Event-Centric Earthquake Monitoring with a Multi-Station Transformer
Earthquake catalogs underpin studies such as fault structure, earthquake processes and seismic hazard, and their value depends on being both complete and reliable. Machine learning has improved individual tasks of earthquake monitoring, but processing stations independently discards the consistency of arrivals across the network, and running tasks in sequence fragments the information describing each earthquake. We present EQNet2, a geometry-aware multi-station Transformer that jointly predicts phase arrivals, first-motion polarities, source location and magnitude, and the association of arrivals into events. On a Northern California test dataset and on continuous waveforms from Ridgecres...
| Authors | Haoyu Wang, Weiqiang Zhu |
|---|---|
| Primary category | physics.geo-ph |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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