Volume 28 Issue S1
Oct.  2020
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PENG Ming, WANG Dengyi, LIU Liu, LIU Chengcheng, LU Cuican. 2020: INTELLIGENT RECOGNITION OF LAYERED GEOLOGICAL BODY BASED ON MACHINE LEARNING AND SEISMIC EXPLORATION. JOURNAL OF ENGINEERING GEOLOGY, 28(S1): 230-236. doi: 10.13544/j.cnki.jeg.2020-280
Citation: PENG Ming, WANG Dengyi, LIU Liu, LIU Chengcheng, LU Cuican. 2020: INTELLIGENT RECOGNITION OF LAYERED GEOLOGICAL BODY BASED ON MACHINE LEARNING AND SEISMIC EXPLORATION. JOURNAL OF ENGINEERING GEOLOGY, 28(S1): 230-236. doi: 10.13544/j.cnki.jeg.2020-280

INTELLIGENT RECOGNITION OF LAYERED GEOLOGICAL BODY BASED ON MACHINE LEARNING AND SEISMIC EXPLORATION

doi: 10.13544/j.cnki.jeg.2020-280
Funds:

The research is supported by the National Natural Science Foundation of China (Grant Nos.41731283, 41877234) and the Fundamental Research Funds for the Central Universities (Grant No.22120180538)

  • Received Date: 2020-06-24
  • Rev Recd Date: 2020-08-25
  • Seismic exploration is a non-destructive detection technology widely applied in oil and gas exploration or geological structure exploration. The seismic signals are quite difficult to be interpreted due to the complexity of underground structures and the inhomogeneous media. Limitations of current methods used in seismic signals interpretation and needs for expert knowledge considerably reduce the efficiency of interpretation. We established a nonlinear model using neural network to predict geological information. Firstly, we generated thousands of random layered geological models of media in rock, water and air. Then we got one signal data each model by using finite difference method to simulate the propagation of seismic waves. At last, 80% signals and geological models data are used as training data sets and 20% as test data sets. As the tests show, this neural network accurately predicts signals the depths of underground media interface and velocity of the media between. We didn't use any filtering or feature extraction method in the work above. This method improves the seismic exploration economically and efficiently. The method of machine learning nicely fits in the simplified geological situation in karst area. This research is a preliminary discussion on the feasibility of applying machine learning method into seismic exploration.
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  • Araya-Polo M,Dahlke T,Frogner C,et al. 2017. Automated fault detection without seismic processing[J]. Leading Edge,36(3):208-214.
    Araya-Polo M,Joseph, et al. 2016. Deep-learning tomography[J]. The Leading Edge,37:58-66.
    Dahlke T,Araya-Polo M,Zhang C,et al. 2016. Predicting geological features in 3D seismic data[C]//Presented at 3D Deep Learning Workshop.
    Frogner C,Zhang C,Mobah H,et al. 2015. Learning with a Wasserstein loss[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems-Volume 2. MIT Press.
    Li S,Liu B,Sun H,et al. 2014. State of art and trends of advanced geological prediction in tunnel construction[J]. Chinese Journal of Rock Mechanics and Engineering,33(6):1090-1113.
    Niu J,Du L,Gu C. 2013. The application of elastic CT method in karst prospecting[J]. Journal of Jilin University(Earth Science Edition),34(4):630-633.
    Shi Z,Liu L,Peng M,et al. 2016. A sonar detection technology for karst cavities under piles and its application[J]. Chinese Journal of Rock Mechanics and Engineering,35(1):177-186.
    Sun M,Zhang J. 2020. The near-surface velocity reversal and its detection via unsupervised machine learning[J]. Geophysics,85(3):1-41.
    Wang W,Yang F,Ma J. 2018. Velocity model building with a modified fully convolutional network[C]//SEG Technical Program Expanded Abstracts 2018.
    Yuan D. 1993. Karst of China[M]. Beijing:Geological Publishing House.
    Zhang C,Frogner C,Araya-Polo M,et al. 2014. Machine learning based automated fault detection in seismic traces[C]//76th Conference and Exhibition, EAGE,Extended Abstracts.
    Zhang Y,Jin Y. 2018.60 Years studying of karst and cavities[J]. Journal of Engineering Geology,26(1):275-277.
    Zhong S,Wang R. 2013. New landsonar method for survey of ground in busy town, karst acves in mountain and sea bottom on water[J]. Journal of Engineering Geology,21(3):422-432.
    李术才,刘斌,孙怀凤,等. 2014. 隧道施工超前地质预报研究现状及发展趋势[J]. 岩石力学与工程学报,33(6):1090-1113.
    牛建军,杜立志,谷成. 2004. 岩溶探测中的弹性波CT方法[J]. 吉林大学学报(地球科学版),(4):142-145.
    石振明,刘鎏,彭铭,等. 2016. 钻孔灌注桩桩底溶洞声呐探测方法及应用研究[J]. 岩石力学与工程学报,35(1):177-186.
    袁道先. 1993.

    中国岩溶[M]. 北京:地质出版社.
    张寿越,金玉璋. 2018. 岩溶(喀斯特)与洞穴研究60a[J]. 工程地质学报,26(1):275-277.
    钟世航,王荣. 2013. 适合闹市区勘探及溶洞探查和水上勘探的物探新方法[J]. 工程地质学报,21 (3):422-432.
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