Comparison of seismic phase association algorithms and their performance.
- 1Institute of Geophysics, Czech Academy of Sciences, Prague, Czech Republic, Geodynamics, Praha 4, Czechia (puente@ig.cas.cz)
- 2Charles University, Faculty of Mathematics and Physics, Department of Geophysics, Prague, Czech Republic
Seismic phase association is a fundamental task for earthquake detection and location workflows, as it gathers individual seismic phases detected on multiple seismic stations and associates them to events.
Current phase picking algorithms are capable of generating large phase datasets, and together with new improved phase association algorithms, they can create larger and more complete earthquake catalogs when applied to dense seismic networks (permanent or temporary).
As part of project MILESTONE, which aims at the automatic creation of large microseismicity catalogs in subduction settings, the present study evaluates the performance of three different phase association algorithms, both by comparing their outputs with a handpicked benchmark dataset and by the retrieval of synthetic events.
For this purpose, we used seismic data from the IPOC (Integrated Plate boundary Observatory Chile) permanent deployment of broadband stations in Northern Chile.
We manually picked P and S phases of raw waveforms on randomly chosen days, with event rates in excess of 100-150 per day. All events that were visually recognizable were picked and located, leading to a dataset to be used as “ground truth”.
We do the phase picking with EQTransformer (Mousavi et al. 2020) and evaluate the performance of three seismic phase associators: 1) PhaseLink (Ross et al. 2019), a deep learning based approach trained on millions of synthetic sequences of P and S arrival times, 2) REAL (Zhang et al. 2020), that combines the advantages of pick-based and waveform-based methods, primarily through counting the number of P and S picks and secondarily from travel-time residuals, and 3) GaMMA (Zhu et al. 2021), an associator that treats the association problem as an unsupervised clustering problem in a probabilistic framework.
In the synthetic test we use NonLinLoc raytracer and add random noise, as well as false picks to simulate an automatic picker output.
In both experiments we evaluate the number of correctly associated and lost events, and the number of constituent picks per event.
How to cite: Puente Huerta, J. A. and Sippl, C.: Comparison of seismic phase association algorithms and their performance., EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6806, https://doi.org/10.5194/egusphere-egu23-6806, 2023.
