Unraveling the dynamics of the 2021 Arkalochori foreshock swarm: a fusion of machine-learning models and non-extensive statistical physics

On 27 September 2021, a significant M_w=6.0 earthquake struck near Arkalochori village in central Crete, Greece, about ~25 km south-southeast of Heraklion city. Remarkably, an extensive seismic swarm lasting nearly four months preceded the mainshock, activating structures near its hypocenter. In this work, we investigate the foreshock swarm by leveraging waveform data from seismological stations of the Hellenic Unified Seismic Network (HUSN) that were operational on Crete Island during its occurrence. Our approach involves the utilization of the EQ-Transformer machine-learning model, pre-trained with a diverse dataset comprising ~50,000 earthquakes sourced from the INGV bulletin (INSTANCE dataset). We employ a sophisticated methodology that incorporates a Bayesian Gaussian Mixture Model (GaMMA) to associate automatically picked P- and S-wave arrival times with event origins. Subsequently, the events are located using a local velocity model. Our findings reveal the detection and precise location (ERH < 1 km, RMS < 0.2 s) of over 3,400 events in the activated area between late May and 26 September 2021, showcasing a substantial increase compared to existing catalogs derived from routine analysis using conventional methods. The spatiotemporal distribution of the foreshock seismicity is examined to unveil migration patterns, potentially linked to fluid dynamics and pore-pressure diffusion. Furthermore, we explore the evolution of seismicity concerning different structures activated during the seismic swarm, with a particular focus on the final days leading up to the mainshock. Finally, our results are subjected to analysis through non-extensive statistical physics methods, providing a comprehensive understanding of the complex dynamics culminating in the Arkalochori earthquake sequence.

Acknowledgements

We would like to thank the personnel of the institutions participating to the Hellenic Unified Seismological Network (http://eida.gein.noa.gr/) for the installation, operation and management of the seismological stations used in this work. The present study is co-funded by the Special Account for Research Grants (S.A.R.G.) of the National and Kapodistrian University of Athens.