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• W4309609819 abstract "Earthquake detection and seismic phase picking play a crucial role in the travel-time estimation of P and S waves, which is an important step in locating the hypocenter of an event. The phase-arrival time is usually picked manually. However, its capacity is restricted by available resources and time. Moreover, noisy seismic data present an additional challenge for fast and accurate phase picking. We propose a deep learning-based model, EPick, as a rapid and robust alternative for seismic event detection and phase picking. By incorporating the attention mechanism into UNet, EPick can address different levels of deep features, and the decoder can take full advantage of the multi-scale features learned from the encoder part to achieve precise phase picking. Experimental results demonstrate that EPick achieves 98.80% accuracy in earthquake detection over the STA/LTA with 80% accuracy, and for phase arrival time picking, EPick reduces the absolute mean errors of P- and S- phase picking from 0.072 s (AR picker) to 0.030 s and from 0.189 s (AR picker) to 0.083 s, respectively. The result of the model generalization test shows EPick’s robustness when tested on a different seismic dataset." @default.
• W4309609819 created "2022-11-28" @default.
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• W4309609819 date "2022-11-17" @default.
• W4309609819 modified "2023-10-10" @default.
• W4309609819 title "EPick: Attention-based multi-scale UNet for earthquake detection and seismic phase picking" @default.
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• W4309609819 doi "https://doi.org/10.3389/feart.2022.953007" @default.
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