Group A, Poster #013, Seismology
Improvements to a Graph Neural Network Earthquake Phase Associator
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Poster Presentation
2023 SCEC Annual Meeting, Poster #013, SCEC Contribution #13213 VIEW PDF
rce region, the combination of which effectively allows solving the association problem. In previous work we demonstrated that when GENIE is trained with synthetic data, it can accurately detect regional seismicity throughout northern California, recover >95% of NCEDC reported events, and increase the total detection rate by ~4x. Now, we demonstrate results for the same application where we train on both synthetic and real data, and also use the phase-type information of picks supplied by deep learning phase pickers, such as PhaseNet. We highlight the improvements in the spatial localization and rate of recovered events obtained through these adjustments to the model, and demonstrate the ease with which our publicly available code can be adapted to new regions.
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