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Abstract 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. It is widely employed to detect earthquakes on permanent and temporary seismic networks and underlies most seismicity catalogs produced around the world. 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. We present PhaseLink, a framework based on recent advances in deep learning for grid‐free earthquake phase association. Our approach learns to link phases together that share a common origin and is trained entirely on millions of synthetic sequences ofPandSwave arrival times generated using a 1‐D velocity model. Our approach is simple to implement for any tectonic regime, suitable for real‐time processing, and can naturally incorporate errors in arrival time picks. Rather than tuning a set of ad hoc hyperparameters to improve performance, PhaseLink can be improved by simply adding examples of problematic cases to the training data set. We demonstrate the state‐of‐the‐art performance of PhaseLink on a challenging sequence from southern California and synthesized sequences from Japan designed to test the point at which the method fails. For the examined data sets, PhaseLink can precisely associate phases to events that occur only ∼12 s apart in origin time. This approach is expected to improve the resolution of seismicity catalogs, add stability to real‐time seismic monitoring, and streamline automated processing of large seismic data sets. 

Abstract The 2010M_W7.2 El Mayor‐Cucapah earthquake ruptured a zone of ~120 km in length in northern Baja California. The geographic distribution of this earthquake sequence was well constrained by waveform relocation. The depth distribution, however, was poorly determined as it is near the edge of, or outside, the Southern California Seismic Network. Here we use two complementary methods to constrain the focal depths of moderate‐sized events (M≥ 4.0) in this sequence. We first determine the absolute earthquake depth by modeling the regional depth phases at high frequencies (~1 Hz). We mainly focus onPnand its depth phasespPnandsPn, which arrive early at regional distance and are less contaminated by crustal multiples. To facilitate depth phase identification and to improve signal‐to‐noise ratio, we take advantage of the dense Southern California Seismic Network and use array analysis to align and stackPnwaveforms. For events without clear depth phases, we further determine their relative depths with respect to those with known depths using differential travel times of thePn, directP, and directSphases recorded for event pairs. Focal depths of 93 out of 122M≥ 4.0 events are tightly constrained with absolute uncertainty of about 1 km. Aftershocks are clustered in the depth range of 3–10 km, suggesting a relatively shallow seismogenic zone, consistent with high surface heat flow in this region. Most aftershocks are located outside or near the lower terminus of coseismic high‐slip patches of the main shock, which may be governed by residual strains, local stress concentration, or postseismic slip.