The Role of Artificial Intelligence in Modeling and Predicting Ground Deformation Using Advanced InSAR Data

Ground deformation, encompassing sudden and gradual shifts in the Earth's surface, poses significant global geohazard risks. These phenomena demand thorough investigation and monitoring and are influenced by a range of natural and anthropogenic factors such as mining, excessive groundwater extraction, seismic activities, structural loads, and subsurface geology. Our research is centered on the location in the Venetian-Friulian Plain (Veneto Region, NE Italy). This area is of interest because it represents a transitional zone where sedimentary deposits from both river systems (fluvial) and lagoon/coastal environments are found, marking the transition from the alluvial plain to the coastal plain. Ground displacement maps are generated using pre-event data from the Veneto Region Sentinel 1-PS data Service and the European Ground Motion Service (EGMS), allowing us to analyze the heightened susceptibility of areas undergoing deformation. Our approach integrates artificial intelligence techniques with InSAR-derived data to create comprehensive pre- and post-event multi-temporal deformation inventories and susceptibility maps. This fusion offers exceptional accuracy and timeliness in identifying, modeling, and predicting ground deformation events. Utilizing insights from InSAR data and AI techniques, we aim to project future trends and potential risks, contributing valuable insights to geohazard assessment and management within the study region.