arXiv cs.AI· Haoyu Wang, Weiqiang Zhu·· 4 天前AI 评分20
EQNet2：用多站 Transformer 实现以事件为中心的地震监测
Event-Centric Earthquake Monitoring with a Multi-Station Transformer
AI 导读
EQNet2 是一个几何感知的多站 Transformer，可联合预测震相到时、初动极性、震源位置与震级，并将到时关联为地震事件。在北加州测试数据集以及 Ridgecrest 和旧金山湾区的连续波形上，它恢复了更多微弱到时、产生更少误检，并将更多拾取关联为已定位事件。其学习到的注意力沿时间—距离模式跨台站连接信息，与地震走时规律一致。
正文 · 原文
Abstract: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 Ridgecrest and the San Francisco Bay Area, EQNet2 recovers more weak arrivals, produces fewer false detections, and associates more picks into located events. Its learned attention links information across stations along time--distance patterns consistent with seismic moveout. As seismic networks grow denser, exploiting multi-station coherence and event-centric prediction offers a path to more complete and reliable catalogs.
| Comments: | 17 pages and 7 figures in main body; 16 figures and 4 tables in supplementary |
| Subjects: | Geophysics (physics.geo-ph); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.17404 [physics.geo-ph] |
| (or arXiv:2610.17404v1 [physics.geo-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17404 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Haoyu Wang [view email]
[v1]
Mon, 12 Oct 2026 21:11:24 UTC (12,834 KB)
来源：arXiv cs.AI · arxiv.org