Characterizing rockbursts and analysis on frequency-spectrum evolutionary law of rockburst precursor based on microseismic monitoring

• Published in: Tunnelling and underground space technology Vol. 105; p. 103564
• Main Authors: Liang, Zhengzhao, Xue, Ruixiong, Xu, Nuwen, Li, Wanrun
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.11.2020; Elsevier BV
• Subjects: Amplitudes; Early warning; Energy; Energy distribution; Fast Fourier Transform; Fast Fourier transformations; Fourier transforms; Frequencies; Frequency analysis; Frequency distribution; Frequency spectrum; Hydroelectric power stations; Microcracks; Microseismic monitoring; Microseisms; Monitoring; Peak frequency; Precursors; Rockburst; Rockbursts; Signal analysis; Tunnels; Underground construction; Waveforms; Wavelet analysis; Wavelet packet transform; Wavelet transforms; Microseismic monitoring; Early warning; Fast Fourier Transform; Rockburst; Wavelet packet transform
• ISSN: 0886-7798; 1878-4364
• DOI: 10.1016/j.tust.2020.103564

The access tunnel in the main powerhouse of the Shuangjiangkou hydropower station in China has complex geological conditions with high in-situ stress. Rockbursts pose serious threats to the safety of personnel and equipment in the tunnel. Three-dimensional microseismic (MS) monitoring technology was used to explore MS activities inside the tunnel surrounding rock. In consideration of various kinds of signals, the Fast Fourier Transform (FFT) method was employed to obtain the amplitude-frequency spectra of signal waveforms, and then, some waveform characteristics (amplitude, duration, dominant frequency distribution range, peak frequency, main frequency value, etc.) were analyzed to recognize MS signals. It provided a guarantee for further analysis of signals, and also provided significant guidance for MS signal recognition in other tunnels. Based on MS activity frequency and released energy time-series curves, the rockburst sequence type in the tunnel can be determined as foreshock-mainshock-aftershock. Studying spatiotemporal distribution characteristics of microcracks inside the surrounding rock, dominant active areas inside the surrounding rock of the tunnel have been delimited. In addition, a more efficient signal analysis technique (wavelet packet transform) was used to frequency-decompose complex MS waveforms and the dominant information of signals was retained; furthermore, a time-frequency model was first established to analyze the activity characteristics inside the surrounding rock; by using the model we can more intuitively analyze and accurately judge rockburst precursor information. The results indicated that a downward shift phenomenon of frequency band energy distribution can be used as an early warning indicator of rockbursts.