耦合岩石图像与锤击音频的岩性分类智能识别分析方法

doi:10.13722/j.cnki.jrme.2019.0748

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摘要在野外地质勘探过程中，地质工程师一般通过岩石表面纹理、颜色和敲击岩石音频等信息，对岩性分类进行初步判断。基于专家经验和智能模式，提出一种耦合岩石图像与锤击音频的岩性分类深度学习与智能识别分析方法。该方法基于所采集的不同类别岩石图像和锤击音频数据，首先采用基于Inception-V3的迁移学习方法，对所采集的6类岩石图像进行深度学习与训练；然后利用回弹仪测定岩石强度，通过“阈值法”得到锤击音频片段，以回弹指数的平均值作为岩石强度值，建立波形和强度的SVM(support vector machine)回归模型，实现岩石表面强度预测；最后耦合岩石图像识别模型与岩石音频强度回归模型进行岩性分类智能识别，岩石种类识别准确率从83.5%(采用单纯的图像识别)提高到90.5%。采用该耦合模型不仅能有效识别岩石岩性分类，还能初步给出岩石表面强度，为野外工程地质勘察提供了新的辅助手段，有利于提高初期野外勘探的工作效率。

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	李明超
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关键词 ：岩石力学, 岩石岩性, 智能识别, 岩石图像, 岩石锤击音频, 深度学习, 迁移学习, 强度SVM回归

Abstract：In the process of field geological exploration，geological engineers generally make a preliminary judgment on the lithology classification through the information of rock surface texture，color and hammering audios. Based on experts¢ experience and intelligent mode，a deep learning and intelligent recognition analysis method for rock lithology classification is proposed by using coupled rock images and hammering audios. Firstly，the Transfer learning method based on Inception-V3 is used to carry out deep learning and training on collected 6 types of rock images. Then，measuring the rock strength indicated as the average value of the rebound index by a rebounder and obtaining hammering audio segments by the threshold method，an SVM (Support Vector Machine) regression model of waveform and intensity is established to predict the rock surface strength. Finally，rock classification is intelligently recognized by coupling rock images recognition model with rock audio intensity regression model. The accuracy of rock classification is increased from 83.5%(only using simple images recognition model) to 90.5%. The coupling model can not only effectively identify rock lithology classification but also preliminarily give the rock surface strength，which provides a new auxiliary method for field engineering geological survey and is beneficial to improve the working efficiency of initial field exploration.

Key words： rock mechanics rock lithology intelligent recognition rock images rock hammering audios deep learning transfer learning SVM regression of strength

引用本文:

李明超，符家科，张野，刘承照. 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 Mingchao，FU Jiake，ZHANG Ye，LIU Chengzhao. Intelligent recognition and analysis method of rock lithology classification based on coupled rock images and hammering audios#br#. , 2020, 39(5): 996-1004.

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http://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2019.0748 或 http://rockmech.whrsm.ac.cn/CN/Y2020/V39/I5/996

