Rockburst early-warning method based on time series prediction of multiple acoustic emission parameters

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 (EC), with EC=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.