Benchmarking seismic phase associators: Insights from synthetic scenarios

In seismology, accurately associating seismic phases to their respective events is crucial for constructing reliable seismicity catalogs. This study presents a comprehensive benchmark analysis of five seismic phase associators, including machine learning based solutions, employing synthetic datasets tailored to replicate the seismicity characteristics of real seismic data in a crustal and a subduction zone scenario.

The synthetic datasets were generated using the NonLinLoc raytracer, using real station distributions and velocity models and simulating a large range of seismic events across different depths. In order to generate sets of picks with quality and diversity similar to a real-world dataset, some modifications such as adjustments to arrival times simulating picking errors, selective station exclusion, incorporation of false picks, were included. Such a controlled environment allowed for the assessment of associator performance under a range of different conditions.

As part of project MILESTONE, we compared the performance of five state-of-the-art seismic phase associators (PhaseLink, GaMMA, REAL, GENIE, and PyOcto) across multiple scenarios, including low-noise environments, high-noise background activity, out-of-network events, and complex aftershock sequences. Each associator's accuracy in identifying and associating true events amidst noise picks and its ability to handle overlapping sets of arrival times from different events were rigorously evaluated.

Additionally, we conducted a systematic comparison of the advantages and disadvantages of each associator, attempting a fair and unbiased evaluation. This included assessing their processing times, a critical factor in operational seismology. Our findings reveal significant differences in the precision and robustness of these associators.

This benchmark study not only underscores the importance of robust phase association in seismological research but also paves the way for future enhancements in seismic data processing techniques. The insights gained from this analysis are expected to significantly contribute to the ongoing efforts in seismic monitoring and hazard assessment, particularly in the realm of machine learning applications.