A wavelet transform method for studying the energy distribution characteristics of microseismicities associated rock failure

Abstract

Microseismicity signals released during rock failure process are firstly recorded using microseismicity monitoring system. A wavelet transform scheme is then developed on the basis of the discrete wavelet transform and implemented into MATLAB to study the energy distribution characteristics of the monitored microseismicity signals. The wavelet transform scheme decomposes the recorded microseismicity signals into various wavelets at seven scales and eight frequency bands. The microseismicity energy at each frequency band is then calculated by integrating the wavelets in each scale. It is found that, for the microseismicity signals recorded during the uniaxial loading of the granite, the microseismicity energies are mainly distributed between the bands 7.8125–15.625 kHz, 15.625–31.250 kHz and 31.25–62.5 kHz and the percentages of the released energies at these frequency bands are 8.24%, 62.72%, 28.08% of the total energies, respectively. The results reveal that the microseismicity energies at these levels are directly related to the damage mechanisms of the granite although further studies are need to identify the failure modes. Then these monitored signals were processed using wavelet transformation to find out the frequency distribution rule and the frequency band energy varying rule of the acoustic emission (AE) signals during the different rock damage and failure stages. The rock failure mechanism was interpreted from the perspective of the relationship between AE signal frequency change and crack propagation. The frequency band energy distribution histograms of the microseismicity signals at different damage stages were computed and drawn by the energy calculation method of wavelet transformation implemented into MATLAB. The energy percentage of the low frequency band (d7-a7) and that of the dominated frequency band (d4-d6) and their variation rule were analyzed especially. Accordingly, the critical damage point is that the low frequency energy percentage is above a certain threshold. This index could be used as the failure precursor criterion for rock mass instability monitoring and early warning, since it provided a theoretical guidance for evaluating internal damage of rock. Finally, it is concluded that the proposed wavelet transform method may provide a new mean for the characteristics analysis of the microseismicity signals recorded by the microseismicity monitoring system and may stimulate the application of the microseismicity monitoring technology in the geotechnical and mining engineerings through analyzing the energy of the microseismicity signals to understand the law of microseismicity emitted by rockmass.