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Seismic phase association is an essential task for characterising seismicity:given a collection of phase picks, identify all seismic events in the data. Inrecent years, machine learning pickers have lead to a rapid growth in thenumber of seismic phase picks. Even though new associators have been suggested,these suffer from long runtimes and sensitivity issues when faced with denseseismic sequences. Here we introduce PyOcto, a novel phase associator tacklingthese issues. PyOcto uses 4D space-time partitioning and can employ homogeneousand 1D velocity models. We benchmark PyOcto against popular state of the artassociators on two synthetic scenarios and a real, dense aftershock sequence.PyOcto consistently achieves detection sensitivities on par or above currentalgorithms. Furthermore, its runtime is consistently at least 10 times lower,with many scenarios reaching speedup factors above 50. On the challenging 2014Iquique earthquake sequence, PyOcto achieves excellent detection capabilitywhile maintaining a speedup factor of at least 70 against the other models.PyOcto is available as an open source tool for Python on Github and throughPyPI.

PyOcto: A high-throughput seismic phase associator