Improvement of Autoregressive Model-Based Algorithms for Picking the Arrival Times of the P-Wave of Rock Acoustic Emission - Wang, Guilin; He, Chenhao; Liang, Feng; Gong, Sheng (Geotechnical and geological engineering, 2023)
Acoustic emission (AE) monitoring technology has been widely used in rock engineering. In addition, the accurate picking of P-wave arrival times is the key to in-depth rock mechanics and AE research. Autoregressive (AR) model-based algorithms such as AR-Akaike information criterion (AR-AIC) and AR-Bayesian information criterion (AR-BIC) are efficient methods currently adopted for P-wave arrival time picking; however, their picking results sometimes have large errors, owing to the complexity of amplitude distribution in the time series of AE waveform data. To minimize these errors, this study improved the adopted algorithms by leveraging the surge phenomenon in AR model variance. Specifically, a single-segment AR-BIC algorithm was developed by pre-processing the time series of AE waveform data. It was verified that the improved AR-BIC algorithm achieved a 50% higher efficiency than the conventional algorithm, including a picking accuracy above 98.5%.