Abstract: Acoustic emission (AE) monitoring technology is widely used in indoor mechanical test and concrete damage assessment, while microseismic monitoring technology is widely used in mine earthquake and rock burst disaster warning. P-wave arrival time picking is fundamental and crucial for both technologies. Processing and analyzing the P-wave arrival time picking data enables the determination of microsource locations and the retrieval of fracture mechanisms. The AR-AIC algorithm, based on the autoregressive model (AR model) and akaike information criterion (AIC), is presently the most commonly used method due to its high accuracy in picking. However, the double-sequence AR model restricts the overall efficiency of picking, and the picking performance under low signal-to-noise ratio and spike signals is also unstable. A more efficient, accurate and stable picking method, autoregressive model-variance surge phenomenon (AR-VSP) algorithm, is proposed by using the variance surge effect of single sequence AR model after signal arrival and introducing a reduction factor based on variance term. The accuracy rate of the AR-VSP algorithm is 96%, surpassing the 91% accuracy of the AR-AIC algorithm. Additionally, the picking speed has increased by 50.06%, and the algorithm exhibits enhanced anti-spike interference capabilities. The AR-VSP algorithm is more robust compared to the AR-AIC algorithm.

Key words: acoustic emission (AE), microseismic, arrival time picking, autoregressive (AR) model, steep increase in variance