An up-to-date seismic catalogue of the 2020 Mw6.4 Petrinja (Croatia) earthquake sequence using machine learning

Identifying earthquakes and selecting their arrival phases are essential tasks in earthquake analysis. As more seismic instruments become available, they produce vast amounts of seismic data. This necessitates the implementation of automated algorithms for efficiently processing earthquake sequences and for recognising numerous events that might go unnoticed with manual methods.In this study, we employed the EQTransformer, trained on the INSTANCE dataset, and utilised PyOcto for phase association, focusing specifically on the Petrinja earthquake series. This series is particularly interesting for its initial phase, which was marked by a limited number of seismometers in the epicentral area during the onset of the sequence in late December 2020. This limitation was subsequently addressed by the swift deployment of five additional stations near the fault zone in early January 2021, followed by a gradual expansion of the seismic network to over 50 instruments.Our analysis covers the Petrinja earthquake series from its onset on December 28, 2020, up to present, offering a complete and up-to-date view of the seismic activity as the seismic activity is still higher than in the interseismic period. We compare our findings from the machine learning-generated catalogue with a detailed manual catalogue. Focusing on the first week of the series, when the seismic network was sparse and there was a high frequency of overlapping earthquakes, we achieved a recall of 80% and a precision of 81% for events with local magnitude greater than 1.0. In contrast, for the subsequent six months of processed data, a period still characterised by a high frequency of earthquakes but with the fully expanded network, our recall improved dramatically to 95% with over 20,000 detected events. This comparison allows us to demonstrate the challenges, evolution, and effectiveness of automatic seismic monitoring throughout the earthquake sequence.