Attention-based learning for early earthquake magnitude estimation from strong-motion records

Rapid and reliable earthquake magnitude estimation in the first seconds after rupture initiation is critical for effective earthquake early warning and emergency response. However, the limited information contained in the earliest seismic signals makes this task challenging. Here we develop a deep-learning model, A2MAG, to estimate earthquake magnitude using only the first 3 s of strong-motion acceleration records from the dense K-NET network in Japan. The model is trained and tested on more than 130,000 waveforms from over 12,000 earthquakes recorded between 1996 and 2024. It achieves a mean absolute error of 0.33 magnitude units and performs consistently across different tectonic regions. The model systematically underestimates large earthquakes (M > 6), reflecting the longer rupture duration and limited early energy released by large events. Using the same input waveforms, the approach also provides highly accurate P-wave arrival times with a mean absolute error of 0.07 s. These results demonstrate both the potential and the fundamental limitations of estimating earthquake magnitude from the earliest seismic signals and provide preliminary evidence for the feasibility of the proposed approach in earthquake early warning applications.