Abstract Mining-induced microseismicity is widely considered as a result of slippage of pre-existing critically stressed fractures caused by stress perturbations around an advancing face. An in-depth analysis of the recorded microseismicity associated with longwall top coal caving mining at Coal Mine Velenje in Slovenia has been previously carried out and reported by the authors. It has been concluded that while microseismic event rate is affected by mining intensity (longwall face daily advance rate) as well as local abundance of pre-existing fractures, spatial and magnitude characteristics of microseismicity are predominantly influenced by the latter. Based upon this improved understanding of fracture-slip seismic-generation mechanism, the current work aimed at establishing a data-driven yet physics-based probabilistic forecasting methodology for hazardous microseismicity using microseismic monitoring data with concurrent face advance records. Through performing statistical analyses and probability distribution fitting for temporal, magnitude and spatial characteristics of microseismicity within a time window, a short-term forecasting model is developed to estimate the probability of potentially hazardous microseismicity over the next time interval in the form of a joint probability. The real time forecasting of hazardous microseismicity during longwall coal mining is realised through regularly updating the statistical model using the most recent microseismic sequence datasets and face advance records. This forecasting methodology is featured by the physical basis which provides a good explicability of forecasting results, and the probabilistic perspective which accounts for the stochastic nature of mining-induced microseismicity. This model has been employed to make time-varying forecasts of hazardous microseismicity around two longwall panels over a one-year coal production period at Coal Mine Velenje, and satisfactory results at both panels were achieved. In addition, the analysis suggested that the energy magnitude distribution of microseismicity is a dominant factor in contributing to the potential of hazardous microseismicity. This statistical model using microseismic monitoring data has important implications in the evaluation of mining-induced hazards and optimal control of longwall face advance in burst-prone deep-level mining sites.

中文翻译：

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与长壁煤矿开采相关的危险微地震的基于物理学的概率预测方法

摘要 采矿诱发的微地震被广泛认为是由前进工作面周围应力扰动引起的预先存在的临界应力裂缝滑动的结果。作者先前已对与斯洛文尼亚 Velenje 煤矿长壁放顶煤开采相关的微地震记录进行了深入分析。已经得出的结论是，虽然微地震事件发生率受开采强度（长壁工作面日推进率）以及局部已有裂缝丰度的影响，但微地震的空间和震级特征主要受后者的影响。基于对裂缝滑移地震发生机制的进一步了解，目前的工作旨在利用微地震监测数据和并发面前记录，建立一种数据驱动但基于物理的危险微地震概率预测方法。通过对时间窗口内微地震的时间、幅度和空间特征进行统计分析和概率分布拟合，建立短期预测模型，以联合概率的形式估计下一时间间隔内潜在危险微地震的概率. 通过使用最新的微地震序列数据集和工作面提前记录定期更新统计模型，实现长壁采煤过程中危险微地震的实时预测。这种预测方法的特点是有物理基础，为预测结果提供了良好的可解释性，概率视角解释了采矿诱发的微地震的随机性。在 Coal Mine Velenje 的一年煤炭生产期间，该模型已被用于对两个长壁面板周围的危险微地震进行时变预测，并且在两个面板上都取得了令人满意的结果。此外，分析表明，微震的能量大小分布是促成危险微震潜力的主要因素。这种使用微地震监测数据的统计模型对评估采矿诱发的危害和优化控制易爆的深部矿区的长壁工作面推进具有重要意义。