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Automatic event detection in low SNR microseismic signals based on multi-scale permutation entropy and a support vector machine

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Abstract

Microseismic monitoring is an effective means for providing early warning of rock or coal dynamical disasters, and its first step is microseismic event detection, although low SNR microseismic signals often cannot effectively be detected by routine methods. To solve this problem, this paper presents permutation entropy and a support vector machine to detect low SNR microseismic events. First, an extraction method of signal features based on multi-scale permutation entropy is proposed by studying the influence of the scale factor on the signal permutation entropy. Second, the detection model of low SNR microseismic events based on the least squares support vector machine is built by performing a multi-scale permutation entropy calculation for the collected vibration signals, constructing a feature vector set of signals. Finally, a comparative analysis of the microseismic events and noise signals in the experiment proves that the different characteristics of the two can be fully expressed by using multi-scale permutation entropy. The detection model of microseismic events combined with the support vector machine, which has the features of high classification accuracy and fast real-time algorithms, can meet the requirements of online, real-time extractions of microseismic events.

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Acknowledgements

The authors wish to express their thanks to two anonymous reviewers for their help to improve the manuscript as well as the collaborative funding support from the China Postdoctoral Science Foundation (2015M582117), Shandong Natural Science Foundation (ZR2013EEM019), The State Key Research Development Program of China (2016YFC0801406), Key Research and Development Program of Shandong Province (2016GSF120012), Qingdao Postdoctoral Applied Research Project, and Special Project Fund of Taishan Scholars of Shandong Province.

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Authors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, People’s Republic of China

    Rui-Sheng Jia, Hong-Mei Sun, Yan-Jun Peng, Yong-Quan Liang & Xin-Ming Lu

  2. Shandong Province Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao, 266590, People’s Republic of China

    Rui-Sheng Jia, Yan-Jun Peng, Yong-Quan Liang & Xin-Ming Lu

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  1. Rui-Sheng Jia
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  2. Hong-Mei Sun
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  3. Yan-Jun Peng
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  4. Yong-Quan Liang
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  5. Xin-Ming Lu
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Correspondence to Rui-Sheng Jia.

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Jia, RS., Sun, HM., Peng, YJ. et al. Automatic event detection in low SNR microseismic signals based on multi-scale permutation entropy and a support vector machine. J Seismol 21, 735–748 (2017). https://doi.org/10.1007/s10950-016-9632-2

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  • DOI: https://doi.org/10.1007/s10950-016-9632-2

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