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Microseismic Event Recognition and Transfer Learning Based on Convolutional Neural Network and Attention Mechanisms

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Abstract

Microseismic monitoring technology is widely used in tunnel and coal mine safety production. For signals generated by ultra-weak microseismic events, traditional sensors encounter limitations in terms of detection sensitivity. Given the complex engineering environment, automatic multi-classification of microseismic data is highly required. In this study, we use acceleration sensors to collect signals and combine the improved Visual Geometry Group with a convolutional block attention module to obtain a new network structure, termed CNN_BAM, for automatic classification and identification of microseismic events. We use the dataset collected from the Hanjiang-to-Weihe River Diversion Project to train and validate the network model. Results show that the CNN_BAM model exhibits good feature extraction ability, achieving a recognition accuracy of 99.29%, surpassing all its counterparts. The stability and accuracy of the classification algorithm improve remarkably. In addition, through fine-tuning and migration to the Pan II Mine Project, the network demonstrates reliable generalization performance. This outcome reflects its adaptability across different projects and promising application prospects.

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Acknowledgments

This work was supported by the Key Research and Development Plan of Anhui Province (202104a05020059) and the Excellent Scientific Research and Innovation Team of Anhui Province (2022AH010003). Partial financial support from Hefei Comprehensive National Science Center is highly appreciated.

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Authors and Affiliations

  1. Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China

    Shu Jin, Ya Gao, Benli Yu & Shenglai Zhen

  2. Key Laboratory of Opto-Electronic Information Acquisition and Manipulation, Ministry of Education, Anhui University, Hefei, 230601, China

    Shu Jin, Ya Gao, Benli Yu & Shenglai Zhen

  3. School of Public Safety and Emergency Management, Anhui University of Science and Technology, Huainan, 232000, China

    Shichao Zhang

  4. IDETECK CO., LTD., Chuangxin Avenue, Hefei, Anhui, 230601, China

    Shichao Zhang

Authors
  1. Shu Jin
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  2. Shichao Zhang
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  3. Ya Gao
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  4. Benli Yu
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  5. Shenglai Zhen
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Correspondence to Shenglai Zhen.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Shu Jin is currently working toward her MS degree in optical engineering in the School of Physics and optoelectronic engineering, Anhui University, Hefei, China. She is a master’s candidate directed by Prof. Shenglai Zhen.

Shenglai Zhen is a professor at the School of Physics and Optoelectronic Engineering at Anhui University, Hefei, China. He received an MS degree in optics in 2003 and a PhD degree in electromagnetic field and microwave technology in 2008, both from the Anhui University. He is mainly engaged in the research of optoelectronic sensors and laser sensors, especially in the field of interferometric sensors.

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Jin, S., Zhang, S., Gao, Y. et al. Microseismic Event Recognition and Transfer Learning Based on Convolutional Neural Network and Attention Mechanisms. Appl. Geophys. (2024). https://doi.org/10.1007/s11770-024-1058-y

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  • DOI: https://doi.org/10.1007/s11770-024-1058-y

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