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<p>Real-time microseismic (MS) monitoring of coal burst danger areas is crucial for predicting and providing early warnings of coal burst risks. Given the nonlinearity, abnormal spatio-temporal (ST) distribution, and complexity of MS data, extracting a reliable characteristic index to identify coal burst danger areas is challenging. This study aims to develop indices to assess deformation localization (DL) states and identify areas at risk of coal burst hazards. We first establish the relationship between coal burst occurrence and coal rock DL and propose two key indices: the Global Cluster Index (GCI) and the Local Density Index (LDI). Subsequent experimental results confirm the correlation between coal rock DL and the MS-based indices. These indices are then applied to track DL in a working face and to identify coal burst danger areas. The method’s validity is confirmed through mining data analysis, high-energy MS events, and examinations of potential causes of coal burst danger areas. Our research findings demonstrate a successful correlation between coal bursts and coal rock DL. By introducing a proposed index based on MS(AE) events, we track DL areas in the mining face using the GCI and LDI. This method has also effectively identified coal burst danger areas in the Hongqinghe 105 and Longjiabao 618 working faces.</p>

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The Construction of Microseismic Spatio-Temporal Evolution Characteristic Indices and the Identification of Coal Burst Danger Areas

  • 期刊论文

  • 发表日期：

    2025年10月31日

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  • Chenli Zhu,
  • Linlin Ding,
  • Yimin Song,
  • Yuda Li

摘要

Real-time microseismic (MS) monitoring of coal burst danger areas is crucial for predicting and providing early warnings of coal burst risks. Given the nonlinearity, abnormal spatio-temporal (ST) distribution, and complexity of MS data, extracting a reliable characteristic index to identify coal burst danger areas is challenging. This study aims to develop indices to assess deformation localization (DL) states and identify areas at risk of coal burst hazards. We first establish the relationship between coal burst occurrence and coal rock DL and propose two key indices: the Global Cluster Index (GCI) and the Local Density Index (LDI). Subsequent experimental results confirm the correlation between coal rock DL and the MS-based indices. These indices are then applied to track DL in a working face and to identify coal burst danger areas. The method’s validity is confirmed through mining data analysis, high-energy MS events, and examinations of potential causes of coal burst danger areas. Our research findings demonstrate a successful correlation between coal bursts and coal rock DL. By introducing a proposed index based on MS(AE) events, we track DL areas in the mining face using the GCI and LDI. This method has also effectively identified coal burst danger areas in the Hongqinghe 105 and Longjiabao 618 working faces.