Rockburst early-warning is crucial for ensuring safety in deep underground engineering. Existing methods primarily focus on classifying rockburst grades, making it challenging to provide timely warnings. This paper proposes a novel rockburst early-warning framework based on time series prediction of acoustic emission (AE) parameters. Six AE parameters (rise time, count, duration, amplitude, absolute energy, and peak frequency) were identified as potential indicators for rockburst early-warning based on rockburst tests. A sliding window method was applied to process normalized AE data, calculating statistical parameters of the local duration. An LSTM-based time series prediction model was developed to forecast the future evolution of these AE parameters. This, in turn, enabled the establishment of a comprehensive multi-indicator early-warning system. The Isolation Forest (IF) algorithm, an outlier detection method, was used to determine the warning thresholds for each indicator. The CRITIC weighting method was employed to integrate the six rockburst indicators into a single early-warning coefficient (), with =100 signifying the warning trigger condition. The results demonstrate that the proposed framework effectively captures the evolution trends of AE parameters, enabling proactive early warnings. This approach addresses the limitations of existing methods, such as reliance on experience for threshold determination, lack of a clear basis for multi-indicator weights, and difficulty in quantifying early-warning trigger conditions. The framework provides a new perspective for rockburst early-warning systems.

中文翻译：

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基于多声发射参数时间序列预测的岩爆预警方法


岩爆预警对于保障深部地下工程安全至关重要。现有方法主要集中于对岩爆等级进行分类，这使得提供及时预警具有挑战性。本文提出了一种基于声发射（AE）参数时间序列预测的新型岩爆预警框架。根据岩爆试验，确定了六个 AE 参数（上升时间、计数、持续时间、振幅、绝对能量和峰值频率）作为岩爆预警的潜在指标。采用滑动窗口方法处理归一化AE数据，计算局部持续时间的统计参数。开发了基于 LSTM 的时间序列预测模型来预测这些 AE 参数的未来演变。这反过来又促成了全面的多指标预警系统的建立。孤立森林（IF）算法是一种异常值检测方法，用于确定每个指标的警告阈​​值。采用CRITIC加权方法将6个岩爆指标整合为一个预警系数()，其中=100表示​​预警触发条件。结果表明，所提出的框架有效地捕捉了AE参数的演变趋势，从而实现主动预警。该方法解决了现有方法阈值确定依赖经验、多指标权重缺乏明确依据、预警触发条件难以量化等局限性。该框架为岩爆预警系统提供了新的视角。