Pseudo-prospective and prospective rock failure along with rockburst prediction based on acoustic emission

Abstract

Rock burst is a serious threat to mine safety production, and its prediction is of great significance to effectively prevent and control the dynamic disaster of rock burst. Therefore, this paper used AE data of rock loading process to conduct pseudo-prospective prediction and short-term and long-term prospective prediction of rock failure, and further explore the prediction of rock burst based on acoustic emission (AE) and its reliability. The results show that: by selecting the appropriate critical point of failure, the autoregressive integrated moving average model can make short-term predictions of rock failure. The prediction accuracy of the acoustic emission posi- tioning technology for the fracture surface and fracture location of rocks is affected by the prediction time. The closer to the failure point, the higher the prediction accuracy is. The energy prediction method based on the energy accumulation mechanism can effectively predict the elastic energy at the moment of failure. This study also proposes combining machine learning with the analysis of historical acoustic emission data from rockbursts, which can improve the reliability of rockburst prediction. The research results can provide theoretical support for the prevention and control of rock burst dynamic disasters.

Keywords

Acoustic emission (AE) / Autoregressive integrated moving average model / AE localization / Energy accumulation / Rockburst

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