RED-PAN: Real-Time Earthquake Detection and Phase-Picking With Multitask Attention Network

Wu Yu Liao, En Jui Lee, Da Yi Chen, Po Chen, Dawei Mu, Yih Min Wu

研究成果: Article同行評審

7 引文 斯高帕斯(Scopus)


In this article, we show that the real-time earthquake detection and phase picking with multitask attention network (RED-PAN) can carry out earthquake detection and seismic phase picking on real-time and continuous data with appropriate data augmentation. Goal-oriented data augmentations materialize the capability of RED-PAN. Mosaic waveform augmentation (MWA) synthesizes data conditioned by superimposed earthquake waveforms, marching MWA (MMWA) extends MWA to allow the dynamic input of seismograms, and earthquake early warning augmentation (EEWA) enables to identify P arrivals using the early part of P -wave waveforms. For stable P and S arrival probability distribution functions (pdfs) of continuous recordings, we use the median values of phase predictions at each time point until the model scans through, which we term the seismogram-tracking median filter (STMF). For real-time P arrival detection, we use a threshold (0.3) on the real-time P arrival pdf as the trigger criterion. We examined our proposed strategy in different application scenarios. For the dataset of the fixed-length samples, our RED-PAN(60 s) model performs similar to EQTransformer (EqT) on the STanford EArthquake Dataset (STEAD) and outperforms the Taiwan dataset. For continuous data examination of the 2019 Ridgecrest earthquake sequence, the number of earthquake waveforms detected by our RED-PAN(60 s) model is 2.7 times the number of EqT under the same receptive field (60-s-long seismogram). In the application of earthquake early warning (EEW), our RED-PAN(60 s) model only requires the P -wave waveform about 0.13 s long from the P -alert and 0.09 s long from the Taiwan Strong Motion Instrumentation Program (TSMIP) network. The source code is available at

All Science Journal Classification (ASJC) codes

  • 電氣與電子工程
  • 一般地球與行星科學


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