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Novel Transfer Learning Framework for Microseismic Event Recognition Between Multiple Monitoring Projects

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Abstract

Deep neural networks have achieved great success in microseismic event recognition tasks in rock engineering. However, a well-trained model on one project may not deliver satisfactory results on other new projects due to the different geological conditions, construction methods and monitoring configurations. In this study, the differences of microseismic monitoring in four monitoring project cases, including the geology conditions and monitoring settings, are compared to better understand the challenges of signal recognition by deep learning, and then the drawbacks and unavailability of current algorithms for microseismic monitoring scenarios are discussed. To address the challenges, a unified transfer learning strategy is proposed to perform knowledge transfer for microseismic recognition between multiple projects with varying difficulties, and it consists of a novel unsupervised two-level adversarial adaptation method and a semisupervised active learning mechanism; thus, it operates in a semisupervised fashion. The results experimentally demonstrate that a well-trained model cannot generalize well to other rock projects without performing transfer learning. This framework is evaluated on four real-world collections of monitoring recordings and compared with other benchmark methods. The results show that it outperforms all counterparts and is able to conduct knowledge transfer with varying difficulties. Moreover, the proposed active learning mechanism significantly reduces the manual labeling costs for both hard and easy tasks. The simulations suggest that we have preliminarily achieved deep model reuse in signal recognition of microseismic monitoring.

Highlights

  • The transferability and vulnerabilities of the existing deep neural networks for microseismic event identification between different monitoring projects are discussed.

  • A novel two-level adversarial domain adaptation method is proposed to perform transfer learning in unsupervised setting.

  • A “performance sacrifice” method is proposed to improve algorithm design and explain models’ performance.

  • An active learning mechanism is implemented to greatly reduce manual labelling cost.

  • A unified transfer learning strategy is proposed to perform knowledge transfer for microseismic event recognition of different underground construction projects with varying difficulties.

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References

  • Ajakan H, Germain P, Larochelle H, Laviolette F, Marchand M (2014) Domain-adversarial neural networks. arXiv e-prints https://arxiv.org/abs/1412.4446

  • Bi X, Zhang C, He Y, Zhao X, Sun Y, Ma Y (2021) Explainable time–frequency convolutional neural network for microseismic waveform classification. Inf Sci 546:883–896. https://doi.org/10.1016/j.ins.2020.08.109

    Article  Google Scholar 

  • Girshick R, Donahue J, Darrell T, Malik J (2013) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Conference on Computer vision and pattern recognition, Columbus, OH, USA, pp 580–587

  • Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: 27th International Conference on neural information processing systems, Montreal, Canada, pp 2672–2680

  • He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. 2015 IEEE International Conference on computer vision. IEEE Computer Society, USA, pp 1026–1034

  • Isola P, Zhu JY, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: 30th Conference on computer vision and pattern recognition, Honolulu, HI, USA, pp 5967–5976

  • Kading C, Rodner E, Freytag A, Denzler J (2017) Fine-tuning deep neural networks in continuous learning scenarios. In: 13th Asian Conference on computer vision. Springer Verlag, Taipei, Taiwan, pp 588–605

  • Karras T, Laine S, Aila T (2020) A style-based generator architecture for generative adversarial networks. IEEE Trans Pattern Anal Mach Intell 99:1

    Google Scholar 

  • Liang Z, Xue R, Xu N, Li W (2020) Characterizing rockbursts and analysis on frequency-spectrum evolutionary law of rockburst precursor based on microseismic monitoring. Tunn Undergr Space Technol. https://doi.org/10.1016/j.tust.2020.103564

    Article  Google Scholar 

  • Long M, Wang J (2015) Learning transferable features with deep adaptation networks. arXiv e-prints https://arxiv.org/abs/1502.02791

  • 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 

  • Perol T, Gharbi M, Denolle M (2018) Convolutional neural network for earthquake detection and location. Sci Adv 4(2):e1700578

    Article  Google Scholar 

  • Qian B, Xu N, Xiao P, Zhao T, Dong L (2019) Damage analysis and deformation early warning of surrounding rock mass during top arch excavation of underground powerhouse of Shuangjiangkou hydropower station. Chin J Rock Mech Eng 38(12):2512–2524. https://doi.org/10.13722/j.cnki.jrme.2019.0476

    Article  Google Scholar 

  • Ross ZE, Meier M-A, Hauksson E (2018) PWave arrival picking and first-motion polarity determination with deep learning. J Geophys Res Solid Earth 123(6):5120–5129. https://doi.org/10.1029/2017jb015251

    Article  Google Scholar 

  • Salakhutdinov R, Hinton G (2009) Semantic hashing. Int J Approx Reason 50(7):969–978

    Article  Google Scholar 

  • Sun B, Saenko K (2016) Deep CORAL: correlation alignment for deep domain adaptation. In: 14th European Conference on computer vision. Springer Verlag, Amsterdam, Netherlands, pp 443–450

  • Sun B, Feng J, Saenko K (2016) Return of frustratingly easy domain adaptation. In: 30th AAAI Conference on artificial intelligence. AAAI Press, Phoenix, Arizona, pp 2058–2065

  • Tang S, Wang J, Tang C (2020) Identification of microseismic events in rock engineering by a convolutional neural network combined with an attention mechanism. Rock Mech Rock Eng 54(1):47–69. https://doi.org/10.1007/s00603-020-02259-0

    Article  Google Scholar 

  • Tzeng E, Hoffman J, Zhang N, Saenko K, Darrell T (2014) Deep domain confusion: maximizing for domain invariance. arXiv e-prints arXiv: 14123474.

  • Tzeng E, Hoffman J, Darrell T, Saenko K (2015) 15th simultaneous deep transfer across domains and tasks. In: International Conference on Computer Vision, Santiago, Chile, pp 4068–4076.=

  • Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation. In: IEEE Conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA, pp 2962–2971

  • Wang JX, Tang SB, Heap MJ, Tang CA, Tang LX (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. https://doi.org/10.1016/j.ijrmms.2021.104685

    Article  Google Scholar 

  • 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 

  • Xu NW, Dai F, Liang ZZ, Zhou Z, Sha C, Tang CA (2014) The dynamic evaluation of rock slope stability considering the effects of microseismic damage. Rock Mech Rock Eng 47(2):621–642. https://doi.org/10.1007/s00603-013-0432-5

    Article  Google Scholar 

  • Xu NW, Li TB, Dai F, Li B, Zhu YG, Yang DS (2015) Microseismic monitoring and stability evaluation for the large scale underground caverns at the Houziyan hydropower station in Southwest China. Eng Geol 188:48–67

    Article  Google Scholar 

  • Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? In: 27th International Conference on neural information processing systems, Montreal, Canada, pp 3320–3328

  • Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: IEEE International Conference on computer vision, Venice, Italy, pp 2242–2251

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (51874065 and U1903112).

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

    Jiaxu Wang & Shibin Tang

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  1. Jiaxu Wang
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  2. Shibin Tang
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Correspondence to Shibin Tang.

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Wang, J., Tang, S. Novel Transfer Learning Framework for Microseismic Event Recognition Between Multiple Monitoring Projects. Rock Mech Rock Eng 55, 3563–3582 (2022). https://doi.org/10.1007/s00603-022-02790-2

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