GeoFlowNet-SAR: Earthquake Displacement Estimation From Synthetic Aperture Radar Images

Displacement estimation using remote sensing images is an effective approach for assessing surface displacement caused by natural disasters like earthquakes and landslides. By employing pixel correlation algorithms, high-precision displacement maps can be generated from images taken before and after surface movement. However, traditional methods often rely on spatial regularization or frequency masking to reduce high-frequency noise, which can smooth spatial details and result in biased displacement estimates, especially near sharp discontinuities typical of earthquake surface ruptures. Moreover, sub-pixel displacement estimation using Synthetic Aperture Radar (SAR) images remains a challenge compared to optical images, due to the strong impact of speckle noise. This paper presents GeoFlowNet-SAR, an innovative sub-pixel displacement estimation method leveraging SAR images. SAR offers advantages thanks to an all-weather observation and high penetration, making it suitable for conditions typically challenging for optical systems in the visible light spectrum. This study uses Sentinel-1 SAR Single Look Complex (SLC) images with dual-polarization (VV and VH modes) and Interferometric Wide (IW) swath mode to balance coverage and resolution. By training on simulated displacement datasets with realistic sharp discontinuities, GeoFlowNet-SAR directly predicts surface displacement fields, providing highly efficient, robust, and precise results, while overcoming some limitations of traditional methods. The effectiveness of the proposed methodological contribution is first quantitatively demonstrated using synthetic simulated earthquake datasets, including comparisons with state-of-the-art correlation methods. The method is further validated using two real remote sensing images from the 2019 Ridgecrest earthquake and from the 2023 Turkey-Syria earthquake. The observed results from these real datasets confirm the effectiveness of GeoFlowNet-SAR in practical applications. The codes are available at: https://gricad-gitlab.univ-grenoble-alpes.fr/giffards/geoflownet-sar.