Zachary Ross

PhaseLink: A Deep Learning Approach to Seismic Phase Association Zachary Ross California Institute of Technology

We present PhaseLink, a deep learning approach to seismic phase association. Seismic phase association is a fundamental task in seismology that pertains to linking together phase detections on different sensors that originate from a common earthquake. This task can be challenging because the number of sources is unknown, events frequently overlap in time, or can occur simultaneously in different parts of a network. Our PhaseLink approach has many desirable properties. First, it is trained entirely on synthetic simulated data (i.e., "sim-to-real"), and is thus easily portable to any tectonic regime. Second, it is straightforward to tune PhaseLink by simply adding examples of problematic cases to the training dataset -- whereas conventional approaches require laborious adjusting of ad hoc hyperparameters. Third, we empirically demonstrate state-of-the-art performance in a wide range of settings. For instance, PhaseLink can precisely associate P- and S-picks to events that are separated by ~12 seconds in origin time. We expect PhaseLink to substantially improve many aspects of seismic analysis, including the resolution of seismicity catalogs, real-time seismic monitoring, and streamlined processing of large seismic