Montagnon T. auteur aut IRD Giffard-Roisin Sophie auteur aut IRD Dalla Mura M. auteur aut IRD Marchandon M. auteur aut IRD Pathier E. auteur aut IRD Hollingsworth J. auteur aut IRD text journalArticle eng text/pdf reformatted digital access Optical image correlation is a powerful method for remotely constraining ground movement from optical satellite imagery related to natural disasters (e.g., earthquakes, volcanoes, landslides). This approach enables the characterization, and identification of the causal factors and mechanisms underlying such processes. By employing sub?pixel correlation algorithms, one can obtain highly accurate (m?to?cm level) displacement fields at high spatial resolution (dm?to?cm) by comparing satellite images acquired before and after a period of movement. However, this method generally assumes a homogeneous translation of all pixels within a given correlation window, which will lead to biased estimates of ground displacement if the real case is not well represented by such a simplification, especially when resolving ground displacements next to sharp gradients in displacement, such as those found in the near?field of earthquake surface ruptures. In this paper, we present an innovative deep learning method estimating sub?pixel displacement maps from optical satellite images for the retrieval of ground displacement. From the generation of a realistic simulated database, comprising Landsat?8 satellite image pairs containing simulated sub?pixel shifts and sharp discontinuities, we develop a Convolutional Neural Network able to retrieve sub?pixel displacements. The comparison to state?of?the?art correlation methods shows that our pipeline significantly reduces by 32% the estimation bias around fault ruptures, leading to more accurate characterization of the near?field strain in surface rupturing earthquakes. Application of our model to the 2019 Ridgecrest earthquake demonstrates the ability of our model to accurately and quickly resolve ground displacement using real satellite images. Code is made available at https://gricad-gitlab.univ-grenoblealpes.fr/montagtr/cnn4l-discontinuities specialized 122APPLIC 066GPHGEN 126TELAPP01 1 4 e2024JH000174 [25 ] 2024 2993-5210 https://www.documentation.ird.fr/hor/fdi:010093787 10.1029/2024jh000174 2993-5210 [F B010093787] https://www.documentation.ird.fr/hor/fdi:010093787 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2025-05/010093787.pdf IRD - Base Horizon / Pleins textes 2025-05-07 2025-11-06 fdi:010093787 fre