Skip to main content

Advertisement

SpringerLink
Log in

Identification of Microseismic Events in Rock Engineering by a Convolutional Neural Network Combined with an Attention Mechanism

  • Original Paper
  • Published:
Rock Mechanics and Rock Engineering Aims and scope Submit manuscript

Abstract

Microseismic technology has widely been used in many rock engineering applications to shield workers from engineering hazards and monitor underground construction. To avoid the heavy workloads imposed by the manual recognition of many microseismic signals, this study proposes a new end-to-end training network architecture to automatically identify microseismic events. A dataset including not only easily identifiable microseismic signals but also barely distinguishable nontypical data has been collected from a practical rock engineering project for training and testing the network model. The applicability of various networks for this task is discussed to select the best method for microseismic recognition. We modify the residual skip connections to make them more suitable for the signal classification task. Then, the novel depthwise spatial and channel attention (DSCA) module is proposed. This module can autonomously learn how to weight information with different levels of importance, similar to human attention, which greatly improves the network performance without incurring additional computational costs. Theoretically, it can be a useful tool to replace traditional denoising algorithms and model the interdependencies between the different channels of a multichannel signal. Furthermore, the DSCA module and the modified residual connections are combined with a traditional convolutional network to obtain a novel network architecture named ResSCA and the results of comparative experiments are presented. Finally, single- and multichannel models are constructed based on ResSCA, which achieved improved accuracy rates. Their advantages and drawbacks are analyzed. This study presents a modified network architecture suitable for identifying and classifying complex signals to enable intelligent microseismic monitoring, which is valuable for various rock engineering applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data Availability

All the monitoring data that support this study are available from the corresponding author upon reasonable request.

References

  • Akram J, Eaton DW (2016) A review and appraisal of arrival-time picking methods for downhole microseismic data. Geophysics 81:KS71–KS91

    Article  Google Scholar 

  • Alvarez I, Garcia L, Mota S, Cortes G, Benitez C, De la Torre A (2013) An automatic P-Phase picking algorithm based on adaptive multiband processing. IEEE Geosci Remote Sens Lett 10:1488–1492. https://doi.org/10.1109/lgrs.2013.2260720

    Article  Google Scholar 

  • Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult IEEE transactions on neural. Networks 5:157–166

    Google Scholar 

  • Chen L, Zhang H, Xiao J, Nie L, Shao J, Liu W, Chua T-S (2017) SCA-CNN: spatial and channel-wise attention in convolutional networks for image captioning. In: 30th IEEE conference on computer vision and pattern recognition, CVPR 2017, July 21, 2017–July 26, 2017, Honolulu, HI, United states, 2017. Proceedings—30th IEEE conference on computer vision and pattern recognition, CVPR 2017. Institute of Electrical and Electronics Engineers Inc., pp 6298–6306. https://doi.org/10.1109/CVPR.2017.667

  • Dai H, Macbeth C (2007) Automatic picking of seismic arrivals in local earthquake data using an artificial neural network. Geophys J Int 120:758–774

    Article  Google Scholar 

  • Dong L, Yang Y, Qian B, Tan Y, Sun H, Xu N (2019) Deformation analysis of large-scale rock slopes considering the effect of microseismic events. Appl Sci 9:3409. https://doi.org/10.3390/app9163409

    Article  Google Scholar 

  • Elman JL (1990) Finding structure in time. Cogn Sci 14:179–211

    Article  Google Scholar 

  • Ge M, Mrugala M, Iannacchione AT (2009) Microseismic monitoring at a limestone mine. Geotech Geol Eng 27:325–339

    Article  Google Scholar 

  • Ghosh GK, Sivakumar C (2018) Application of underground microseismic monitoring for ground failure and secure longwall coal mining operation: a case study in an Indian mine. J Appl Geophys 150:21–39

    Article  Google Scholar 

  • Girshick R, Donahue J, Darrell T, Malik JR (2014) feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, CVPR 2014, Columbus, OH, United states, June 23, 2014–June 28 2014. Proceedings of the IEEE computer society conference on computer vision and pattern recognition. IEEE Computer Society, pp 580–587. https://doi.og/10.1109/CVPR.2014.81

  • Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statisticss, AISTATS 2010, Sardinia, Italy, May 13, 2010–May 15, 2010 2010. Journal of Machine Learning Research. Microtome Publishing, pp 249–256

  • Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics, AISTATS 2011, Fort Lauderdale, FL, United states, April 11, 2011–April 13 2011. Journal of Machine Learning Research. Microtome Publishing, pp 315–323

  • Guo X, Li Z, Qin N, Jin W (2011) Adaptive picking of microseismic event arrival using a power spectrum envelope. Comput Geosci 37:158–164. https://doi.org/10.1016/j.cageo.2010.05.022

    Article  Google Scholar 

  • He K, Sun J (2015) Convolutional neural networks at constrained time cost. In: Proceedings of the IEEE conference on computer vision and pattern recognition, CVPR 2015, Boston, MA, United states, June 7, 2015 - June 12, 2015. Proceedings of the IEEE computer society conference on computer vision and pattern recognition. IEEE Computer Society, pp 5353–5360. https://doi.org/10.1109/CVPR.2015.7299173

