Research on precursory signals of microseism of rock burst based on wavelet analysis
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摘要: 采集某煤矿1300工作面冲击地压发生前后的微震信号,进行时序特征分析,并采用db5作为小波基函数对微震信号进行5层分解,得到各层子频带的频谱特征和能量百分比。分析结果表明:冲击地压发生前,微震次数和能量呈现先增加后减少再增加的趋势;冲击地压发生前1 h出现了前兆信号,能量主要分布在62.5~250 Hz的中高频段,能量百分比达到55%;冲击地压发生时,波形起伏明显,振幅明显增大,低频信号占主要成分,能量主要集中在0~62.5 Hz的低频段,且占总能量的70%左右。得出冲击地压前兆:振幅增加,微震主频明显降低,频带由高频向低频发展,且前震发生的时间越靠近主震,低频信号越多,低频信号所占的能量百分比也越大。因此,可将微震主频急剧降低、振幅明显升高、低频信号能量百分比增加作为冲击地压前兆的主要特征,结合每日微震次数和能量变化趋势进行冲击地压预测。Abstract: The microseism signals before and after rock burst on 1300 working face of a coal mine were collected, and sequential characteristics were analyzed. The db5 is used as wavelet basis function to decompose microseism signals into five layers, and the spectral characteristics and energy percentage of each sub-frequency band are obtained. The analysis results show that before rock burst, the number and energy of microseism increase first and then decrease and then increase; precursor signal appeared one hour before rock burst occurred, the energy is mainly distributed in the middle and high frequency region of 62.5-250 Hz, and the energy percentage reaches 55%; when the rock burst occurs, the amplitude increases significantly, with obvious vibration fluctuations, low frequency signals are the main component, and the energy is mainly concentrated in the low frequency range of 0-62.5 Hz, and accounts for about 70% of the total energy. Precursory of rock burst is obtained: the amplitude increases, the frequency of the microseism is significantly reduced, the frequency band develops from high frequency to low frequency, and low frequency signal increases as the time of foreshock occurs closer to the main shock, the percentage of energy occupied by the low frequency signal also increases. Therefore, sharp reduction of main frequency of the microseismic, obvious increase of amplitude and increase of energy percentage of low-frequency signals can be used as the main feature of precursory of rock burst, combined with daily microseismic times and energy trends, rock burst can be predicted.
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Key words:
- rock burst /
- microseism /
- precursory signal /
- wavelet analysis /
- spectral characteristic /
- energy percentage
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