Enhancing the seismic catalog of the Gargano Area (Southern Italy) with machine learning-based event detection and earthquake relocation

Enriching seismic network catalogues is a key object to understand the seismotectonic processes and seismic hazard. In areas with poor knowledge of seismotectonic patterns, the installation of dense local seismic networks is essential. The enhanced density of seismic network coverage improves the detection of seismic events of low energy. Nowadays, Machine Learning (ML) techniques are becoming widely used in seismology, in addition to standard automatic procedures (i.e., STA/LTA-based algorithms).

In this study, we assess the performance of automatic P- and S-wave picking and earthquake detection algorithms for the period from 2013 to 2022, applied to the data recorded by the OTRIONS seismic network (FDSN code OT), a local network installed in 2013 in the Apulia region (Southern Italy) by UniBa and INGV.

The aim is to provide an automated data analysis system to collect a catalogue of the seismic activity of the Gargano area. For the period 2013-2022, a catalogue has been collected by employing CASP (Complete Automatic Seismic Processor), a software based on STA/LTA algorithms for automated event detection, picking and location. The obtained CASP automated catalogue has been manually revised to identify false events and quarry blasts.

Now, for the same period, the goal is to compile a new seismic catalogue for the Gargano area by using PhaseNet, an ML algorithm for phase detection, based on a deep neural network. We used GaMMA algorithm for phase association. Finally, NonLinLoc software was used to locate the events.

The results revealed a significant increase in the number of detected events with respect to the CASP processing. To evaluate the reliability of the results obtained by PhaseNet and GaMMA, a manual revision has been carried out on a sub-dataset of the collected event catalogue and compared with the CASP manual catalogue for the same period: we observed a significant increase in the earthquake detection. This increase also relates to events whose reliability has been verified.

From a seismotectonic point of view, the newly detected seismicity confirms the seismicity pattern of the Gargano Promontory, characterized by a deepening of the earthquakes trend moving northwards in the area with a clear and well defined cut off of the seismicity in the lower crust. This peculiar result is one of the most intriguing findings of the study and could provide important indications on the thermo-rheological characteristics of the lower crust.

Finally, to improve the knowledge of the seismogenic structures of the Gargano area, the new package of NonLinLoc, NLL-SSST-coherence, was used to looking for seismogenic structures. Preliminary results show that the Gargano area is characterised by widespread seismicity.