Automatic recognition and classification of microseismic waveforms based on computer vision

Tunnelling and Underground Space Technology - Tập 121 - Trang 104327 - 2022
Jiaming Li1, Shibin Tang1, Kunyao Li2, Shichao Zhang3, Liexian Tang4, Leyu Cao1, Fuquan Ji2
1State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China
2Research and Development Center of Transport Industry of Intelligent Manufacturing Technologies of Transport Infrastructure, CCCC Second Harbor Engineering Company LTD, Wuhan, 430000, China
3School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
4School of Mining Engineering, University of Science and Technology Liaoning, Anshan 114051, China

Tài liệu tham khảo

Bi, 2021, Explainable time-frequency convolutional neural network for microseismic waveform classification, Inform. Sci., 546, 883, 10.1016/j.ins.2020.08.109 Breiman, 1996, Bagging predictors, Mach Learn., 24, 123, 10.1007/BF00058655 Cai, 2018, A fuzzy comprehensive evaluation methodology for rock burst forecasting using microseismic monitoring, Tunn. Undergr. Space Technol., 80, 232, 10.1016/j.tust.2018.06.029 Chen, 2019, Automatic waveform classification and arrival picking based on convolutional neural network, Earth Space Sci., 6, 1244, 10.1029/2018EA000466 Dai, 2017, Microseismic early warning of surrounding rock mass deformation in the underground powerhouse of the Houziyan hydropower station, China. Tunn. Undergr. Space Technol., 62, 64, 10.1016/j.tust.2016.11.009 Dong, 2016, Discriminant models of blasts and seismic events in mine seismology, Int. J. Rock Mech. Min. Sci., 86, 282, 10.1016/j.ijrmms.2016.04.021 Dong, 2016, Discrimination of mine seismic events and blasts using the fisher classifier, naive Bayesian classifier and logistic regression, Rock Mech. Rock Eng., 49, 183, 10.1007/s00603-015-0733-y Esposito, 2006, Automatic discrimination among landslide, explosion-quake, and microtremor seismic signals at Stromboli volcano using neural networks, Bull. Seismol. Soc. Am., 96, 1230, 10.1785/0120050097 Feng, 2012, Studies on the evolution process of rockbursts in deep tunnels, J. Rock Mech. Geotech. Eng., 4, 289, 10.3724/SP.J.1235.2012.00289 Del Gaudio, 2014, What we can learn about slope response to earthquakes from ambient noise analysis: an overview, Eng. Geol., 182, 182, 10.1016/j.enggeo.2014.05.010 He, 2016, Deep residual learning for image recognition, IEEE CVPR, 770 Huang, 2018, Micro-seismic event detection and location in underground mines by using convolutional neural networks (CNN) and deep learning, Tunn. Undergr. Space Technol., 81, 265, 10.1016/j.tust.2018.07.006 Huang, 2017, Unveiling the signals from extremely noisy microseismic data for high-resolution hydraulic fracturing monitoring, Sci. Rep., 7, 11996, 10.1038/s41598-017-09711-2 Krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, Neural Inf. Process. Syst., 1, 1097 Li, 2018, Independently recurrent neural network (IndRNN): building a longer and deeper RNN, Proc IEEE Conf. Comput. Vis. Pattern Recog., 5457–5466 Li, 2021, Macro-micro response characteristics of surrounding rock and overlying strata towards the transition from open-pit to underground mining, Geofluids., 2021, 1, 10.1155/2021/3066553 Li, 2018, The spatial-temporal evolution law of microseismic activities in the failure process of deep rock masses, J. Appl. Geophy., 154, 1, 10.1016/j.jappgeo.2018.07.014 Lin, 2019, Automatic recognition and classification of multi-channel microseismic waveform based on DCNN and SVM, Comput. Geosci., 123, 111, 10.1016/j.cageo.2018.10.008 Lin, 2018, Automatic classification of multi-channel microseismic waveform based on DCNN-SPP, J. Appl. Geophy., 159, 446, 10.1016/j.jappgeo.2018.09.022 Liu, 2019, Characterizing rockbursts along a structural plane in a tunnel of the Hanjiang-to-Weihe River Diversion