Evaluating Multi-station Phase Picking Algorithm Phase Neural Operator (PhaseNO) on Local Seismic Networks

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ID: 319034
2026
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Abstract
Summary Reliable automatic phase picking is important for many seismic applications. With the development of machine learning approaches, many algorithms are proposed, evaluated and applied to different areas. Many of these algorithms are single station based, while recent proposed methods start to combine surrounding stations into consideration in the problem of phase picking. Among these algorithms, the Phase Neural Operator (PhaseNO) shows promising results on regional datasets comparing to existing algorithms. But there are many use cases for the local seismic networks in our community, therefore in this paper we evaluate the performance of PhaseNO on 4 different local datasets and compare the results to PhaseNet and EQTransformer. We used both individual phase picking metrics as well as association metrics to illustrate the performance of PhaseNO. By manually reviewing the newly detected events, we find the PhaseNO model outperforms the single station-based approaches in the local-scale use cases due to its consideration of coherent signals from multiple stations. We also explored PhaseNO’s behaviors when only using one station, as well as gradually increasing the number of stations in the seismic network to better understand its behavior. Overall, using the off-the-shelf machine learning based phase pickers, PhaseNO demonstrated its good performance on local-scale seismic networks.
Reference Key
openalex_W4415877567 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Qingkai Kong, Avigyan Chatterjee, Chengping Chai, Alex Dzubay, Kayla Kroll, J. C. Stachnik, Scott Fertig, J. Y. Liefer, P. A. Friberg
Journal geophysical journal international
Year 2026
DOI
10.1093/gji/ggag242
URL
https://doi.org/10.1093/gji/ggag242
Keywords Keywords not found

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