A Novel Approach to Tsunami Prediction Using Ambient Noise‐Derived Green's Functions
Abstract Conventional tsunami simulations rely on accurate bathymetric data, posing challenges in regions lacking such information. We introduce a novel approach using ambient noise interferometry to derive empirical Green's functions of infragravity waves from noise correlation functions (NCFs...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-03-01
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| Series: | Geophysical Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024GL113971 |
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| Summary: | Abstract Conventional tsunami simulations rely on accurate bathymetric data, posing challenges in regions lacking such information. We introduce a novel approach using ambient noise interferometry to derive empirical Green's functions of infragravity waves from noise correlation functions (NCFs) extracted from a 10‐year Deep‐ocean Assessment and Reporting of Tsunamis data set in the Pacific Ocean. Our analysis reveals pronounced propagating behavior in NCFs, indicative of wave dispersion relationships. Long‐period NCFs align with shallow‐water wave dynamics, making them suitable for tsunami simulations. By eliminating the need for precise bathymetry, our method offers a practical solution for data‐sparse regions. A case study of an Alaska tsunami demonstrates our NCFs effectively fit observed pressure data, outperforming conventional Cornell Multi‐Grid Coupled Tsunami Model simulations. The fidelity of our results underscores the potential of ambient noise interferometry‐derived NCFs to enhance tsunami predictions, even in complex environments. Our findings advance tsunami research and have significant implications for disaster preparedness and mitigation. |
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| ISSN: | 0094-8276 1944-8007 |