Characterizing rockbursts and analysis on frequency-spectrum evolutionary law of rockburst precursor based on microseismic monitoring
Characterizing rockbursts and analysis on frequency-spectrum evolutionary law of rockburst precursor based on microseismic monitoring
复制标题
基于微震监测的岩爆特征及岩爆前兆频谱演化规律分析
DOI：
10.1016/j.tust.2020.103564
复制
发表时间：
2020-11-01
影响因子：
6.9
通讯作者：
Li, Wanrun
中科院分区：
文献类型：
--
作者：
Liang, Zhengzhao;Xue, Ruixiong;Li, Wanrun
中国双江口水电站主厂房引水隧洞地质条件复杂，地应力高。岩爆对隧道内人员和设备的安全构成严重威胁。采用三维微震监测技术探测隧道围岩中的微震活动。在考虑各种信号的情况下，利用快速傅立叶变换(FFT)方法得到信号波形的幅频谱，进而得到信号的一些波形特征(幅度、持续时间、主频分布范围、峰值频率、主频值等)。分析以识别MS信号。为进一步的信号分析提供了保障，对其他隧道的MS信号识别也具有重要的指导意义。根据MS活动频率和释放的能量时间序列曲线，可以确定隧道内岩爆序列类型为前震-主震-余震。研究了隧道围岩内部微裂缝的时空分布特征，圈定了隧道围岩内部的主要活动区域。此外，采用了一种更有效的信号分析技术(小波包变换)对复杂的MS波形进行频率分解，保留了信号的主导信息；首次建立了分析围岩内部活动特征的时频模型，利用该模型可以更直观地分析和准确判断冲击地压前兆信息。结果表明，频带能量分布的下移现象可以作为岩爆的预警指标。
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.