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

• URL: http://arxiv.org/abs/2407.15089v1
• Date: Sun, 21 Jul 2024 08:23:37 GMT
• Title: Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling
• Authors: Pu Ren, Rie Nakata, Maxime Lacour, Ilan Naiman, Nori Nakata, Jialin Song, Zhengfa Bi, Osman Asif Malik, Dmitriy Morozov, Omri Azencot, N. Benjamin Erichson, Michael W. Mahoney,
• Abstract summary: Conditional Generative Modeling for Ground Motion (CGM-GM) We propose a novel artificial intelligence (AI) simulator to synthesize high-frequency and spatially continuous earthquake ground motion waveforms. CGM-GM demonstrates a strong potential for outperforming a state-of-the-art non-ergodic empirical ground motion model.
• Score: 43.056135090637646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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