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

Xin Fan, Jianyuan Cheng, Yunhong Wang, Sheng Li, Bin Yan and Qingqing Zhang
Additional contact information
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

Energies, 2022, vol. 15, issue 7, 1-13

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.

Keywords: mining water hazard; microseismic monitoring; intelligent recognition; feature extraction; support vector machine; classification model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/7/2326/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/7/2326/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:7:p:2326-:d:777461

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().