Automatic detection of arrival time for noisy microseismic data using a transformed difference between multiwindow energy ratios method

Detection of arrival times of microseismic events is one of the most fundamental steps in the application of microseismic monitoring. However, accurately determining arrival time remains challenging due to low signal-to-noise ratio and the complexity of geological structures in microseismic monitoring. To address this, we propose a new arrival time detection strategy, which is implemented by using a transformed difference between multiwindow energy ratios method (TDER). First, we use a modified difference between multiwindow energy ratios (DER') to characterize microseismic traces. Then, we introduce a detection method for detecting arrival times using a transformed DER', i.e., TDER. The transforming process can extract the feature of arrival points in manual picking. To establish and validate the TDER method, we use pseudo-synthetic and field data. The method's sensitivity to varying noise levels was tested using pseudo-synthetic data combined with real noise. Two field datasets were collected from a microseismic monitoring system deployed on an unstable rock face and an active tomography survey on a mountain, respectively. This method demonstrated good performance in low signal-to-noise ratio (SNR) scenarios and outperformed traditional STA/LTA and the original DER picking methods with superior accuracy and fewer failed detection. We also examined how changes in parameters affect the TDER picking results. The developed method is an adaptive and nearly parameter-free method, which can be easily implemented.