[17]	LECUN Y，BENGIO Y，HINTON G. Deep learning[J]. Nature，2015，521(7553)：436-444.
[27]	VAPNIK V. The Nature of statistical learning theory[M]. 2nd ed. New York：Springer，1999：181-190.
[19]	郭超，刘烨. 多色彩空间下的岩石图像识别研究[J]. 科学技术与工程，2014，14(18)：247-251.(GUO Chao，LIU Ye. Recognition of Rock Images Based on Multiple Color Spaces[J]. Science Technology and Engineering，2014，14(18)：247-251.(in Chinese))
[26]	PAN S J，YANG Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering，2009，22(10)：1 345-1 359.
[1]	佘诗刚，林鹏. 中国岩石工程若干进展与挑战[J]. 岩石力学与工程学报，2014，33(3)：433-457.(SHE Shigang，LIN Peng. Some developments and challenging issues in rock engineering field in China[J]. Chinese Journal of Rock Mechanics and Engineering，2014，33(3)：433-457.(in Chinese))
[8]	汪洋. 北京白查A型花岗岩的地球化学特征及其成因与构造指示意义[J]. 岩石学报，2009，25(1)：13-24.(WANG Yang. Geochemistry of the baicha a-type granite in Beijing municipality：petrogenetic and tectonic implications[J]. Acta Petrologica Sinica，2009，25(1)：13-24.(in Chinese))
[9]	冉明佳，钟康惠，罗明非，等. 西藏冈底斯带东段石炭纪构造环境讨论[J]. 地质论评，2012，58(2)：250-258.(RAN Mingjia，ZHONG Kanghui，LUO Mingfei，et al. A discussion on carboniferous tectonic settings in the eastern sector of gangdise belt，Xizang(Tibet)[J]. Geological Review，2012，58(2)：250-258.(in Chinese))
[11]	刘烨，程国建，马微，等. 基于铸体薄片图像颜色空间与形态学梯度的岩石分类[J]. 中南大学学报：自然科学版，2016，47(7)：2 375-2 382.(LIU Ye，CHENG Guojian，MA Wei，et al. Rock classification based on features form color space and morphological gradient of rock thin section image[J]. Journal of Central South University：Science and Technology，2016，47(7)：2 357-2 382.(in Chinese))
[18]	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.(in Chinese))
[21]	SINGH T N，KAINTHOLA A，VENKATESH A. Correlation between point load index and uniaxial compressive strength for different rock types[J]. Rock Mechanics and Rock Engineering，2012，45(2)：259-264.
[3]	HACK R，HUISMAN M. Estimating the intact rock strength of a rock mass by simple means[C]// Proceedings of the 9th Congress of the International Association for Engineering Geology and the Environment. Durban，South Africa：[s. n.]，2002：1 971-1 977.
[5]	王子娟，刘新荣，傅晏，等. 两种岩石试件的“超声-回弹-密度”综合筛选法研究[J]. 岩石力学与工程学报，2018，37(增1)：3 575-3 583.(WANG Zijuan，LIU Xinrong，Fu Yan，et al. Comprehensive screening method of “ultrasonic-rebound-density”for two kinds of rock specimens[J]. Chinese Journal of Rock Mechanics and Engineering，2018，37(Supp.1)：3 575-3 583.(in Chinese))
[7]	郭清宏，周永章，曹姝旻，等. 广绿玉玉石的矿物学研究[J]. 中山大学学报(自然科学版)，2010，49(3)：146-151.(GUO Qinghong，ZHOU Yongzhang，CAO Shumin，et al. Study on Mineralogy of Guangning Jade[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni，2010，49(3)：146-151.(in Chinese))
[10]	高阳，王永诗，李孝军，等. 基于岩石孔喉结构的致密砂岩分类方法——以济阳坳陷古近系为例[J]. 油气地质与采收率，2019，26(2)：32-41.(GAO Yang，WANG Yongshi，LI Xiaojun，et al. Classification method of tight sandstone based on pore throat structure：a case study of paleogene in Jiyang depression[J]. Petroleum Geology and Recovery Efficiency，2019，26(2)：32-41.(in Chinese))
[13]	ESTEVA A，KUPREL B，NOVOA RA，et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature，2017，542(7639)：115-118.
[15]	SIMONYAN K，ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]// 3rd International Conference on Learning Representations. San Diego，CA：[s. n.]，2015：1-14.
[20]	KAMANI M，AJALLOEIAN R. Evaluation of the mechanical degradation of carbonate aggregate by rock strength tests[J]. Journal of Rock Mechanics and Geotechnical Engineering，2019，11(1)：121-134.
[23]	TSIAMBAOS G ，SABATAKAKIS N . Considerations on strength of intact sedimentary rocks[J]. Engineering Geology，2004，72(3/4)：261-273.
[25]	周胜，刘三民. 基于迁移学习的数据流分类研究综述[J]. 天津理工大学学报，2019，35(3)：24-29.(ZHOU Sheng，LIU Sanmin. A survey of data stream classification research based on transfer learning[J]. Journal of Tianjin University of Technology，2019，35(3)：24-29.(in Chinese))
[4]	MARINOS P，HOEK E. Estimating the geotechnical properties of heterogeneous rock masses such as flysch[J]. Bulletin of engineering geology and the environment，2001，60(2)：85-92.
[6]	樊磊，赵文吉，宫兆宁，等. 基于包络线消除法的岩石光谱对应分析[J]. 吉林大学学报：地球科学版，2012，42(2)：575-582.(FAN Lei，ZHAO Wenji，GONG Zhaoning，et al. Correspondence analysis of rock spectra based on continuum removing[J]. Journal of Jilin University：Earth Science Edition，2012，42(2)：575-582.(in Chinese))
[14]	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. Las Vegas：[s. n.]，2016：2 818-2 826.
[16]	KRIZHEVSKY A，SUTSKEVER I，HINTON G E. Imagenet classification with deep convolutional neural networks[C]// Advances in Neural Information Processing Systems. Lake Tahoe，NV：[s. n.]，2012：1 097-1 105.
[24]	REN Q，WANG G，LI M，et al. Prediction of rock compressive strength using machine learning algorithms based on spectrum analysis of geological hammer[J]. Geotechnical and Geological Engineering，2018，(3)：1-15.
[2]	柴华，李宁，夏守姬，等. 高清晰岩石结构图像处理方法及其在碳酸盐岩储层评价中的应用[J]. 石油学报，2012，33(增2)：154-159.(CHAI Hua，LI Ning，XIA Shouji，et al. High-resolution rock structure image processing method and its applications in carbonate reservoir evaluation[J]. Acta Petrolei Sinica，2012，33(Supp.2)：154-159.(in Chinese))
[12]	BIANCO S，BUZZELLI M，MAZZINI D，et al. Deep learning for logo recognition[J]. Neurocomputing，2017，245：23-30.
[22]	SINGH V K ，SINGH D P . Correlation between point load index and compressive strength for quartzite rocks[J]. Geotechnical and Geological Engineering，1993，11(4)：269-272.