approaches to granite tiles classification[J]. Expert Systems with Applications,2017,84:1−11.

[6] SHU Lei,MCLSAAC K,OSINSKI G R,et al. Unsupervised feature learning for autonomous rock image classification[J]. Computers & Geosciences,2017,106:10−17.

[7] 张野,李明超,韩帅. 基于岩石图像深度学习的岩性自动识别与分类方法[J]. 岩石学报,2018,34(2):333−342.

ZHANG Ye,LI Mingchao,HAN Shuai. Automatic identification and classification in lithology based on deep learning in rock images[J]. Acta Petrologica Sinica,2018,34(2):333−342.

[8] 马泽栋,马雷,李科,等. 基于岩石图像深度学习的多尺度岩性识别[J]. 地质科技通报,2022,41(6):316−322.

MA Zedong,MA Lei,LI Ke,et al. Multi–scale lithology recognition based on deep learning of rock images[J]. Bulletin of Geological Science and Technology,2022,41(6):316−322.

[9] 冯雅兴,龚希,徐永洋,等. 基于岩石新鲜面图像与孪生卷积神经网络的岩性识别方法研究[J]. 地理与地理信息科学,2019,35(5):89−94.

FENG Yaxing,GONG Xi,XU Yongyang,et al. Lithology recognition based on fresh rock images and twins convolution neural network[J]. Geography and Geo-Information Science,2019,35(5):89−94.

[10] XU Zhenhao,MA Wen,LIN Peng,et al. Deep learning of rock images for intelligent lithology identification[J]. Computers & Geosciences,2021,154:104799.

[11] XU Zhenhao,SHI Heng,LIN Peng,et al. Integrated lithology identification based on images and elemental data from rocks[J]. Journal of Petroleum Science and Engineering,2021,205:108853.

[12] 许振浩,马文,林鹏,等. 基于岩石图像迁移学习的岩性智能识别[J]. 应用基础与工程科学学报,2021,29(5):1075−1092.

XU Zhenhao,MA Wen,LIN Peng,et al. Intelligent lithology identification based on transfer learning of rock images[J]. Journal of Basic Science and Engineering,2021,29(5):1075−1092.

[13] LIU Xiaoyang,JING Wei,ZHOU Mingxuan,et al. Multi–scale feature fusion for coal–rock recognition based on completed local binary pattern and convolution neural network[J]. Entropy,2019,21(6):622.

[14] HOUSHMAND N,GOODFELLOW S,ESMAEILI K,et al. Rock type classification based on petrophysical,geochemical,and core imaging data using machine and deep learning techniques[J]. Applied Computing and Geosciences,2022,16:100104.

[15] JOUINI M S,KESKES N. Numerical estimation of rock properties and textural facies classification of core samples using X–ray computed tomography images[J]. Applied Mathematical Modelling,2017,41:562−581.

[16] 张川,叶发旺,徐清俊,等. 钻孔岩心高光谱技术系统及其在矿产勘查中的应用[J]. 地质科技情报,2016,35(6):177−183.

ZHANG Chuan,YE Fawang,XU Qingjun,et al. Drill core hyperspectral technology system and its application in mineral prospecting[J]. Geological Science and Technology Information,2016,35(6):177−183.

[17] WANG Chunling,LI Yan,FAN Guangpeng,et al. Quick recognition of rock images for mobile applications[J]. Journal of Engineering Science & Technology Review,2018,11(4):111−117.

[18] FAN Guangpeng,CHEN Feixiang,CHEN Danyu,et al. Recognizing multiple types of rocks quickly and accurately based on lightweight CNNs model[J]. IEEE Access,2020,8:55269−55278.

[19] FAN Guangpeng,CHEN Feixiang,CHEN Danyu,et al. A deep learning model for quick and accurate rock recognition with smartphones[J]. Mobile Information Systems,2020,2020:7462524.

[20] HE Kaiming,ZHANG Xiangyu,REN Shaoqing,et al. Identity mappings in deep residual networks[C]//Computer Vision–ECCV 2016:14th European Conference,Amsterdam,The Netherlands,October 11–14,2016,Proceedings,Part IV 14. Springer International Publishing,2016:630–645.

[21] SZEGEDY C,VANHOUCKE V,IOFFE S,et al. Rethinking the inception architecture for computer vision[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2016:2818–2826.

[22] CHOLLET F. Xception:Deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2017:1251–1258.

[23] SANDLER M,HOWARD A,ZHU Menglong,et al. MobileNetV2:Inverted residuals and linear bottlenecks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2018:4510–4520.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.