An Energy-based Method for Automatically Picking Noisy Microseimic Data

First-arrival picking is a critical step in processing microseismic data. Different automated lgorithms have been developed to identify first-arrival times in large microseismic datasets. However, accurately determining these times remains challenging due to low signal-to-noise ratios and the complex geological structures encountered in microseismic monitoring. To address this issue, we propose a new transformed characteristic function (TCF) derived from enhanced differences between multi-window energy ratios to minimize the impact of noise. The process begins with defining the characteristic function (CF), followed by introducing a detection method that utilizes the transformed CF (TCF) to identify first-arrival times. The transformation process captures the features of first-arrival points observed in manual picking and can be adjusted based on CF parameters, ensuring consistent and reliable results even under varying parameter settings. To validate the TCF method, we applied it to field data collected from a microseismic monitoring network on an unstable rock face. The method exhibited excellent performance under low signal-to-noise ratio conditions, outperforming traditional first-arrival picking methods with higher accuracy.