تقرير
Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling
| العنوان: | Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling |
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| المؤلفون: | 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. |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Physics - Geophysics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.15089 |
| رقم الأكسشن: | edsarx.2407.15089 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |