Statistics-free interpolation of ocean observations with deep spatio-temporal prior - Sorbonne Université Access content directly
Conference Papers Year : 2022

Statistics-free interpolation of ocean observations with deep spatio-temporal prior

Abstract

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.
Fichier principal
Vignette du fichier
maclean22_ecml__Springer_Lecture_Notes_in_Computer_Science_template___Copy_.pdf (1.07 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03765735 , version 1 (31-08-2022)

Identifiers

  • HAL Id : hal-03765735 , version 1

Cite

Arthur Filoche, Théo Archambault, Anastase Alexandre Charantonis, Dominique Béréziat. Statistics-free interpolation of ocean observations with deep spatio-temporal prior. ECML/PKDD Workshop on Machine Learning for Earth Observation and Prediction (MACLEAN), Sep 2022, Grenoble, France. ⟨hal-03765735⟩
105 View
63 Download

Share

Gmail Facebook X LinkedIn More