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<p>In the mining industry, microseismic monitoring has emerged as a crucial instrument for reducing risks related to the stability of underground rock masses. The present research conducts an analysis of dynamic hazards specifically at the 8204-2 working face in Tashan Mine. A novel monitoring framework is introduced, which integrates a nonlinear threshold curve model with a wireless microseismic monitoring system. The results demonstrate the effectiveness of the microseismic monitoring system with high-frequency sampling, utilizing the nonlinear threshold model, in detecting and providing real-time early warnings for microseismic signals, even in challenging geological environments. Practical implementation and monitoring over the past six months revealed that the microseismic monitoring system, employing the threshold curve model, achieved a monitoring and early warning accuracy exceeding 95%. This model combines the construction of a nonlinear threshold curve with dynamic conversion of confidence factors, and implements multi-level graded early warning based on the matching degree between real-time data and the hazard model, making its accuracy superior to traditional monitoring and early warning systems. Consequently, this study holds great significance in enhancing precise identification and early warning capabilities for mine microseism, as well as improving safety assessments for deep rock mining and construction practices.</p>

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Enhancing safety and early warning capabilities in mining through microseismic monitoring technology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月17日

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  • Jiaxu Jin,
  • Yong Xiao,
  • Pengfei Wu,
  • Jun Wang,
  • Jie Liu,
  • Yunlong Mo,
  • Lei Zhang

摘要

In the mining industry, microseismic monitoring has emerged as a crucial instrument for reducing risks related to the stability of underground rock masses. The present research conducts an analysis of dynamic hazards specifically at the 8204-2 working face in Tashan Mine. A novel monitoring framework is introduced, which integrates a nonlinear threshold curve model with a wireless microseismic monitoring system. The results demonstrate the effectiveness of the microseismic monitoring system with high-frequency sampling, utilizing the nonlinear threshold model, in detecting and providing real-time early warnings for microseismic signals, even in challenging geological environments. Practical implementation and monitoring over the past six months revealed that the microseismic monitoring system, employing the threshold curve model, achieved a monitoring and early warning accuracy exceeding 95%. This model combines the construction of a nonlinear threshold curve with dynamic conversion of confidence factors, and implements multi-level graded early warning based on the matching degree between real-time data and the hazard model, making its accuracy superior to traditional monitoring and early warning systems. Consequently, this study holds great significance in enhancing precise identification and early warning capabilities for mine microseism, as well as improving safety assessments for deep rock mining and construction practices.