Automatic time picking of microseismic data based on shearlet-AIC algorithm

Time picking is a crucial step in microseismic data processing. The picking results have a great influence on the orientation of hypocenter location. Especially when the signal-to-noise ratio (SNR) of data is low, it is difficult to obtain arrival times accurately with conventional approaches. To solve the question above, this paper proposes a new time-picking approach based on the Akaike Information Criterion (AIC) and shearlet transform named the shearlet-AIC, which can accurately pick the arrival times. With the proposed method, the downhole microseismic data can be divided into several scales according to the different statistical characteristic between signals and noise and obtain the feature of a different frequency domain. We can acquire the minimum values that represent the picking results in every scale of the frequency domain by using the shearlet-AIC. To verify the reliability of the proposed method, we conduct it on both synthetic and field datasets that were recorded with a vertical array of receivers. The experimental results show that our method can precisely pick the arrival times of P-waves even when the SNR of data is as low as − 8 dB and the accuracy is superior to the other methods mentioned in our paper.