Multistation seismic location via machine learning: application to Oklahoma and Southern California

SUMMARY Locating earthquakes plays an important role in the study of seismic activity and geological structures. Traditional methods for locating earthquakes mainly rely on waveform matching and traveltime fitting. With the development of artificial intelligence technology, machine learning methods have often been applied to locate earthquakes. However, current machine learning approaches may face challenges related to physical constraints. In this study, we build a 3-D U-Net network with station distribution constraints to locate earthquakes. To improve the generalizability of the network model, we apply data augmentation techniques, including data shifting, selection, rotation and fusion. The location results are evaluated using a testing data set from Oklahoma, showing an average location error of about 5 km. The origin time can be determined based on the earthquake's location and the waveforms recorded by stations through waveform shifting and stacking. This method does not require the complex processing steps of traditional seismic approaches, allowing for rapid earthquake location. Additionally, we apply the network model to data recorded in Southern California through transfer learning for further application. The results show that this new method is stable and generalized, making it applicable to earthquake location problems associated with arbitrary station networks. Furthermore, we discuss the effects of data augmentation, network architecture and the Gaussian radius of labels on the outcomes. These insights help us better understand machine learning algorithms and improve the application of deep learning in earthquake location.