Meticulous evaluation of rock mass quality in mine engineering based on machine learning of core photos
LIU Fei-yue1, LIU Yi-han2, YANG Tian-hong1, XIN Jun-chang2, ZHANG Peng-hai1, DONG Xin1, ZHANG Hai-tao3
1. Center for Rock Instability and Seismicity Research, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China; 2. School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China; 3. China Gold Group Inner Mongolia Mining Co., Ltd., Manzhouli 021400, China
Abstract:In mining engineering, the geological drilling boreholes are used to obtain accurate reserves of mineral resources, and many core photos are gathered in this process. It has a practical engineering significance to get the structural information from those core photos in order to evaluate rock mass quality. However, the current manual method for geological borehole logging is inefficient, and the results are usually affected by subjective factors. A method for evaluation of rock mass quality is proposed using the Mask-RCNN deep learning instance segmentation network. Firstly, the core strips are cut from the core photos automatically, and the core segments longer than 10 cm are identified from those core strips, then the rock quality designation RQD is calculated. Finally, using the information of boreholes and the geological model, the ordinary Kriging method is employed to get a heterogenous RQD block model to achieve a meticulous evaluation of rock mass quality. The case study in Wushan Copper and Molybdenum Mine indicates that the machine learning method can accurately calculate the RQD from core photos, and the geostatistical method can effectively evaluate the rock mass quality. The results show that the rock mass quality evaluation based on deep learning is consistent with the actual situation, and the proposed method has a wide range of application prospects in mining engineering.
刘飞跃, 刘一汉, 杨天鸿, 信俊昌, 张鹏海, 董鑫, 张海涛. 基于岩芯图像深度学习的矿山岩体质量精细化评价[J]. 岩土工程学报, 2021, 43(5): 968-974.
LIU Fei-yue, LIU Yi-han, YANG Tian-hong, XIN Jun-chang, ZHANG Peng-hai, DONG Xin, ZHANG Hai-tao. Meticulous evaluation of rock mass quality in mine engineering based on machine learning of core photos. Chinese J. Geot. Eng., 2021, 43(5): 968-974.
[1] LIU F Y, YANG T H, ZHOU J R, et al.Spatial variability and time decay of rock mass mechanical parameters: a landslide study in the Dagushan open-pit mine[J]. Rock Mechanics and Rock Engineering. 2020, 53(7): 3031-3053. [2] STAVROPOULOU M, EXADAKTYLOS G, SARATSIS G.A combined three-dimensional geological geostatistical numerical model of underground excavations in rock[J]. Rock Mechanics and Rock Engineering, 2007, 40(3): 213-243. [3] GUO J T, WU L X, ZHANG M M, et al.Towards automatic discontinuity trace extraction from rock mass point cloud without triangulation[J]. International Journal of Rock Mechanics and Mining Sciences, 2018, 112: 226-237. [4] RIQUELME A, ABELLAN A, TOMAS R, et al.A new approach for semi-automatic rock mass joints recognition from 3D point clouds[J]. Computers and Geosciences, 2014, 68: 38-52. [5] 詹伟, 章杨松. 基于图像处理的岩体结构面迹线半自动检测[J]. 岩土工程技术, 2020, 34(1): 7-12. (ZHAN Wei, ZHANG Yang-song.Semi-automatic detection of rock mass discontinuity trace based on image processing[J]. Geotechnical Engineering Technique, 2020, 34(1): 7-12. (in Chinese)) [6] 邹先坚, 王川婴, 韩增强, 等. 全景钻孔图像中结构面全自动识别方法研究[J]. 