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

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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> arrivals using the early part of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> -wave waveforms. For stable <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> arrival detection, we use a threshold (0.3) on the real-time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> -wave waveform about 0.13 s long from the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> -alert and 0.09 s long from the Taiwan Strong Motion Instrumentation Program (TSMIP) network. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/tso1257771/RED-PAN</uri> .