Abstract's details

Exploring statistical insights coupled with deep neural networks for the inversion of the MTF

Manuel Lopez Radcenco (OceanDataLab, France)


Gilles Guitton (OceanDataLab, France); Fabrice Collard (OceanDataLab, France)

Event: 2022 CFOSAT Science Team Meeting

Session: Side workshop 2

Presentation type: Type Oral

Contribution: PDF file


The SWIM instrument is designed to provide directional ocean wave spectral measurements by means of an inversion of the modulation transfer function (MTF), i.e., the relationship between the measured backscatter modulation spectra and the surface ocean waves directional spectra. Current inversion algorithms, however, rely on either geometrical and/or geophysical models, which present limitations that may hinder the accuracy and precision of the inversion process. Recently, data-driven machine learning approaches have appeared as an appealing alternative to model-based approaches, allowing for the leverage of the ever-increasing availability of remote-sensing, in situ and model data to overcome the limitations of current models. Thus, we propose here to explore deep learning as a means to improve on current strategies for the inversion of the MTF. In particular, physically-informed machine learning approaches have shown great promise to exploit existing knowledge and produce better constrained models with enhanced relevance and interpretability. We therefore use classical statistical analysis to better understand the dependencies of the MTF on relevant geophysical parameters and complement the proposed data-driven models. Specifically, the MTF between SWIM cross-section modulation spectra in wavenumber space and WaveWatchIII wave model spectra in frequency space will be discussed. To this end, we exploit both model data, in the form of WaveWatchIII-derived synthetic ocean waves directional spectra, as well as real SWIM observations, alongside with theoretical MTF formulations, to evaluate the potential of deep neural networks to produce a more accurate, data-driven inversion of the MTF.

Oral presentation show times:

Room Start Date End Date
Main room Tue, Sep 13 2022,14:45 Tue, Sep 13 2022,15:00
Manuel Lopez Radcenco