A multi-window algorithm for real-time automatic detection and picking of P-phases of microseismic events

Zuolin Chen and Robert R. Stewart


There exist a variety of algorithms for the detection and picking of a seismic event in real-time seismic monitoring. However, all have some limitations. We propose and test a multi-window algorithm for the automatic detection and picking of impulsive P-phases of seismic events in low SNR (signal-to-noise ratio) environments. This method employs both the instantaneous and the averaged absolute amplitude of traces in several time windows before and after each time point (sample) as the characteristic functions. When the instantaneous absolute value of a characteristic function exceeds an automatically adjusted dynamic threshold, ratios based on the averages of the windows over time samples provide parameters to differentiate an expected event from unwanted noise. Examination of the algorithm by using synthetic and real data shows that the picking accuracy of impulsive first arrivals can be less than 1-2 samples even when the signal-to-noise ratio is lower than 3.

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