Abstract
<jats:p>The determination of earthquake focal mechanisms is crucial for understanding tectonic processes and assessing seismic hazard. While traditional methods based on manual polarity picking have proven effective, they are time-consuming and potentially subject to analyst’s bias, particularly for small to moderate earthquakes. This study presents a machine learning-based approach for automating focal mechanism determination through P-wave polarity prediction. We develop and validate a methodology that combines a Convolutional Neural Network (CNN), based on the work by Ross et al. (2018) with the SKHASH algorithm (Skoumal et al. 2024), focusing on Italian earthquakes with magnitudes between 0 and 4.0, spanning the country's major active tectonic settings. The CNN, trained on a subset of the INSTANCE catalog by Michelini et al. (2021) containing over 450,000 Italian seismic waveforms, achieves 96% accuracy in polarity classification. To ensure the reliability of prediction, we implement stringent probability thresholds and validate our approach using a test dataset of 240,335 waveforms from 15,420 events. We then apply the methodology to 300 earthquakes with existing Time Domain Moment Tensor (TDMT) solutions finding a median Kagan angle (Kagan, 1991) between our FM and the TDMT solutions of 28.7°. This automated approach proves to be effective and may, in the future, offer a reliable means of determining focal mechanisms for lower magnitude events, where moment tensor inversions might not be feasible. This would contribute to a better understanding of regional seismotectonics and enhance seismic monitoring capabilities.</jats:p>