The development of seismic event catalogs has undergone a revolution due to the combination of increasingly available continuous waveform data with advanced methods for detecting events and phase arrival times. This began with template matching but has accelerated and generalized with the development of deep learning methods for detecting phase arrival times. Some of the most popular deep learning models for detecting phase arrival times (Zhu and Beroza, 2019; Mousavi et al., 2020) have been applied to a range of applications from quickly delivering refined catalogs of aftershocks after a major event (Liu et al....

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