An end-to-end multi-task network for early prediction of the instrumental intensity and magnitude in the north–south seismic belt of China

Abstract

The seismic activity in the north–south seismic belt of China is among the highest in the world. Predicting instrumental intensity and magnitude after an earthquake mitigates regional seismic disasters. The standard workflow for prediction involves building empirical formulas using characteristic parameters of the initial arrival seismic wave, but this method has limitations in accuracy. Recent data-driven models have shown promise in predicting instrument intensity and magnitude. Still, this is currently done mainly on a single-task basis and does not consider whether a multi-task model can utilize complementary information from different tasks to improve overall performance. This study proposes a data-driven multi-task model called SeismNet, which can simultaneously predict instrument intensity and magnitude. We tested the effectiveness of SeismNet using ground motion records of the north–south seismic belt of China. The model can predict instrument intensity and magnitude more rapidly and accurately than the baseline and single-task models, with increasing accuracy as the input seismic wave duration increases. We also tested the method on three destructive earthquake events (Ms > 6.5) that occurred in China and found that at 3 s after the P-wave arrival, the prediction is almost consistent with the observation. Overall, this study offers a new method for improving earthquake prediction accuracy in the North-South seismic belt of China.