Statistics-free interpolation of ocean observations with deep spatio-temporal prior
Résumé
Interpolating sea surface height satellite measurements is a challenging inverse problem as altimeter observation can be very sparse in space and time. Operational methods rely on second-order statistics of ocean evolution which are difficult to estimate due to the high dimensionality of the studied system. In this work, we investigate a statistics-free and unsupervised variational method using a deep spatio-temporal prior, a neural network optimized on only one observational window. Results are aligned with state-of-the-art operational methods.
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