Deep Learning for detecting anthropogenic ground deformation signals from GNSS time series along the North Adriatic coasts of Italy

Detecting and analyzing spatiotemporal features of surface deformation signals caused by anthropogenic activities remains a challenging task in areas facing multi-hazard risks (e.g., earthquakes, subsidence, sea level rise, and flooding), particularly in coastal regions. We use Global Navigation Satellite System (GNSS) displacement time-series and apply deep learning procedures to identify and characterize ground deformation patterns resulting from natural subsidence and human activities. The focus area is Northern Italy, specifically the North Adriatic coasts, a region with many gas and oil production and storage operations. Unlike production sites, where hydrocarbons are extracted continuously throughout the year, storage sites follow a seasonal cycle: gas/oil is injected in summer and extracted in winter. Our goals are to (1) identify spatial and temporal ground deformation patterns in GNSS time series linked to these anthropogenic activities, and (2) estimate key reservoir properties such as volume, depth, and spatial extent. We generate synthetic training datasets using 114 GNSS stations and simulated reservoirs modeled with the Mogi model, varying depth and volume-change characteristics over time. Weighted Principal Component Analysis (WPCA) is employed to handle missing GNSS data by assigning zero weights to gaps. We will discuss results relative to the application of Convolutional Neural Networks, AutoEncoders, and Graph Neural Networks. After training and calibrating these models on synthetic GNSS datasets, we apply them to real-world GNSS observations. A comparison will be carried out, discussing pros and cons of the various techniques.