Seismic source characterization in the central Mediterranean Region

Abstract

The process of earthquake source characterization involves determining the key properties of an earthquake and its origin, including its location, as indicated by latitude and longitude coordinates, its depth and its magnitude. This dissertation focuses on establishing a statistical model, by combining different Neural Network (NN) architectures including the Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN) and Graph Neural Network (GNN), in order to characterize the source of the earthquakes occurring in the central Mediterranean region, particularly concentrating on the Maltese islands, Sicily and the surrounding areas. The models considered are trained and validated on earthquake data recorded between 2013 and 2024. These data were obtained from seismic stations positioned around the Maltese Islands and Sicily, which are installed and maintained by the Istituto Nazionale di Geofisica e Vulcanologia (INGV) and the University of Malta’s Seismic Monitoring and Research Group (SMRG). The objective is to predict earthquake source parameters using station coordinates and waveform features. Each earthquake is represented as a graph in which the vertices correspond to stations, and the associated features are assigned to these nodes. For each event, the model outputs latitude, longitude, depth, and magnitude. Two model architectures are considered, namely, an edgeless graph model and a dynamic edges GNN. To identify the optimal model of each architecture, a systematic series of experiments, including hyperparameter tuning, regularization techniques, restricting the analysis to a more localized region and ensemble modelling, are conducted. The best two models are then fit on test data comprising events from January 2025 to August 2025. The edgeless graph architecture emerged as the best-performing architecture.