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Cluster analysis of the domain of microseismic event attributes for floor water inrush warning in the working face

  • Signal processing
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Abstract

Differences are found in the attributes of microseismic events caused by coal seam rupture, underground structure activation, and groundwater movement in coal mine production. Based on these differences, accurate classification and analysis of microseismic events are important for the water inrush warning of the coal mine working face floor. Cluster analysis, which classifies samples according to data similarity, has remarkable advantages in nonlinear classification. A water inrush early warning method for coal mine floors is proposed in this paper. First, the short time average over long time average (STA/LTA) method is used to identify effective events from continuous microseismic records to realize the identification of microseismic events in coal mines. Then, ten attributes of microseismic events are extracted, and cluster analysis is conducted in the attribute domain to realize unsupervised classification of microseismic events. Clustering results of synthetic and field data demonstrate the effectiveness of the proposed method. The analysis of field data clustering results shows that the first kind of events with time change rules is of considerable importance to the early warning of water inrush from the coal mine working face floor.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 41904098, in part by the Beijing Nova Program under Grant 2022056, and in part by the National Natural Science Foundation of China (52174218).

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Authors and Affiliations

  1. School of Safety Engineering, China University of Mining and Technology, Xuzhou, 221116, China

    Guo-Jun Shang & Xiao-Fei Liu

  2. Shenzhen Urban Public Safety Technology Research Institute Co., Ltd, Shenzhen, 518000, China

    Guo-Jun Shang & Li Li

  3. Hebei Coal Research Institute Company Ltd., Jizhong Energy Group, Xingtai, 054000, China

    Li-Song Zhao

  4. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum-Beijing, Beijing, 102249, China

    Jin-Song Shen & Wei-Lin Huang

Authors
  1. Guo-Jun Shang
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  2. Xiao-Fei Liu
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  3. Li Li
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  4. Li-Song Zhao
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  5. Jin-Song Shen
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  6. Wei-Lin Huang
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Corresponding author

Correspondence to Wei-Lin Huang.

Additional information

Shang Guo-Jun is a postdoctoral fellow at China University of Mining and Technology. He graduated from China University of Petroleum (Beijing) in 2012 with a bachelor’s degree in Exploration Technology and Engineering. He studied at China University of Petroleum (Beijing) from 2016 to 2022 and obtained his Ph.D. degree in Geological Resources and Geological Engineering in 2022. He is currently engaged in post-doctoral research at the School of Safety, China University of Mining and Technology. His main research interest is mining engineering safety and science.

Huang Wei-Lin is a Professor at China University of Petroleum (Beijing). He studied at China University of Petroleum (Beijing) from 2016 to 2022 and obtained his double bachelor’s, master’s and Ph.D. degrees. He is currently engaged in teaching and research at the College of Geophysics, China University of Petroleum (Beijing). His main research interests include seismic signal processing, analysis and inversion, artificial intelligence and big data, microseismic monitoring, and image and signal processing.

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Shang, GJ., Liu, XF., Li, L. et al. Cluster analysis of the domain of microseismic event attributes for floor water inrush warning in the working face. Appl. Geophys. 19, 409–423 (2022). https://doi.org/10.1007/s11770-022-0952-4

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  • DOI: https://doi.org/10.1007/s11770-022-0952-4

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