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 |
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Wiley
2025-03-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2024GL113971 |
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| author | Kun‐Chi Ho Justin Yen‐Ting Ko Hsin‐Hua Huang Shiann‐Jong Lee |
| author_facet | Kun‐Chi Ho Justin Yen‐Ting Ko Hsin‐Hua Huang Shiann‐Jong Lee |
| author_sort | Kun‐Chi Ho |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d6eebb2f1cc14347b600be959c3871d0 |
| institution | Kabale University |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-d6eebb2f1cc14347b600be959c3871d02025-08-20T03:52:42ZengWileyGeophysical Research Letters0094-82761944-80072025-03-01525n/an/a10.1029/2024GL113971A Novel Approach to Tsunami Prediction Using Ambient Noise‐Derived Green's FunctionsKun‐Chi Ho0Justin Yen‐Ting Ko1Hsin‐Hua Huang2Shiann‐Jong Lee3Institute of Oceanography National Taiwan University Taipei TaiwanInstitute of Oceanography National Taiwan University Taipei TaiwanInstitute of Earth Sciences Academia Sinica Taipei TaiwanInstitute of Earth Sciences Academia Sinica Taipei TaiwanAbstract 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.https://doi.org/10.1029/2024GL113971tsunami predictionambient noise interferometryinfragravity wavescross‐correlation functionsdisaster preparedness |
| spellingShingle | Kun‐Chi Ho Justin Yen‐Ting Ko Hsin‐Hua Huang Shiann‐Jong Lee A Novel Approach to Tsunami Prediction Using Ambient Noise‐Derived Green's Functions Geophysical Research Letters tsunami prediction ambient noise interferometry infragravity waves cross‐correlation functions disaster preparedness |
| title | A Novel Approach to Tsunami Prediction Using Ambient Noise‐Derived Green's Functions |
| title_full | A Novel Approach to Tsunami Prediction Using Ambient Noise‐Derived Green's Functions |
| title_fullStr | A Novel Approach to Tsunami Prediction Using Ambient Noise‐Derived Green's Functions |
| title_full_unstemmed | A Novel Approach to Tsunami Prediction Using Ambient Noise‐Derived Green's Functions |
| title_short | A Novel Approach to Tsunami Prediction Using Ambient Noise‐Derived Green's Functions |
| title_sort | novel approach to tsunami prediction using ambient noise derived green s functions |
| topic | tsunami prediction ambient noise interferometry infragravity waves cross‐correlation functions disaster preparedness |
| url | https://doi.org/10.1029/2024GL113971 |
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