Recursive-filtering-based microseismic event picking under wireless channel fading and measurement outliers.

This study focuses on addressing the challenge of recursive state estimation in microseismic event detection affected by wireless channel attenuation and measurement outliers (MOs). Existing methods face difficulties in accurately picking the first arrival event due to the combined effects of signal distortion from wireless channel fading and the inability to distinguish and suppress measurement outliers effectively. To overcome these challenges, the signal model for the microseismic system with wireless transmission channel fading is established in this paper, which reflects real-world engineering scenarios. Subsequently, a recursive filter incorporating a self-adaptive saturation function (SSF) is proposed to mitigate the adverse effects of MOs on arrival time picking accuracy. The filter gain is derived by minimizing the upper bound matrix of the filter error covariance, and a sufficient condition is proposed to ensure that the filtering error is mean-square exponentially bounded. The presented algorithm effectively suppresses the negative impact of MOs and enhances filtering performance in scenarios with wireless channel fading. The experimental results demonstrate the superiority and effectiveness of the developed approach.