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| Methods of P-onset picking of acoustic emission compression waves and optimized improvement |
| BAI Tianyang1，WU Shunchuan1，WANG Jinjin2，ZHANG Shihuai1，CHEN Zijian1，XU Miaofei1 |
| (1. Key Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mine，University of Science and Technology Beijing，Beijing 100083，China；2. PowerChina Road Bridge Group Co.，Ltd.，Chongqing 400700，China) |
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Abstract Microseismic and acoustic emission(AE) monitoring during rock fracture process have been widely applied in the area of rock engineering. Automated P-onset picking is a fundamental and key link of location and moment tensor inversion in AE technique. The identification sensitivities regarding the jumping of the amplitude，frequency and phase of the analog signal waveform with the Allen picker，the Baer-Kradolfer picker，the Higher Order Statistic picker and the AR-AIC picker were analyzed for improving the picking accuracy. A comparison of P-onset picking for different signal to noise(SNR) levels by different methods was carried out based on the field monitoring data from the Tunnel Sealing Experiment in Atomic Energy of Canada Limited?s Underground Research Laboratory(URL). The result showed that the Allen picker，the Baer-Kradolfer picker and the Higher Order Statistic picker had a wider scope of SNR identification and better identification ability especially for AE signals at low SNR level. Thus，an improvement for the AR-AIC picker was proposed. The key factors affecting the picking accuracy were discussed and the reasonable parameters applied to AE signals were gained when picking P-onset of real signal waveforms using the improved AR-AIC picker. The P-onset was successfully picked when using the improved AR-AIC picker on AE signals with SNR level below 10，and it indicated that the Kurtosis method was the best picker during the preliminary P-onset detection stage. Comparisons of the results of automatic and manual identification showed that the accuracy rate reached 94% with a time lag less than 5 ?s. The improved AR-AIC picker shows good feasibility in practical application especially for AE signals in low SNR level.
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2016, Vol. 35 
