PhaseNet: a deep-neural-network-based seismic arrival-time picking method
Jun 12,2025
This paper introduces PhaseNet, a deep neural network designed for seismic arrival-time picking. PhaseNet uses three-component seismic waveforms as input and outputs probability distributions for P-wave arrivals, S-wave arrivals, and noise. Trained on a large dataset of manually labeled P and S arrival times, PhaseNet achieves higher accuracy and recall rates compared to traditional methods like STA/LTA. The model demonstrates robust performance across different instrument types and signal-to-noise ratios, and it can be applied to continuous data for earthquake detection. The study highlights PhaseNet's potential to improve earthquake monitoring and S-wave velocity modeling.
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