Improved U-Net3+ Network for First Arrival Picking of Noisy Earthquake Recordings

Determining the precise arrival time of earthquake recordings in noisy settings is crucial for effective earthquake data processing and analysis. This study introduces a novel U-Net3+-based network specially designed for accurate first arrival picking in noisy data. To reduce information loss in seismic waveform extraction, enhancements were made to the U-Net3+ decoder, featuring a residual structure for better information propagation, a convolutional layer replacing the pooling layer to preserve high-frequency seismic waveform details, and a convolutional block attention module (CBAM) integrated into the deeper layers to prioritize critical information at the onset of the seismic wave. We evaluated our model against U-Net, U-Net2+, U-Net3+, and Phase-Net using noisy earthquake recordings, and carried out comprehensive ablation studies on each module. The model’s generalizability was further validated using the “DiTing” dataset. The proposed network model demonstrated superior accuracy and robustness against noise, with picking rates exceeding 92.42%, 90.82%, and 87.81% when signal-to-noise ratios (SNRs) dropped below 20, 10, and 5 dB, respectively. This work contributes a promising approach to developing a more noise-resistant, low-cost seismic phase picking model, and offers potential significant gains for seismic monitoring techniques.