A machine-learning workflow for event detection and relocation in the Gargano Promontory (Southern Italy)

In regions with limited tectonic information, dense deployments of local seismic networks significantly improve the detection of low-magnitude events. The consequent enrichment of seismic catalogs is proved to have a crucial role for understanding seismotectonic processes and assessing seismic hazards. This study evaluates the performance of machine learning algorithms (MLA) for P- and S-wave picking and event association, using data recorded between April 2013 and June 2025 by the OTRIONS seismic network (FDSN code OT), operating in the Gargano Promontory (GP, Southern Italy) since 2013.

The MLA workflow consists of PhaseNet, a deep learning-based phase picker, in combination with GaMMA, an association algorithm, were employed and approximately 27,000 seismic events were detected. NonLinLoc was employed for event locations. The visual inspection confirmed that about 51% of the events were local earthquakes, while the remainder events were classified as quarry blasts, false events, or events located outside the network. The visual revision procedure was essential at this step.

Compared to the previous manual catalog (based on the STA/LTA detection algorithm) in the same area, the MLA workflow brougth to a new enriched automatic catalog. The quality assessment of the new catalog indicates that the automatic picking is reliable and confirms the OT network’s ability to detect a high rate of low-magnitude seismicity. The NonLinLoc-SSST-Coherence algorithm was also applied to better identify the structures on which seismicity is accomodated and the results suggest that NonLinLoc-SSST-Coherence has better permormances when applied to small seismic sequences than to the widespread seismicity of GP. 

From a seismotectonic perspective, the already known seismogenic layer deepening northeasternward characterizing the GP seismicity here appears for the first time to be splitted in two structures located at different depth. This study highlights the crucial role of dense local networks and MLA tools in managing and analyzing large volumes of low-energy seismic data.