关键词: 岩石力学, 岩爆, 岩爆预测, 声音信号

Abstract: By using the self-developed true-triaxial rockburst testing machine, the rockburst processes were reproduced in laboratory and the sound signals of rockburst process were monitored. The combination index of Meyer cepstral coefficient, spectral centroid and short-time average zero-crossing rate, which can quantitatively describe the sound characteristics, was used as the feature extraction information of typical destructive phenomenon of rockburst process. Then, Gaussian process, a machine learning method for solving small sample, nonlinear classification problems, was used to construct an intelligent identification model. Thus, the intelligent identification of typical failure phenomena in a rockburst process was realized. In addition, in order to overcome the shortage of traditional rock burst prediction methods, which emphasize on trend prediction but can not distinguish the development stage of rock burst process, a multilevel, progressive and dynamic prediction method of laboratory rockburst was developed based on the strategy of intelligent recognition + trend prediction. The variation laws of acoustic characteristic indexes such as quiet period, harmonic mean value and chromatographic vector mean value before rockburst were taken as the precursor information of rock burst. The prediction results of different laboratory rockbursts indicate that the method is feasible and lays the testing foundation of the sound-based method for in situ rockburst prediction in the further.

Key words: rock mechanics, rockburst, rockburst prediction, sound signals