Deep learning for P-wave first-motion polarity determination and its application in focal mechanism inversion

The focal mechanism provides seismological constraints on the geological faults that generate the earthquakes and thus is important for regional seismotectonic research. Focal mechanism calculation based on the P-wave first-motion-polarity is a widely used method, particularly helpful for small to moderate-size earthquakes. However, determining the P-wave first-motion polarity can be challenging and subjective for smaller earthquakes. Here, we propose a deep-learning method (EQpolarity) for determining the P-wave first-motion polarity using the vertical-component seismic waveforms. The proposed deep-learning method was trained using a large-scale dataset from South California and then adapted to the Texas earthquake data via a transfer learning method. The original and secondary models obtained 95.43% and 98.82% accuracy on the Texas database, respectively, indicating the effectiveness of transfer learning. We further apply the deep learning method to thousands of events on the TexNet catalog to determine the focal mechanisms. Most of the focal mechanism solutions align well with the strikes, dips, and rakes of the known faults that were explored previously using full-waveform-based methods. The generation of the large focal mechanism database offers significant insights into the seismotectonic status of West Texas. The open-source package of EQpolarity can be accessed at https://github.com/chenyk1990/eqpolarity.