  • He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, CVPR 2016, Las Vegas, NV, United states, June 26, 2016–July 1, 2016. Proceedings of the IEEE computer society conference on computer vision and pattern recognition. IEEE Computer Society, pp 770–778. https://doi.org/10.1109/CVPR.2016.90

  • Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: 31st meeting of the IEEE/CVF conference on computer vision and pattern recognition, CVPR 2018, Salt Lake City, UT, United states, June 18, 2018 - June 22 2018. Proceedings of the IEEE computer society conference on computer vision and pattern recognition. IEEE Computer Society, pp 7132–7141

  • Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift ArXiv

  • Jaderberg M, Simonyan K, Zisserman A (2015) Spatial transformer networks. Adv Neural Infor Process Syst 2015:2017–2025

    Google Scholar 

  • Le QV, Jaitly N, Hinton G (2015) A simple way to initialize recurrent networks of rectified linear units ArXiv

  • Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE Conf Comput Vis Pattern Recogn 86:2278–2324

    Google Scholar 

  • Lee M, Byun J, Kim D, Choi J, Kim M (2017) Improved modified energy ratio method using a multi-window approach for accurate arrival picking. J Appl Geophys 139:117–130. https://doi.org/10.1016/j.jappgeo.2017.02.019

    Article  Google Scholar 

  • Lei Z (2019) Transfer adaptation learning a decade survey ArXiv

  • Li Y, Ni Z, Tian Y (2018) Arrival-time picking method based on approximate negentropy for microseismic data. J Appl Geophys 152:100–109. https://doi.org/10.1016/j.jappgeo.2018.03.012

    Article  Google Scholar 

  • Li P, Chen X, Shen S (2019) Stereo R-CNN based 3D Object detection for autonomous driving. In: 32nd IEEE/CVF conference on computer vision and pattern recognition, CVPR 2019, June 16, 2019–June 20, 2019, Long Beach, CA, United states, 2019. Proceedings of the IEEE computer society conference on computer vision and pattern recognition. IEEE Computer Society, pp 7636–7644. https://doi.org/10.1109/CVPR.2019.00783

  • Liang Z, Peng S, Zheng J (2014) Self-adaptive denoising for microseismic signal based on EMD and mutual information entropy. Comput Eng Appl 50:7–11

    Google Scholar 

  • Lin B, Wei X, Junjie Z (2019) Automatic recognition and classification of multi-channel microseismic waveform based on DCNN and SVM. Comput Geosci 123:111–120. https://doi.org/10.1016/j.cageo.2018.10.008

    Article  Google Scholar 

  • Liu F, Ca T, Ma T, Tang L (2019) Characterizing rockbursts along a structural plane in a tunnel of the Hanjiang-to-Weihe river diversion project by microseismic monitoring rock. Mechan Rock Eng 52:1835–1856. https://doi.org/10.1007/s00603-018-1649-0

    Article  Google Scholar 

  • Loshchilov I, Hutter F (2018) Fixing weight decay regularization in adam ICLR 2018 conference

  • Ma TH, Tang CA, Tang LX, Zhang WD, Wang L (2015) Rockburst characteristics and microseismic monitoring of deep-buried tunnels for Jinping II Hydropower Station. Tunn Undergr Space Technol 49:345–368. https://doi.org/10.1016/j.tust.2015.04.016

    Article  Google Scholar 

  • Milev AM, Spottiswoode SM (2002) Effect of the rock properties on mining-induced seismicity around the ventersdorp contact reef Witwatersrand Basin, South Africa. Mech Induc Seism 159:165–177

    Article  Google Scholar 

  • Mnih V, Heess N, Graves A, Kavukcuoglu K (2014) Recurrent models of visual attention. In: 28th annual conference on neural information processing systems 2014, NIPS 2014, December 8, 2014–December 13, 2014, Montreal, QC, Canada, 2014. Advances in neural information processing systems. Neural information processing systems foundation, pp 2204–2212

  • Paszke A et al (2019) PyTorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst 32:8024–8035

    Google Scholar 

  • Paul BQ, Pierre G, Yoann C, Munkhuu U (2015) Detection and classification of seismic events with progressive multi-channel correlation and hidden Markov models. Comput Geosci 83:110–119. https://doi.org/10.1016/j.cageo.2015.07.002

    Article  Google Scholar 

  • Rodriguez IV, Bonar D, Sacchi M (2012) Microseismic data denoising using a 3C group sparsity constrained time-frequency transform. Geophysics 77:21–29

    Article  Google Scholar 

  • Shang X, Li X, Morales-Esteban A, Chen G (2017) Improving microseismic event and quarry blast classification using artificial neural networks based on principal component analysis. Soil Dyn Earthq Eng 99:142–149. https://doi.org/10.1016/j.soildyn.2017.05.008

    Article  Google Scholar 

  • Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition ArXiv

  • Song F, Kuleli HS, Toksöz MN, Ay E, Zhang H (2010) An improved method for hydrofracture-induced microseismic event detection and phase picking. Geophysics 75:A47–A52

