  Large earthquakes can be destructive and quickly wreak havoc on a landscape.
To mitigate immediate threats, early warning systems have been developed to
alert residents, emergency responders, and critical infrastructure operators
seconds to a minute before seismic waves arrive. These warnings provide time to
take precautions and prevent damage. The success of these systems relies on
fast, accurate predictions of ground motion intensities, which is challenging
due to the complex physics of earthquakes, wave propagation, and their
intricate spatial and temporal interactions. To improve early warning, we
propose a novel AI-enabled framework, WaveCastNet, for forecasting ground
motions from large earthquakes. WaveCastNet integrates a novel convolutional
Long Expressive Memory (ConvLEM) model into a sequence to sequence (seq2seq)
forecasting framework to model long-term dependencies and multi-scale patterns
in both space and time. WaveCastNet, which shares weights across spatial and
temporal dimensions, requires fewer parameters compared to more
resource-intensive models like transformers and thus, in turn, reduces
inference times. Importantly, WaveCastNet also generalizes better than
transformer-based models to different seismic scenarios, including to more rare
and critical situations with higher magnitude earthquakes. Our results using
simulated data from the San Francisco Bay Area demonstrate the capability to
rapidly predict the intensity and timing of destructive ground motions.
Importantly, our proposed approach does not require estimating earthquake
magnitudes and epicenters, which are prone to errors using conventional
approaches; nor does it require empirical ground motion models, which fail to
capture strongly heterogeneous wave propagation effects.
