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Automatic Events Recognition in Low SNR Microseismic Signals of Coal Mine Based on Wavelet Scattering Transform and SVM

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  • Xin Fan

    (College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Jianyuan Cheng

    (Xi’an Research Institute Co., Ltd., China Coal Technology and Engineering Group Corp., Xi’an 710077, China)

  • Yunhong Wang

    (Xi’an Research Institute Co., Ltd., China Coal Technology and Engineering Group Corp., Xi’an 710077, China)

  • Sheng Li

    (Xi’an Research Institute Co., Ltd., China Coal Technology and Engineering Group Corp., Xi’an 710077, China)

  • Bin Yan

    (Xi’an Research Institute Co., Ltd., China Coal Technology and Engineering Group Corp., Xi’an 710077, China)

  • Qingqing Zhang

    (Xi’an Research Institute Co., Ltd., China Coal Technology and Engineering Group Corp., Xi’an 710077, China)

Abstract

The technology of microseismic monitoring, the first step of which is event recognition, provides an effective method for giving early warning of dynamic disasters in coal mines, especially mining water hazards, while signals with a low signal-to-noise ratio (SNR) usually cannot be recognized effectively by systematic methods. This paper proposes a wavelet scattering decomposition (WSD) transform and support vector machine (SVM) algorithm for discriminating events of microseismic signals with a low SNR. Firstly, a method of signal feature extraction based on WSD transform is presented by studying the matrix constructed by the scattering decomposition coefficients. Secondly, the microseismic events intelligent recognition model built by operating a WSD coefficients calculation for the acquired raw vibration signals, shaping a feature vector matrix of them, is outlined. Finally, a comparative analysis of the microseismic events and noise signals in the experiment verifies that the discriminative features of the two can accurately be expressed by using wavelet scattering coefficients. The artificial intelligence recognition model developed based on both SVM and WSD not only provides a fast method with a high classification accuracy rate, but it also fits the online feature extraction of microseismic monitoring signals. We establish that the proposed method improves the efficiency and the accuracy of microseismic signals processing for monitoring rock instability and seismicity.

Suggested Citation

  • Xin Fan & Jianyuan Cheng & Yunhong Wang & Sheng Li & Bin Yan & Qingqing Zhang, 2022. "Automatic Events Recognition in Low SNR Microseismic Signals of Coal Mine Based on Wavelet Scattering Transform and SVM," Energies, MDPI, vol. 15(7), pages 1-13, March.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2326-:d:777461
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    Citations

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    Cited by:

    1. Longjun Dong & Yanlin Zhao & Wenxue Chen, 2022. "Mining Safety and Sustainability—An Overview," Sustainability, MDPI, vol. 14(11), pages 1-6, May.
    2. Sergey Zhironkin & Elena Dotsenko, 2023. "Review of Transition from Mining 4.0 to 5.0 in Fossil Energy Sources Production," Energies, MDPI, vol. 16(15), pages 1-35, August.
    3. Chenbo Shi & Yanhong Cheng & Chun Zhang & Jin Yuan & Yuxin Wang & Xin Jiang & Changsheng Zhu, 2023. "Wavelet Scattering Convolution Network-Based Detection Algorithm on Nondestructive Microcrack Electrical Signals of Eggs," Agriculture, MDPI, vol. 13(3), pages 1-19, March.
    4. Olga Zhironkina & Sergey Zhironkin, 2023. "Technological and Intellectual Transition to Mining 4.0: A Review," Energies, MDPI, vol. 16(3), pages 1-37, February.

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