Real-time probabilistic tsunami forecasting in Cascadia from sparse offshore pressure observations
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866918525625434112 |
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| author | Henneking, Stefan Kutschera, Fabian Venkat, Sreeram Gabriel, Alice-Agnes Ghattas, Omar |
| author_facet | Henneking, Stefan Kutschera, Fabian Venkat, Sreeram Gabriel, Alice-Agnes Ghattas, Omar |
| contents | Near-field tsunami early warning in the Cascadia Subduction Zone is limited by sparse offshore observations. We investigate whether a hypothetical network of 175 ocean-bottom pressure sensors can support real-time Bayesian inference of the full spatiotemporal seafloor velocity field and probabilistic tsunami forecasting for a margin-wide and a partial fully-coupled Cascadia earthquake dynamic rupture-tsunami scenario. The simulated oceanic acoustic, Rayleigh, and tsunami wavefields are similar during the first two minutes after nucleation but diverge thereafter, enabling rapid earthquake scenario discrimination. Using an acoustic-gravity inversion with assimilation of pressure data, tsunami wave height forecasts are obtained in less than a second. We leverage a Bayesian inversion-based framework that splits the computations into an offline precomputation phase performed with large-scale computing facilities, and an online phase that computes forecasts and can be executed on a laptop. Forecast errors remain low at 22.1% for the margin-wide and 19.6% for the partial rupture. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_14966 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Real-time probabilistic tsunami forecasting in Cascadia from sparse offshore pressure observations Henneking, Stefan Kutschera, Fabian Venkat, Sreeram Gabriel, Alice-Agnes Ghattas, Omar Geophysics Numerical Analysis Computational Physics Near-field tsunami early warning in the Cascadia Subduction Zone is limited by sparse offshore observations. We investigate whether a hypothetical network of 175 ocean-bottom pressure sensors can support real-time Bayesian inference of the full spatiotemporal seafloor velocity field and probabilistic tsunami forecasting for a margin-wide and a partial fully-coupled Cascadia earthquake dynamic rupture-tsunami scenario. The simulated oceanic acoustic, Rayleigh, and tsunami wavefields are similar during the first two minutes after nucleation but diverge thereafter, enabling rapid earthquake scenario discrimination. Using an acoustic-gravity inversion with assimilation of pressure data, tsunami wave height forecasts are obtained in less than a second. We leverage a Bayesian inversion-based framework that splits the computations into an offline precomputation phase performed with large-scale computing facilities, and an online phase that computes forecasts and can be executed on a laptop. Forecast errors remain low at 22.1% for the margin-wide and 19.6% for the partial rupture. |
| title | Real-time probabilistic tsunami forecasting in Cascadia from sparse offshore pressure observations |
| topic | Geophysics Numerical Analysis Computational Physics |
| url | https://arxiv.org/abs/2603.14966 |