Multi-master event waveform stacking microseismic location method based on time-frequency transformation

Waveform stacking is frequently employed in the analysis of microseismic data due to its adaptability, effectiveness, and noise immunity. However, the low signal-to-noise ratio and inaccurate velocity model have a considerable negative impact on the waveform stacking location method's performance. A time-frequency transform-based characteristic function and the addition of a multi-master event method to waveform stacking location are presented as solutions to the aforementioned issues. In this paper, non-smooth statistical features of characteristic function are extracted and enhanced using time-frequency transform. Based on the two-pair double-difference method, multi-master events are introduced and a reference criterion for selecting the master events is proposed. Experiments with simulated and actual microseismic data show that the conversion of time-domain signals into time-frequency signals by wavelet transform can enhance the peak capacity of the signal and reduce the background noise, enhancing the imaging resolution. The introduction of multi-master events not only reduces the sensitivity of the waveform stacking method to velocity error but also improves the clustering effect of the master event method.