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Nowadays, intelligent mining is the global mining technology hotspot and the inevitable trend of mine development,especially in the environment where the safety, high efficiency and sustainable development of mines are directly restricted by the underground pressure disaster, which caused by high stress and mining disturbance. Microseismic monitoring technology is one of key technologies in underground pressure monitoring, which provides safety assessment on mine by analyzing microseismic signals to obtain their source location. The accuracy of P-phase onset time plays a key role in source location, where short term average/long term average (STA/LTA) method is the most wildly used method in P wave detection. However, the microseismic signal is too weak that can easily be affected by noise from underground mining, and STA/LTA ratio is also highly influenced. In this paper, a double-filtering algorithm based on band-pass filter and synchrosqueezing wavelet transform is proposed to remove the noise from field microseismic data. The results show that the double-filtering algorithm can greatly enhance the effect of STA/LTA, where more accurate P-phase onset time is obtained at the same time.

A Study on Enhancing the Accuracy of P Wave Detection based on Double-Filtering Algorithm for Microseismic Signals from Underground Mines