Microseismic Dynamic Response and Multi-Source Warning during Rockburst Monitoring Based on Weight Decision Analysis

HIGHLIGHTS: What are the main findings? Fourier transform, wavelet packet transform and Hilbert-Huang transform are used to analyze microseismic events, and the time-frequency characteristics of high-energy events are obtained. The unascertained measure comprehensive evaluation model based on 1366 working face of Hengda coal mine is established. What is the implication of the main finding? It provides a method for the deep analysis of microseismic signals and provides a basis for the risk assessment of rock burst. The establishment of the model based on weight decision analysis makes the multi-parameter monitoring of rock burst more efficient and accurate. ABSTRACT: To prevent rockburst disasters and improve the accuracy of warnings for rockburst, based on the microseismic data of the 1366 working face of Hengda Coal Mine collected by the microseismic monitoring system, Fourier transform, wavelet packet transform, and Hilbert–Huang transform analysis methods are used for time-frequency domain joint analysis. The time-frequency differences of the main frequency, amplitude, frequency band percentage, and instantaneous energy of the high-energy microseismic event and the events before high-energy microseismic event are obtained. The analysis shows that the high-energy event has obvious low frequency characteristics, and when the high-energy event occurs, the instantaneous energy shows an obvious “inverted V” trend. At the same time, it is found that the acoustoelectric indexes show a trend of “rising” or “inverted V” when the high-energy event occurs. On this basis, the unascertained measure comprehensive evaluation model of rock burst hazard is established by analytic hierarchy process (AHP). Based on the analysis of microseismic data and the acoustoelectric index of the 1366 working face in Hengda coal mine, it is of great significance to determine the warning indicators for rockburst, improve the accuracy of uncertainty quantitative analysis for rockburst, and improve the discrimination accuracy of rockburst risk.