Enhancing seismicity detection and characterization in the Val d’Agri region: insights into tectonic and induced processes using Deep Learning techniques

Monitoring microseismicity is fundamental to advancing our understanding of fault mechanics under natural and anthropogenic influences. Recent advancements in seismological methodologies, particularly those employing deep learning techniques, have significantly improved the detection of weak earthquakes while preserving high levels of precision and reliability.

This study aims to enhance the detection and characterization of seismicity in the Val d’Agri region (Southern Italy) by implementing advanced deep learning-based methodologies, focusing on understanding the tectonic and anthropogenic influences driving seismic activity. The Val d’Agri region is a tectonically active area of considerable scientific and industrial relevance, hosting Europe’s largest onshore oil reservoir and an artificial lake. By employing state-of-the-art deep learning and full waveform earthquake detection methods we identified and located seismic events over a three-year period, achieving a twofold increase in detected events compared to the manually revised bulletin, with a recall rate of ~95%.

Spatial and temporal analyses, based on a density-based clustering approach, revealed distinct seismic clusters. The seismicity is mostly concentrated along the Monti della Maddalena fault system in the southwestern region, characterized by shallow earthquakes (5–7 km depth), while the northeastern and northwestern areas exhibit sparser and deeper activity (15–20 km depth). High-resolution event localization illuminated fault geometries and spatial distributions with high detail. Additionally, our dataset highlights a temporal correlation between seismicity rates and the filling and emptying phases of the Pertusillo artificial reservoir.

Our findings underscore the utility of automated workflows in improving seismic monitoring and fault characterization, providing critical insights into tectonic processes and reservoir-induced seismicity.