Seismic phase association connects earthquake arrival‐time measurements to their causative sources. Effective association must determine the number of discrete events, their location, and origin times, and it must differentiate real arrivals from measurement artifacts. The advent of deep‐learning (DL) pickers, which provide high rates of picks from closely overlapping small‐magnitude earthquakes, motivates revisiting the phase association problem and approaching it using the methods of DL. We have developed a graph neural network associator that simultaneously predicts both source space–time localization, and discrete source‐arrival association likelihoods. The method is applicable to arbitrary geometry, time‐varying seismic networks of hundreds of stations, and is robust to high rates of sources and input picks with variable noise and quality. Our Graph Earthquake Neural Interpretation Engine (GENIE) uses one graph to represent the station set and another to represent the spatial source region. GENIE learns relationships from data in this combined representation that enable it to determine robust source and source‐arrival associations. We train on synthetic data, and test our method on real data from the northern California seismic network using input generated by the PhaseNet DL phase picker. We successfully re‐detect ∼96% of all events M >1 reported by the U.S. Geological Survey (USGS) during 500 random days between 2000 and 2022. Over a 100‐day continuous interval of processing in 2017–2018, we detect ∼4× the number of events reported by the USGS. Our new events have small‐magnitude estimates below the magnitude of completeness of the USGS catalog, and are located close to the active faults and quarries in the region. Our results demonstrate that GENIE can effectively solve the association problem under complex seismic monitoring conditions.

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