Group A, Poster #165, Ground Motions (GM)

Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling

Pu Ren
Poster Image: 

Poster Presentation

2024 SCEC Annual Meeting, Poster #165, SCEC Contribution #14040 VIEW PDF
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.
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Citation: Ren, P. (2024, 09). Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling. Poster Presentation at 2024 SCEC Annual Meeting.
Keywords: Ground motion synthesis, Generative modeling, Earthquake simulation, San Francisco Bay Area
SCEC Award: 24123
Relevant SCEC Themes:
Improving Observations and Closing Data Gaps
Advanced Modeling Frameworks
Research Computing