  Predicting high-fidelity ground motions for future earthquakes is crucial for
seismic hazard assessment and infrastructure resilience. Conventional empirical
simulations suffer from sparse sensor distribution and geographically localized
earthquake locations, while physics-based methods are computationally intensive
and require accurate representations of Earth structures and earthquake
sources. We propose a novel artificial intelligence (AI) simulator, Conditional
Generative Modeling for Ground Motion (CGM-GM), to synthesize high-frequency
and spatially continuous earthquake ground motion waveforms. CGM-GM leverages
earthquake magnitudes and geographic coordinates of earthquakes and sensors as
inputs, learning complex wave physics and Earth heterogeneities, without
explicit physics constraints. This is achieved through a probabilistic
autoencoder that captures latent distributions in the time-frequency domain and
variational sequential models for prior and posterior distributions. We
evaluate the performance of CGM-GM using small-magnitude earthquake records
from the San Francisco Bay Area, a region with high seismic risks. CGM-GM
demonstrates a strong potential for outperforming a state-of-the-art
non-ergodic empirical ground motion model and shows great promise in seismology
and beyond.
