Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910292393328640 |
|---|---|
| author | Ehlers, Svenja Klein, Marco Heinlein, Alexander Wedler, Mathies Desmars, Nicolas Hoffmann, Norbert Stender, Merten |
| author_facet | Ehlers, Svenja Klein, Marco Heinlein, Alexander Wedler, Mathies Desmars, Nicolas Hoffmann, Norbert Stender, Merten |
| contents | Accurate short-term predictions of phase-resolved water wave conditions are crucial for decision-making in ocean engineering. However, the initialization of remote-sensing-based wave prediction models first requires a reconstruction of wave surfaces from sparse measurements like radar. Existing reconstruction methods either rely on computationally intensive optimization procedures or simplistic modelling assumptions that compromise the real-time capability or accuracy of the subsequent prediction process. We therefore address these issues by proposing a novel approach for phase-resolved wave surface reconstruction using neural networks based on the U-Net and Fourier neural operator (FNO) architectures. Our approach utilizes synthetic yet highly realistic training data on uniform one-dimensional grids, that is generated by the high-order spectral method for wave simulation and a geometric radar modelling approach. The investigation reveals that both models deliver accurate wave reconstruction results and show good generalization for different sea states when trained with spatio-temporal radar data containing multiple historic radar snapshots in each input. Notably, the FNO demonstrates superior performance in handling the data structure imposed by wave physics due to its global approach to learn the mapping between input and output in Fourier space. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_11913 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data Ehlers, Svenja Klein, Marco Heinlein, Alexander Wedler, Mathies Desmars, Nicolas Hoffmann, Norbert Stender, Merten Atmospheric and Oceanic Physics Artificial Intelligence Machine Learning 68T99 Accurate short-term predictions of phase-resolved water wave conditions are crucial for decision-making in ocean engineering. However, the initialization of remote-sensing-based wave prediction models first requires a reconstruction of wave surfaces from sparse measurements like radar. Existing reconstruction methods either rely on computationally intensive optimization procedures or simplistic modelling assumptions that compromise the real-time capability or accuracy of the subsequent prediction process. We therefore address these issues by proposing a novel approach for phase-resolved wave surface reconstruction using neural networks based on the U-Net and Fourier neural operator (FNO) architectures. Our approach utilizes synthetic yet highly realistic training data on uniform one-dimensional grids, that is generated by the high-order spectral method for wave simulation and a geometric radar modelling approach. The investigation reveals that both models deliver accurate wave reconstruction results and show good generalization for different sea states when trained with spatio-temporal radar data containing multiple historic radar snapshots in each input. Notably, the FNO demonstrates superior performance in handling the data structure imposed by wave physics due to its global approach to learn the mapping between input and output in Fourier space. |
| title | Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data |
| topic | Atmospheric and Oceanic Physics Artificial Intelligence Machine Learning 68T99 |
| url | https://arxiv.org/abs/2305.11913 |