Deep Learning spatio-temporal analysis of anthropogenic ground deformation recorded by GNSS time series in the North Adriatic coasts of Italy

Automatic detection and characterization of spatial and temporal features of surface deformation signals associated with anthropogenic activities is a challenging task, with important implications for the evaluation of multi-hazard related to human activities (e.g. earthquakes, subsidence, sea-level rise and flooding), particularly in coastal areas. In this work, we use synthetic Global Navigation Satellite System (GNSS) displacement time-series and apply Deep Learning algorithms (i.e. Convolutional Neural Network (CNN) and Autoencoder) in extracting the time and space features of ground deformation due to natural and anthropogenic processes. We focus on improving three fundamental aspects such as the spatial coverage, the temporal coverage and the accuracy of measurement that come from GNSS technique. The study area is Northern Italy, and particularly the North Adriatic coasts, where gas and oil production sites as well as gas storage sites are present. If in production sites hydrocarbon is constantly extracted during the year, in storage sites the gas/oil is usually injected from April to October and extracted between November and March. Our goals are to understand the effect of hydrocarbon production and extraction/injection process on surface deformation as precisely measured by the dense network of continuous GNSS stations operating in the study area and the ability of CNN-Autoencoder to characterize ground displacements caused by anthropogenic processes. Aims of this work are to identify temporal and spatial patterns in ground deformation time series caused by oil and gas extraction/or gas storage (i.e. extraction and injection); and estimate reservoir parameters (i.e. volumes, depths and extensions). We realize the training dataset by setting up 202 GNSS stations, randomly locating gas/oil reservoirs, which are described by a simple Mogi model, characterized by different depths and temporal evolution of volume changes. The Mogi model, as an approximate spherical shape of a reservoir, displays the ratio of horizontal displacement to vertical displacement due to volume change (i.e. inflating or deflating) and pressure varying with time. The temporal evolution of the volumes of the Mogi sources is simulated by using different parameters associated with several functions namely seasonal, exponential, multi-linear and bell shape. Weighted Principal Component Analysis (WPCA) is used to deal with missing data, which is a common feature in GNSS time series, under an assumption that the weights of missing data are zero. Furthermore, since the CNN-Autoencoder works by analyzing images, the synthetic GNSS time series are interpolated by leveraging the Kriging Interpolation method, which is a Gaussian Process Regression, to obtain the ground displacement in 2D physical space. After calibrating the CNN-Autoencoder model with the synthetic GNSS time series, the model is applied to real data. The code is written in Python and runs on a High-performance computing (HPC) cluster with Graphic Process Unit (GPU) at National Institute of Geophysics and Volcanology (INGV) in Bologna, Italy.