Fast-AIC Method for Automatic First Arrivals Picking of Microseismic Event With Multitrace Energy Stacking Envelope Summation

Abstract

As the signal-to-noise ratio (SNR) of surface microseismic monitoring data is generally low and large, traditional detection and picking algorithms cannot satisfy the real-time and high accuracy to processing. Therefore, a Fast Akaike information criterion (Fast-AIC) algorithm is proposed for microseismic event automatic detection and first arrival time picking. First, an automatic detection method of microseismic events based on multitrace energy stacking is proposed, to avoid the missed detection and false detection in conventional automatic detection methods. Second, the Fast-AIC algorithm is developed by mathematical derivation from the Vector Auto-regressive (VAR-AIC algorithm), to improve the efficiency of first arrival time picking of microseismic signals. Finally, the new method and three other conventional first arrival time picking methods are tested on microseismic monitoring data from a hydraulic fracture site in Shanxi, China. We have found that the new method has the highest picking accuracy and computational efficiency.