Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data

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.

Subjects

Deep operator learning

Fourier neural operator

Nonlinear ocean waves

Phase-resolved surface reconstruction

Radar inversion

X-band radar images

MLE@TUHH

DDC Class

624: Civil Engineering, Environmental Engineering

Funding(s)

Excitability of Ocean Rogue Waves – Numerical Prediction and Early Warning by combining Wave Physics, Numerical Simulation and Data Driven Methods

Projekt DEAL

Publication version

publishedVersion

Lizenz

https://creativecommons.org/licenses/by/4.0/

Name

1-s2.0-S0029801823024435-main.pdf

Type

Main Article

Size

2.41 MB

Format

Adobe PDF

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Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data