Using Artificial Intelligence to Pick P-Wave First-Arrival of the Microseisms: Taking the Aftershock Sequence of Wenchuan Earthquake as an Example

%D 2019
%R 10.13209/j.0479-8023.2018.036
%J Acta Scientiarum Naturalium Universitatis Pekinensis
%P 451-460
%V 55
%N 3
%X
In order to accurately and quickly pick up P-wave first-arrival of a large number of seismic events, deep learning method is introduced into the micro seismic P-wave first-arrival picking problem. The structure of convolution neural network is adjusted to apply to the characteristics of the seismic waveform data and first-arrival picking problem. The algorithm takes a 10s-window three-component seismic waveform data as input instead of scanning the continuous waveform. So the running time is far less than traditional methods such as STA/LTA and template matching. The algorithm is applied to aftershocks of 2008 Wenchuan earthquake in July and August, using 7467 manual picked first-arrival data as training dataset. Among the 1867 testing data, 98.9% of the P arrival times picked using this algorithm have an error less than 0.5 s compare to the results picked manually. This method can still maintain good pick-up capability under the condition of low signal-to-noise ratio.

%U https://xbna.pku.edu.cn/EN/10.13209/j.0479-8023.2018.036