Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909263637512192 |
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| author | Ren, Pu Nakata, Rie Lacour, Maxime Naiman, Ilan Nakata, Nori Song, Jialin Bi, Zhengfa Malik, Osman Asif Morozov, Dmitriy Azencot, Omri Erichson, N. Benjamin Mahoney, Michael W. |
| author_facet | Ren, Pu Nakata, Rie Lacour, Maxime Naiman, Ilan Nakata, Nori Song, Jialin Bi, Zhengfa Malik, Osman Asif Morozov, Dmitriy Azencot, Omri Erichson, N. Benjamin Mahoney, Michael W. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_15089 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling Ren, Pu Nakata, Rie Lacour, Maxime Naiman, Ilan Nakata, Nori Song, Jialin Bi, Zhengfa Malik, Osman Asif Morozov, Dmitriy Azencot, Omri Erichson, N. Benjamin Mahoney, Michael W. Geophysics Artificial Intelligence Machine Learning 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. |
| title | Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling |
| topic | Geophysics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2407.15089 |