Optical satellite imagery is widely used for estimating ground movement in the aftermath of natural disasters such as earthquakes. This type of imagery enables detailed analysis of the factors and mechanisms that drive or influence these events. By using sub-pixel correlation algorithms, it provides precise displacement measurements (in the meter-to-centimeter range) and high spatial resolution (decimeter-to-centimeter level) by comparing images taken before and after the event. In this study, we present a deep neural network approach, trained on our new specific realistic dataset FaultDeform, to retrieve full-scale seismic ground motion displacement fields from optical satellite images with sub-pixel precision. The FaultDeform dataset, available at https://doi.org/10.57745/G02ZXZ, is the first satellite synthetic dataset tailored for ground motion estimation. We introduce the GeoFlowNet pipeline, utilizing a U-net architecture to solve the displacement estimation problem, delivering high-speed performance through GPU implementation, and outperforming current correlators in speed and precision. Comprehensive comparisons with state-of-the-art methods such as COSI-Corr, MicMac and CNN-DIS, and validation on real-world data from the 2019 Ridgecrest and 2013 Balochistan earthquakes showcases the robustness of our method. Codes are freely available: gricad-gitlab.univ-grenoble-alpes.fr/montagtr/GeoFlowNet.

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.

Following the 7.8 magnitude earthquake that struck Turkey and Syria on February 6, 2023, TomoSAR, an extensive software designed for SAR image processing, demonstrated its effectiveness in assessing land subsidence. It provided the initial three-dimensional displacement data, marking a significant milestone in this field. Notably, TomoSAR stands out as the first publicly accessible tool capable of jointly processing Persistent and Distributed Scatterers (https://github.com/DinhHoTongMinh/TomoSAR). Continual efforts are underway to elevate TomoSAR’s accessibility and performance. This involves integrating algorithms into a parallel version to facilitate enhanced performance and open avenues for complimentary scientific services at no cost.

Estimating the ground displacement from non-rigid registration of two optical satellite images, separated from hours to months, is key in the study of natural disasters such as earthquakes. Compared to standard image registration and flow estimation tasks, a key challenge here lies in resolving very small displacements (typically cm- or m-scale) with sub-pixel accuracy and precision using coarser image resolutions (e.g. 15 m for Landsat-8). Traditional block matching/sliding window methods, employing local windowed correlation techniques, are unable to reduce the effects of long-wavelength noise arising from differences in image lightning, vegetation, or acquisition artifacts. By using both local and global scales, fully convolutional deep learning registration models (U-nets) are potentially able to better resolve ground displacements, less affected my multi-scale noise. Yet, no labelled database exists for ground deformation. Here, we develop a new synthetic database of 50,000 realistic satellite image pairs containing simulated earthquake displacements, along with their ground truth displacement maps, which are used to train state-of-the-art fully convolutional deep learning models (U-net). The inference shows good preliminary results, with a fast computation time (less than 1 second for a 256 × 256 image).

Recently, an advanced Persistent Scatterers and Distributed Scatterers (PSDS) radar interferometry technique has been implemented as an open-source TomoSAR package (https://github.com/DinhHoTongMinh/TomoSAR). TomoSAR offers state-of-the-art algorithms to capture your movement best. However, it is easy to make you crazy with memory requirements. Due to so many images to calculate, it says for only the covariance matrix with 200 images of 500x2000 size, 45 GB should be allocated for that. For a small computer, it can be a task impossible. For our cluster, the RAM is capacity up to TB. The good news is we can process free of charge for you under a scientific collaboration.

Image correlation is a powerful method for remotely constraining ground displacements associated with natural disasters. By employing sub-pixel correlation algorithms, one can obtain a displacement field by correlating satellite images acquired before and after a displacement event. However, this computation may be biased when dealing with sharp discontinuities, typical of earthquake surface ruptures, which are of current interest in the context of quantifying the partitioning of slip between the primary fault core and neighboring damage zone. In this paper, we present an innovative deep learning method to perform sub-pixel correlation of optical satellite images for the retrieval of ground displacement, designed to mitigate bias around fault ruptures. From the generation of a realistic simulated database of images before and after synthetic ground displacement built specifically to deal with fault discontinuities in satellite images (e.g. Landsat-8 in this case), we developed a Convolutional Neural Network (CNN) able to retrieve sub-pixel displacements. Comparison with a state-of-the-art phase correlation method shows our pipeline is able to mitigate the sub-pixel bias in the near-field of earthquake ruptures.

Precise estimation of ground displacement at regional scales from optical satellite imagery is fundamental for the study of natural disasters, such as earthquakes, volcanoes, landslides, etc. Current methods make use of correlation techniques between two acquisitions in order to retrieve a fractional pixel shift. However, differences in local lighting conditions between two acquisitions can lead to differences in image reflectance, which in turn can bias the displacement estimate, especially in the sub-pixel domain. Data-driven methods may provide a way to overcome these errors. From the generation of a realistic simulated database based on Landsat-8 satellite image pairs with added simulated sub-pixel shifts, we developed a Convolutional Neural Network (CNN) able to retrieve sub-pixel displacements.

In this paper, seismic vulnerability assessment is addressed under the umbrella of remote sensing. A study for estimating and evaluating information for assessing seismic vulnerability based on a building basis is presented. The proposed methodology utilizes the capabilities of remote sensing and combines in-situ data tested in the area of Grenoble (France). A map is estimated in agreement with in-situ data, as support information system for seismic risk in the context of building vulnerability assessment. In the methodology proposed, building attributes such as roof identification, building height and characteristic scale are extracted from very high resolution panchromatic data, and an accurate digital elevation model. Support vector machine regression is used to estimate building vulnerability and in-situ data are available for evaluation.

In this paper, 2 data fusion strategies from SAR images are investigated through application to measurement of displacement field due to the Kashmir earthquake (Mw=7.6, 2005). Firstly, the 3D displacement field at the Earth's surface is retrieved by a linear inversion, using the measurements from sub-pixel image correlation and differential interferometry. In addition to the generalized least square method, a fuzzy approach is applied to represent the measurement uncertainty. Secondly, the geometry of the fault is optimized by a non linear inversion, using the same measurements. The inter-comparisons between strategies and approaches are performed in order to highlight the advantages and disadvantages of each strategy and approach.

Presents four case examples of contribution on InSAR to active tectonics issues in Taiwan Island that is one of the most seismically active regions in the world. For these studies, differential InSAR technique is used in a 2-pass approach, the resulting interferograms combine images of the ERS-1/2 satellites from 1993 to 2001. The results illustrate the different tectonic processes that InSAR can investigate in Taiwan: (1) regarding the 1999 Chi-Chi earthquake event, InSAR allows to capture coseismic displacements, and to detect displacements at nearby faults triggered by the earthquake, (2) in the Tainan area (SW Taiwan), InSAR can measure the interseismic crustal deformation field (uplift of an anticline) over eight years, (3) for the Fengshan fault and Longitudinal Valley fault, InSAR is able to monitor fault creep. This study makes it possible to consider InSAR as a tool (in complement to the GPS network) for monitoring several active faults in Taiwan that have the potential to produce earthquakes.