Source Characterization via Deep Learning: Integrating Polarity Prediction and Focal Mechanism Estimation

In modern seismology, the rapid and accurate characterization of seismic sources following an earthquake is a fundamental task. Most observatories currently compute moment tensor solutions for events exceeding specific magnitude thresholds to ensure reliability. For instance, the National Institute of Geophysics and Volcanology (INGV) provides routine solutions for moderate to large events (Mw​≥3.5) using established methods (Scognamiglio et al., 2009). However, characterizing smaller events remains challenging due to low signal-to-noise ratios and the need of modeling high-frequency waveforms that requires detailed knowledge of the velocity model, which is rarely available.

Machine learning (ML) techniques have emerged as powerful tools to address these limitations, particularly in improving the prediction of first-arrival seismic wave polarities. These ML-derived polarities can be effectively integrated into traditional frameworks to compute robust focal mechanisms. In this study, we implement a workflow that bridges deep learning polarity predictions with standard focal mechanism estimation techniques, focusing on the tectonic setting of the Italian Peninsula.

Our methodology consists of two primary stages. First, we trained a Convolutional Neural Network (CNN) for polarity prediction using the INSTANCE catalog (Michelini et al., 2021), which provides the high-quality, manually reviewed data essential for supervised learning. Second, we validated the model’s performance by analyzing approximately 4,700 earthquakes that occurred in Italy between January 1 2021, and January 1 2025, with magnitudes below 4.5.

To benchmark our results, we selected a subset of earthquakes with existing Time Domain Moment Tensor (TDMT) solutions (Scognamiglio et al., 2006). Using the polarities predicted by the CNN, we computed focal mechanisms using the SKHASH code (Skoumal et al., 2024). The accuracy of these solutions was then evaluated against the TDMT catalog using Kagan angle analysis (Kagan, 1991) to quantify the rotation between double-couple sources.