Rapid wavefield forecasting for earthquake early warning via deep sequence to sequence learning

Dongwei Lyu, Rie Nakata, Pu Ren, Michael W. Mahoney, Arben Pitarka, Nori Nakata and N. Benjamin Erichson ()
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Dongwei Lyu: International Computer Science Institute
Rie Nakata: International Computer Science Institute
Pu Ren: Lawrence Berkeley National Laboratory
Michael W. Mahoney: International Computer Science Institute
Arben Pitarka: Lawrence Livermore National Laboratory
Nori Nakata: International Computer Science Institute
N. Benjamin Erichson: International Computer Science Institute

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract We propose a deep learning model, WaveCastNet, to forecast high-dimensional wavefields. WaveCastNet integrates a convolutional long expressive memory architecture into a sequence-to-sequence forecasting framework, enabling it to model long-term dependencies and multiscale patterns in both space and time. By sharing weights across spatial and temporal dimensions, WaveCastNet requires significantly fewer parameters than more resource-intensive models such as transformers, resulting in faster inference times. Crucially, WaveCastNet also generalizes better than transformers to rare and critical seismic scenarios, such as high-magnitude earthquakes. Here, we show the ability of the model to predict the intensity and timing of destructive ground motions in real time, using simulated data from the San Francisco Bay Area. Furthermore, we demonstrate its zero-shot capabilities by evaluating WaveCastNet on real earthquake data. Our approach does not require estimating earthquake magnitudes and epicenters, steps that are prone to error in conventional methods, nor does it rely on empirical ground-motion models, which often fail to capture strongly heterogeneous wave propagation effects.

Date: 2025
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DOI: 10.1038/s41467-025-65435-2

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