Deriving focal mechanism solutions of small to moderate earthquakes in Sichuan, China via a deep learning method

Abstract

As one of the most seismically active regions, Sichuan basin is a key area of seismological studies in China. This study applies a neural network model with attention mechanisms, simultaneously picking the P-wave arrival times and determining the first-motion polarity. The polarity information is subsequently used to derive source focal mechanisms. The model is trained and tested using small to moderate earthquake data from June to December 2019 in Sichuan. We apply the trained model to predict first-motion polarity directions of earthquake recordings in Sichuan from January to May 2019, and then derive focal mechanism solutions using HASH algorithm with predicted results. Compared with the source mechanism solutions obtained by manual processing, the deep learning method picks more polarities from smaller events, resulting in more focal mechanism solutions. The catalog documents focal mechanism solutions of 22 events (M_L 2.6–4.8) from analysts during this period, whereas we obtain focal mechanism solutions of 53 events (M_L 1.9–4.8) through the deep learning method. The derived focal mechanism solutions for the same events are consistent with the manual solutions. This method provides an efficient way for the source mechanism inversion of small to moderate earthquakes in Sichuan region, with high stability and reliability.