An automatic arrival time picking algorithm of P-wave based on adaptive characteristic function

Abstract

The accuracy of arrival time and picking speed are significant issues in terms of micro-seismic identification, location, travel-time tomography, as well as source mechanism. In order to detect small changes in signal amplitude and frequency with different signal-to-noise ratios (SNRs), the Allen's characteristic function (Allen's CF) was modified to an adaptive characteristic function (ACF) by relative time series and relative energy coefficients which was compared with the Allen's CF, Baer's CF, and Sedlak's CF. Then, an algorithm for S/L-AIC picker based on ACF for automatic P-wave arrival time picking was presented by the short-term-average to long-term-average ratios (STA/LTA) picker and the Akaike Information Criterion (AIC). In addition, the algorithm was applied to a dataset acquired by a local micro-seismic network at the Jing-Gong coal mine in Shanxi, China, which was compared with a two-step AIC picker, using the original manual selection as a reference to verify the reliability and robustness of the algorithm. Furthermore, the influence of five basic parameters on the accuracy of the S/L-AIC picker based on ACF was discussed by orthogonal tests. The results highlighted that: 81 % of the automatic picks processed by the S/L-AIC picker based on ACF were within +4 ms of deviation, while 66.3 % of the automatic picks processed by the two-step AIC picker were within the same error range; Compared with the other CFs, the ACF applied to the S/L-AIC algorithm performs better for low-SNR data and confirms more accurate and stable results. This study highlights that accurate automatic P-wave arrival time determination is feasible when using the S/L-AIC picker based on ACF.