Automatic microseismic event detection based on multi-channel semblance coefficient

Abstract

Microseismic event detection is a crucial processing step in microseismic monitoring technique. Conventional event detection methods, such as the STA/LTA method, are limited to the detection of the events with high signal-to-noise ratios (SNR), thus they usually fail to identify certain events when the ambient noise is high. To solve this problem, we develop a new method to detect the low SNR microseismic events in this paper. Our method adopts a semblance coefficient to quantitatively determine the waveform similarity of windowed record segments after moveout correction, and uses it as the detector of the microseismic events. Application of our method on both synthetic and field datasets demonstrates that this method can successfully identify the microseismic events with a very low signal-to-noise ratio whilst having a much lower false trigger rate compared to the STA/LTA method.