Sparse fault representation based on moment tensor interpolation
Accurate representation of large earthquake sources is required for understanding rupture dynamics and improving seismic hazard assessments. While capable of capturing complex spatio-temporal slip scenarios, traditional finite-fault models often suffer from over-parameterization, require strong reg...
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| Format: | Article |
| Language: | English |
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McGill University
2024-12-01
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| Series: | Seismica |
| Subjects: | |
| Online Access: | https://seismica.library.mcgill.ca/article/view/1433 |
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| Summary: | Accurate representation of large earthquake sources is required for understanding rupture dynamics and improving seismic hazard assessments. While capable of capturing complex spatio-temporal slip scenarios, traditional finite-fault models often suffer from over-parameterization, require strong regularization, and pose significant computational challenges, especially in rapid-response scenarios. Conversely, multiple point source (MPS) models reduce the rupture as a sequence of point sources but are inadequate to simulate short-period wavefield and static displacement. We introduce a hybrid source representation that leverages moment tensor interpolation to bridge the gap between these models. By treating moment tensors as "key" centroids of a tensor field, we construct geometrically coherent slip models that retain the spatial complexity of finite-fault models while maintaining MPS's computational efficiency and simplicity. Our method extends existing 2D tensor-field reconstruction techniques to moment tensors, allowing source-type-preserving interpolation and enabling sparse model approximation and source upscaling for numerical simulations. We demonstrate how our approach can benefit both the inverse and forward problems on the January 2024 Noto earthquake, computing a sparse approximation of the USGS NEIC source model with fewer than ten key tensors and computing full wavefield and static deformation from upscaled source distributions in a realistic 3D regional tomographic model using spectral-elements method.
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| ISSN: | 2816-9387 |