Earthquake catalogs for: Improving detection of micro-earthquakes in the Val d'Agri region (Southern Italy) using Deep Learning algorithms

This repository contains two earthquake catalogs (dubbed PRN and QS) obtained from the application of two deep-learning-based detection workflows to continuous seismic data recorded in the Val d’Agri region (Southern Italy). These catalogs have been generated using the PhaseNet neural network for seismic phase picking (Zhu & Beroza, 2019). The workflows used to generate the catalogs are described in detail in: Caredda et al. (2025). These datasets offer a more comprehensive representation of local seismicity compared to manually generated, STA/LTA-based catalogs (available in the open periodic monitoring reports accessible at: https://cms.ingv.it/sperimentazioni/val-d-agri [last accessed on 18/09/2025]). The datasets include event origin times, locations, magnitudes, location uncertainties, and phase arrival times with corresponding PhaseNet “pick probabilities” (for the PRN catalog), providing an enriched representation of local seismicity compared to conventional STA/LTA-based catalogs. These catalogs can serve as valuable resources for further research on seismicity, induced processes, Earth structure, and seismic hazard assessment in the Val d’Agri region. References: Caredda, E., M.P. Isken, S. Cesca, M. Errico, G. Zerbinato, and A. Morelli (2025) Improving detection of micro-earthquakes in the Val d’Agri region (Southern Italy) using deep learning algorithms, Seismica (in press).  Zhu, W., and Beroza, G. C. (2018). PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method. Geophysical Journal International, 216(1), 261–273. https://doi.org/10.1093/gji/ggy423