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Distributed Fuzzy Clustering Analysis of Time-Lapse Electrical Resistivity Tomography for Water Inrush Monitoring in Coal Mines

Author

Listed:
  • Zhang Herui

    (School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China)

  • Wang Guolin

    (China Railway Shanghai Design Institute Group Corporation Limited, Shanghai 200070, China)

  • Teng Xiaozhen

    (School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China)

  • Zheng Xiaohui

    (China Railway Shanghai Design Institute Group Corporation Limited, Shanghai 200070, China)

Abstract

The majority of water inrush accidents in coal mines are caused by mining engineering activities. To avoid water inrush accidents, the Time-lapse Electrical Resistivity Tomography (TLERT) is applied to monitor water migration in fractured zone. A great challenge for the application of TLERT monitoring is the huge and numerous time series data sets generated by monitoring systems, which are difficult to process manually. This research proposed a distributed fuzzy clustering algorithm based on kernel function estimation to analyze TLERT images automatically. The resistivity date can be classified with different cluster centroids. The fuzzy c -means algorithm was chosen to display resistivity change. The algorithm was validated using a floor water inrush model. The results indicate that the water migration in the fractured zone can be monitored automatically and the edge of the resistivity changing area can be shown clearly.

Suggested Citation

  • Zhang Herui & Wang Guolin & Teng Xiaozhen & Zheng Xiaohui, 2022. "Distributed Fuzzy Clustering Analysis of Time-Lapse Electrical Resistivity Tomography for Water Inrush Monitoring in Coal Mines," Sustainability, MDPI, vol. 14(24), pages 1-12, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:17011-:d:1007717
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