Multivariate Early Warning Method for Rockburst Monitoring Based on Microseismic Activity Characteristics

Abstract

The monitoring of rockburst is one of the worldwide problems in underground engineering and how to effectively predict and early warn the occurrence of rockburst disasters has become an urgent problem to be solved. In this article, the high rockburst occurrence section of the deep diversion tunnel of Jinping Hydropower Station on the yalong River is taken as the research object. Based on the microseismic monitoring technology and combined with the principle of seismology with qualitative analysis and quantitative calculation, the distribution law of “time, space, and intensity” of microseismic activity and the change law of source parameters time series are used as the precursor characteristics of rockburst early warning. Based on these, the internal relationship between the microseismic activity and the rockburst micro-fracture was studied. The monitoring results show that the rockburst occurred before has obvious micro-fracture precursors. The microseismic activity is a self-organizing process from spatial disordered dispersion to ordered concentration. The abnormal changes in source parameters such as density of microseismic events, seismic energy density, the cumulative volume, energy index, 3S index, and b values can be used as a warning identification of rockburst. Therefore, the multivariate early warning method for rockburst monitoring based on the comprehensive analysis of source parameters in the deep tunnel is proposed. The prediction accuracy of this method is up to 80.6%, and it can provide reference for the rockburst prediction, warning, and safe construction of such tunnel engineering.