Abstract
Earthquakes are disasters that threaten the operational safety of high-speed railways. To obtain reliable alerts for the earthquake monitoring and early warning systems of high-speed railways, based on magnitude and peak ground acceleration (PGA) thresholds (M = 5.5 and PGA = 40 cm/s2), an earthquake early warning (EEW) method for high-speed railways using deep learning is proposed. And the application of deep learning method in EEW for high-speed railway is explored. We design a single-station deep learning network architecture (named the CT architecture) by combining convolutional neural and transformer networks, and with that architecture, we train two separate models (CT-M and CT-PGA models) using the strong motion data recorded from the Kyoshin Network in Japan, which are used to predict whether the magnitude and PGA exceed the thresholds for issuing an alert. To verify the robustness of the method, we apply it to the M7.3 earthquake and M7.4 earthquake off the coast of Fukushima in 2021–2022. Results show that within 10 s after P-wave arrival, the accuracy of the alert reaches 90 %, and the average observed lead time reaches 18 s. The proposed method displays potential application on EEW systems for high-speed railways.
Published Version
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