Project by microseismic monitoring, Rock Mech. Rock Eng., 52, 1835, 10.1007/s00603-018-1649-0 Liu, 2013, Studies on temporal and spatial variation of microseismic activities in a deep metal mine, Int. J. Rock Mech. Min. Sci., 60, 171, 10.1016/j.ijrmms.2012.12.022 Lv, 2019, Noise suppression of microseismic data based on a fast singular value decomposition algorithm, J. Appl. Geophys., 170, 103831, 10.1016/j.jappgeo.2019.103831 Ma, 2015, Rockburst characteristics and microseismic monitoring of deep-buried tunnels for Jinping II Hydropower Station, Tunn. Undergr. Space Technol., 49, 345, 10.1016/j.tust.2015.04.016 Malovichko, 2012, Discrimination of blasts in mine seismology, proceedings of the sixth international seminar on deep and high stress mining, Aust. Cent. Geomech., 161 Nair, V., Hinton, G.E., 2010. Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning. Omnipress, Madison, WI, USA, pp. 807–814. Peng, 2019, Automatic classification of microseismic signals based on MFCC and GMM-HMM in underground mines, Shock Vib., 2019, 1 Peng, 2020, Automatic classification of microseismic records in underground mining: a deep learning approach, IEEE Access., 8, 17863, 10.1109/ACCESS.2020.2967121 Peng, 2020, Microseismic records classification using capsule network with limited training samples in underground mining, Sci. Rep., 10, 13925, 10.1038/s41598-020-70916-z Scherer, D., Müller, A., Behnke, S., 2010. Evaluation of pooling operations in convolutional architectures for object recognition. In: Diamantaras K, Duch W, Iliadis LS, eds. Artificial Neural Networks – ICANN 2010. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 92–101. Shang, 2017, Improving microseismic event and quarry blast classification using artificial neural networks based on principal component analysis, Soil Dyn. Earthq. Eng., 99, 142, 10.1016/j.soildyn.2017.05.008 Simonyan, 2015, Very deep convolutional networks for large-scale image recognition, Comput. Vis. Pattern Recognit. Stockwell, 1996, Localization of the complex spectrum: the S transform, IEEE Trans. Signal Process., 44, 998, 10.1109/78.492555 Tang, 2010, Preliminary engineering application of microseismic monitoring technique to rockburst prediction in tunneling of Jinping II project, J. Rock Mech. Geotech. Eng., 2, 193, 10.3724/SP.J.1235.2010.00193 Tary, 2014, Spectral estimation—What is new? What is next?, Rev. Geophys., 52, 723, 10.1002/2014RG000461 Vallejos, 2013, Logistic regression and neural network classification of seismic records, Int. J. Rock Mech. Min. Sci., 62, 86, 10.1016/j.ijrmms.2013.04.005 Wang, 2021, An auto-detection network to provide an automated real-time early warning of rock engineering hazards using microseismic monitoring, Int. J. Rock Mech. Min. Sci., 140, 104685, 10.1016/j.ijrmms.2021.104685 Wilkins, 2020, Identifying microseismic events in a mining scenario using a convolutional neural network, Comput. Geosci., 137, 104418, 10.1016/j.cageo.2020.104418 Xiao, 2016, Mechanism of evolution of stress–structure controlled collapse of surrounding rock in caverns: a case study from the Baihetan hydropower station in China, Tunn. Undergr. Space Technol., 51, 56, 10.1016/j.tust.2015.10.020 Zhang, 2019, An automatic recognition method of microseismic signals based on EEMD-SVD and ELM, Comput. Geosci., 133, 104318, 10.1016/j.cageo.2019.104318 Zhang, 2020, A novel noise reduction method for space-borne full waveforms based on empirical mode decomposition, Optik., 202, 163581, 10.1016/j.ijleo.2019.163581 Zhao, 2015, Classification of mine blasts and microseismic events using starting-up features in seismograms, T. Nonferr. Metal. Soc., 25, 3410, 10.1016/S1003-6326(15)63976-0 Zhao, 2017, Using supervised machine learning to distinguish microseismic from noise events, Seg Tech. Program Expanded Abstr., 2918 Zhou, 2012