Research on the Initial Arrival Recognition and Judgment Method of Microseismic Signals Based on PELT

Abstract

In microseismic monitoring, accurately identifying the arrival time of the P-wave initial arrival is crucial for the precise location and analysis of microseismic sources. However, due to the typically low energy of microseismic signals and poor signal-to-noise ratio (SNR), existing first-arrival picking algorithms struggle with the accuracy of picking results when dealing with microseismic data of low SNR, as they are greatly affected by strong background noise. To address this issue, this study proposes a new initial arrival identification method, which first employs variational mode decomposition (VMD) and the sample entropy method for denoising microseismic data with a low SNR, and then utilizes the pruned exact linear time (PELT) algorithm to determine the time of the microseismic initial arrival. Compared with the traditional short-term average and long-term average ratio (STA/LTA) algorithm and the Akaike information criterion (AIC) method, the method proposed in this paper demonstrates significant advantages in terms of picking precision and noise resistance.