Deep learning for magnitude prediction in earthquake early warning

Fast and accurate magnitude prediction is the key to the success of earthquake early warning (EEW). However, it is difficult to significantly improve the performance of magnitude prediction by empirically defined characteristic parameters. In this study, we have proposed a new approach (EEWNet) based on deep learning to predict magnitude for EEW. The initial few seconds of P-wave recorded by a single station without any preprocessing is used as the input to EEWNet, and the maximum displacement for the whole record is predicted and by which the magnitude is calculated. A large number of borehole underground strong motion records are used to train, validate and test the proposed EEWNet, and the predicted results are compared against those by empirical peak displacement Pd method. The comparison demonstrates that EEWNet produces better and quicker results than those by Pd, and EEWNet can predict magnitude between 4.0 and 5.9 as early as the first 0.5 s P-wave arrives. EEWNet is therefore expected to significantly enhance the accuracy and speed of magnitude estimation in practical regional EEW systems. (c) 2022 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.

Automated summary

This study proposes a new approach, EEWNet, based on deep learning to predict the magnitude in earthquake early warning systems. By using the initial P-wave recorded by a single station as input without any preprocessing, EEWNet can accurately predict the magnitude. Compared to traditional methods, EEWNet can provide faster magnitude estimates when the P-wave arrives.