Deep-seated Gravitational Slope Deformations identification and trend analysis from ground deformation time series: A case study in the Italian Alps

Abstract

In alpine regions, Deep-seated Gravitational Slope Deformations (DsGSDs) pose significant risks due to their continuous deformation rates, potentially leading to sudden and accelerated transformations that can cause unpredictable damage to local communities and infrastructure. Monitoring DsGSDs is crucial for effective risk assessment and land-use planning. Advances in remote sensing technologies, particularly InSAR (Interferometric Synthetic Aperture Radar), offer substantial advantages in monitoring and studying these widespread and slow processes. The European Ground Motion Service (EGMS), which provides Europe-wide ground motion data, emerges as a viable tool for detecting, monitoring, and characterizing DsGSDs. This study aimed to develop and evaluate an automated workflow for identifying and analyzing trends in DsGSDs in alpine areas using deformation time series datasets. The approach involves utilizing advanced statistical methods to characterize DsGSD phenomena in alpine regions. Focusing on the Carnic Alps area in the northern part of the Veneto Region and Friuli-Venezia Giulia Region (NE, Italy), our objective is to explore supervised machine learning (ML) and deep learning (DL) algorithms to automatically identify DsGSD areas and analyze the spatiotemporal behavior of long time series of ground deformations. The findings will be compared with data from the Italian Landslide Inventory (IFFI), serving to not only validate the newly extracted information but also assess the potential of integrating multi-source datasets. This work sets the foundation for further analyses on how transient climatic factors could influence DsGSDs regimes.