Sentinel-1 InSAR Coseismic Deformation Dataset for the 2026 Venezuela M7.2 and M7.5 Earthquakes

This dataset contains Sentinel-1 interferometric synthetic aperture radar (InSAR) coseismic deformation products for the 2026 Venezuela M7.2 and M7.5 earthquake sequence. The data were collected from four Sentinel-1 tracks, including ascending tracks T106A and T33A and descending tracks T98D and T25D, providing multi-geometry observations of the coseismic surface deformation field. The InSAR observations reveal a predominantly right-lateral strike-slip deformation pattern associated with the earthquake sequence. The coseismic deformation is mainly distributed along the Boconó–San Sebastián fault system in northern Venezuela and extends for approximately 200 km from the San Felipe region toward Caracas, indicating rupture of a long segment of the Caribbean–South America plate-boundary fault zone. The dataset includes geocoded line-of-sight (LOS) displacement grids, filtered wrapped phase grids, LOS look-vector component grids, quadtree-sampled observations, and preview figures. The LOS displacement grids are provided in millimeters, and the filtered wrapped phase grids are provided in radians. The LOS look-vector grids contain the east, north, and up components of the Sentinel-1 viewing geometry and can be used to project three-dimensional displacement models into the radar LOS direction. The Sentinel-1 SAR data were processed using GMTSAR by the InSAR team at the University of Science and Technology of China (USTC). Interferograms were generated from Sentinel-1 SLC image pairs, filtered, and converted to geocoded LOS displacement fields. Quadtree sampling was further applied to the LOS displacement fields for finite-fault inversion and related geodetic analyses. This dataset is useful for studies of earthquake-induced deformation, finite-fault slip inversion, rupture process analysis, regional tectonics, and seismic hazard assessment. If you use these data in a publication, please cite the Zenodo DOI associated with this dataset. If the dataset plays a substantial role in your scientific analysis, interpretation, or publication, we encourage you to contact the data creators to discuss appropriate acknowledgement or potential co-authorship. Contact: Xin Wang, [e-mail]; Xiaohua Xu, [e-mail].