    Article  Google Scholar 

  • Srivastava RK, Greff K, Schmidhuber J (2015) Highway networks arXiv preprint arXiv:00387

  • Urbancic T, Trifu C-I (2000) Recent advances in seismic monitoring technology at Canadian mines. J Appl Geophys 45:225–237

    Article  Google Scholar 

  • Vaswani A et al (2017) Attention is all you need. In: 31st annual conference on neural information processing systems, NIPS 2017, December 4, 2017–December 9, 2017, Long Beach, CA, United states, 2017. Advances in neural information processing systems. Neural information processing systems foundation, pp 5998–6008

  • Wang J, Teng TL (1995) Artificial neural network-based seismic detector. Bull Seismol Soc Am 85:308–319

    Google Scholar 

  • Wang M, Liu B, Foroosh H (2017) Factorized convolutional neural networks. In: 16th IEEE international conference on computer vision workshops, ICCVW 2017, October 22, 2017–October 29, 2017, Venice, Italy, 2017. Proceedings - 2017 IEEE international conference on computer vision workshops, ICCVW 2017. Institute of Electrical and Electronics Engineers Inc., pp 545–553. https://doi.org/10.1109/ICCVW.2017.71

  • Wilkins AH, Strange A, Duan Y, Luo X (2020) Identifying microseismic events in a mining scenario using a convolutional neural network. Comput Geosci 137:104418. https://doi.org/10.1016/j.cageo.2020.104418

    Article  Google Scholar 

  • Woo S, Park J, Lee J-Y, Kweon IS (2018) CBAM: convolutional block attention module. In: 15th european conference on computer vision, ECCV 2018, September 8, 2018 - September 14, 2018, Munich, Germany, 2018. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). Springer, New York, pp 3–19. https://doi.org/10.1007/978-3-030-01234-2_1

  • Chollet F Xception (2017) Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, CVPR 2017, Honolulu, HI, United states, July 21, 2017–July 26 2017. Institute of Electrical and Electronics Engineers Inc., pp 1800–1807. https://doi.org/10.1109/CVPR.2017.195

  • Xu K et al (2015) Show, attend and tell: Neural image caption generation with visual attention. In: 32nd international conference on machine learning, ICML 2015, July 6, 2015–July 11, 2015, Lile, France, 2015. 32nd international conference on machine learning, ICML 2015. International Machine Learning Society (IMLS), pp 2048–2057

  • Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: 13th European conference on computer vision, ECCV 2014, September 6, 2014–September 12, 2014, Zurich, Switzerland, 2014. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). Springer, New York, pp 818–833. https://doi.org/10.1007/978-3-319-10590-1_53

  • Zeng X, Ouyang W, Yang B, Yan J, Wang X (2016) Gated Bi-directional CNN for Object Detection. In: Computer vision—14th European conference, ECCV 2016, Proceedings, 2016. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). Springer, New York, pp 354–369. https://doi.org/10.1007/978-3-319-46478-7_22

  • Zhang T, Qi G-J, Xiao B, Wang J (2017) Interleaved group convolutions. In: 16th IEEE international conference on computer vision, ICCV 2017, Venice, Italy, 2017. Proceedings of the IEEE international conference on computer vision. Institute of Electrical and Electronics Engineers Inc., pp 4373–4382. https://doi.org/10.1109/ICCV.2017.469

  • Zhang J, Li W, Ogunbona P (2019) <Transfer Learning For Cross-Dataset Recognition A Survey.pdf> ArXiv

  • Zhao Y, Takano K (1999) An artificial neural network approach for broadband seismic phase picking. Bull Seismol Soc Am 89:670–680

    Google Scholar 

  • Zhao GY, Ma J, Dong LJ, Li XB, Hui CG, Zhang CX (2015) Classification of mine blasts and microseismic events using starting-up features in seismograms. Trans Nonferrous Metals Soc China 25:3410–3420

    Article  Google Scholar 

  • Zhuang D, Ma K, Tang C, Cui X, Yang G (2019) Study on crack formation and propagation in the galleries of the Dagangshan high arch dam in Southwest China based on microseismic monitoring and numerical simulation. Int J Rock Mech Min Sci 115:157–172. https://doi.org/10.1016/j.ijrmms.2018.11.016

    Article  Google Scholar 

Download references

Acknowledgements

This project was financially supported by the National Key Research and Development Program (2017YFC1503102) and the National Natural Science Foundation of China (41941018, 51874065 and U1903112).

Author information

Authors and Affiliations

  1. State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116024, China

    Shibin Tang, Jiaxu Wang & Chunan Tang

Authors
  1. Shibin Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiaxu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chunan Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shibin Tang.

Ethics declarations

Conflict of interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tang, S., Wang, J. & Tang, C. Identification of Microseismic Events in Rock Engineering by a Convolutional Neural Network Combined with an Attention Mechanism. Rock Mech Rock Eng 54, 47–69 (2021). https://doi.org/10.1007/s00603-020-02259-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00603-020-02259-0

Keywords

Search

Navigation