Investigating the 2024 swarm like activity offshore Kefalonia Island aided by Machine Learning algorithms

V. Anagnostou; E. Papadimitriou; V. Karakostas; T. Back

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arXiv · 2505.17221

Investigating the 2024 swarm like activity offshore Kefalonia Island aided by Machine Learning algorithms

V. Anagnostou·E. Papadimitriou·V. Karakostas·T. Back

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Abstract

In March 2024, a swarm like seismic activity occurred north of Kefalonia Island, in the central Ionian Islands area. Following a machine-learning aided workflow, we compiled an enhanced seismic catalog of 2495 low to moderate magnitude earthquakes throughout a 2 month period. Spatiotemporal analysis reveals a narrow epicentral distribution of nearly E-W alignment, approximately 5km long, much longer than the length anticipated by common scaling laws for the aftershock area extension of the stronger earthquakes that did not exceed M4.0. The findings of the study indicate that the swarm like activity is possibly triggered by a combination of fluid movements and Coulomb stress changes. The strongest earthquakes appear beyond the diffusivity curves that are within the expected upper crust values and are possibly triggered by stress transfer by the first strong earthquake. Fluid effects rapidly diminish within the first days, while the changes in the stress field due to the combined effect of the two strongest earthquakes promote the triggering of most of the weaker earthquakes of the excitation. The findings of this study reinforce the idea of swarm-like activity initiating due to interactions between stress redistributions and fluid movements in the upper crust. The rapid employment of ML tools for the compilation of robust seismic catalogs can vastly improve our understanding of the processes that drive seismicity in highly productive areas such as the Central Ionian Islands, thus leading to improved seismic hazard assessment.

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V. Anagnostou, E. Papadimitriou, V. Karakostas, T. Back. 2025-05-22. Investigating the 2024 swarm like activity offshore Kefalonia Island aided by Machine Learning algorithms. https://arxiv.org/abs/2505.17221

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