岩石力学与工程学报, 2017, 36(8): 1910-1920. (ZHOU Xian-jian, WANG Chuan-ying, HAN Zeng-qiang, et al.Fully automatic identifying the structural planes with panoramic images of boreholes[J]. Chinese Journal of Rock Mechanics and Engineering, 2017, 36(8): 1910-1920. (in Chinese)) [7] 周永章, 王俊, 左仁广, 等. 地质领域深度学习、深度学习及实现语言[J]. 岩石学报, 2018, 34(11): 3173-3178. (ZHOU Yong-zhang, WANG Jun, ZUO Ren-guang, et al.Machine learning, deep learning and Python language in field of geology[J]. Acta Petrologica Sinica, 34(11): 3173-3178. (in Chinese)) [8] ZHANG K, HOU R B, ZHANG G H, et al.Rock drillability assessment and lithology classification based on the operating parameters of a drifter: case study in a coal mine in China[J]. Rock Mechanics and Rock Engineering, 2016, 49(1): 329-334. [9] 李明超, 符家科, 张野, 等. 耦合岩石图像与锤击音频的岩性分类智能识别分析方法[J]. 岩石力学与工程学报, 2020, 39(5): 996-1004. (LI Ming-chao, FU Jia-ke, ZHANG Ye, et al.Intelligent recognition and analysis method of rock lithology classification based on coupled rock images and hammering audios[J]. Chinese Journal of Rock Mechanics and Engineering, 2020, 39(5): 996-1004. (in Chinese)) [10] 王鹏宇, 王述红. 四类常见边坡岩石类别识别和边界范围确定的方法[J]. 岩土工程学报, 2019, 41(8): 1505-1512. (WANG Peng-yu, WANG Shu-hong.Method for identifying four common rock types of slopes and determining boundary range[J]. Chinese Journal of Geotechnical Engineering, 2019, 41(8): 1505-1512. (in Chinese)) [11] 张夏林, 师志龙, 吴冲龙, 等. 基于移动设备的野外地质大数据智能采集和可视化技术[J]. 地质科技通报, 2020, 39(4): 21-28. (ZHANG Xia-lin, SHI Zhi-long, WU Chong-long, et al.Intelligent data acquisition and visualization technology of field geology based on mobile device[J]. Bulletin of Geological Science and Technology, 2020, 39(4): 21-28. (in Chinese)) [12] 郑帅, 姜谙男, 张峰瑞, 等. 基于深度学习与可靠度算法的围岩动态分级方法及其工程应用[J]. 岩土力学, 2019, 40(增刊1): 308-318. (ZHENG Shuai, JIANG An-nan, ZHANG Rui-feng, et al.Dynamic classification method of surrounding rock and its engineering application based on machine learning and reliability algorithm[J]. Rock and Soil Mechanics, 2019, 40(S1): 308-318. (in Chinese)) [13] 柳厚祥, 李汪石, 查焕奕, 等. 基于深度学习技术的公路隧道围岩分级方法[J]. 岩土工程学报, 2018, 40(10): 1809-1817. (LIU Hou-xiang, LI Wang-shi, ZHA Zhuan-yi, et al.Method for surrounding rock mass classification of highway tunnels based on deep learning technology[J]. Chinese Journal of Geotechnical Engineering, 2018, 40(10): 1809-1817. (in Chinese)) [14] 王运敏. 现代采矿手册[M]. 北京: 冶金工业出版社, 2011. (WANG Yun-min.Modern Mining Manual[M]. Beijing: Metallurgical Industry Press, 2011. (in Chinese)) [15] MAYER J M, STEAD D.A comparison of traditional, step-path, and geostatistical techniques in the stability analysis of a large open pit[J]. Rock Mechanics and Rock Engineering, 2017, 50: 927-949. [16] EIVAZY H, ESMAIELI K, JEAN R Modeling geomechanical heterogeneity of rock masses using direct and indirect geostatistical conditional simulation methods[J]. Rock Mechanics and Rock Engineering 2017, 50: 3175-3195. [17] HE K, GKIOXARI G, PIOTR D, et al. Mask R-CNN[C]//2017 IEEE International Conference on Computer Vision (ICCV), 2017, Venice: 2980-2988. [18] 陈昌彦, 王贵荣. 各类岩体质量评价方法的相关性探讨[J]. 岩石力学与工程学报, 2002, 21(12): 1894-1900. (CHEN Chang-yan, WANG Gui-rong.Discussion on the interrelation of various rock mass quality classification systems at home and abroad[J]. Chinese Journal of Rock Mechanics and Engineering, 2002, 21(12): 1894-1900. (in Chinese)) [19] SHEPHARD M S, GEORGES M K.Automatic three- dimensional mesh generation by the finite octree technique[J]. International Journal for Numerical Methods in Engineering, 1991, 32(4): 709-749.