Seismic clusters and fluids diffusion: a lesson from the 2018 Molise (Southern Italy) earthquake sequence

The identification of seismic clusters is essential for many applications of statistical analysis and seismicity forecasting: uncertainties in cluster identification leads to uncertainties in results. However, there are several methods to identify clusters, and their results are not always compatible. We tested different approaches to analyze the clustering: a traditional window-based approach, a complex network-based technique (nearest neighbor—NN), and a novel approach based on fractal analysis. The case study is the increase in seismicity observed in Molise, Southern Italy, from April to November 2018. To analyze the seismicity in detail with the above-mentioned methods, an improved template-matching catalog was created. A stochastic declustering method based on the Epidemic Type Aftershock Sequence (ETAS) model was also applied to add probabilistic information. We explored how the significant discrepancies in these methods’ results affect the result of NExt STrOng Related Earthquak...