PhaseNet (GJI 2019)
github.com/ai4eps/phasenetU-Net-style deep neural network for P/S seismic arrival-time picking trained on millions of waveforms from the Northern California Earthquake Data Center, achieving near-analyst picking precision at orders-of-magnitude higher speed and robustness to low signal-to-noise traces where STA/LTA fails; a foundational reference for deep-learning phase picking, integrated into SeisBench model collections and national seismic networks, with PhaseNet-DAS extending it to distributed acoustic sensing (Stanford AI4EPS, 386+ stars, MIT License, actively maintained)
Sourced from
- GitHub — github.com/ai4eps/phasenet
- Awesome AI for Science — github.com/ai4eps/phasenet
Related resources
Self-attention transformer performing simultaneous earthquake detection and P/S phase picking on continuous seismic waveforms, trained on the large-scale STEAD benchmark dataset and outperforming legacy detection methods with far fewer false positives; widely adopted by seismological observatories for earthquake monitoring, included in SeisBench model collections, and extended by efficient EQT-Mini/EQT-Lite successors for real-time deployment (Stanford, 418+ stars, MIT License, actively maintained)
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