GNGTS 2024 - Atti del 42° Convegno Nazionale

Session 2.1 GNGTS 2024 Possible applications of self-similarity in earthquake clustering to seismic hazard D. Zaccagnino 1* , L. Telesca 2 , C. Doglioni 1,3 1 Sapienza University, Earth Sciences Department, Rome, Italy 2 Institute of Methodologies for Environmental Analysis, National Research Council, Tito, Italy 3 Istituto Nazionale di Geofisica e Vulcanologia (INGV), Rome, Italy We perform an analysis to understand what information may be hidden in partial, limited earthquake catalogues only containing mid-size and a few large seismic events (or even no one) about the largest possible ones using clustering properties of recorded events. We consider the local and global coefficients of variation, the scaling exponent of the Gutenberg–Richter law, the fractal dimension of epicentral series D f , the seismic rate and the number of events. We find that the largest earthquakes occur in locally Poissonian systems (local coefficient of variation of interevents L V 1) with globally clustered dynamics (global coefficient of variation C V 1). While local clustering in time is strongly dependent on the size of the catalogue, so that longer databases tend to be less regular and more Poissonian than shorter ones, the global coefficient seems to be a reliable parameter even in cases of rather limited available information, e.g., few thousand events (Zaccagnino et al., 2023a). We analyse regional seismicity in different tectonic settings getting analogous results, e.g., Southern California, Cascadia (Zaccagnino et al., 2022), Italian Apennines, New Zealand (Zaccagnino et al., 2023a), and Turkey (Zaccagnino et al., 2023b). The fractal dimension of spatial series is positively correlated with the seismic rate, C V and the maximum listed magnitude. Conversely, the b-value does not show any correlation with the principal observables except for the number of earthquakes. We explain this phenomenon considering the different sizes of mainshocks in various tectonic settings. We propose that the predictive power of clustering properties stems from the self-similar nature of slow dynamics producing the emergence of slips in complex systems such as the brittle crust. Prospectively, this approach can be of great interest, once tuned, to extrapolate the features of extreme, still unobserved events given a limited database. References Zaccagnino D., Telesca L. and Doglioni C.; 2022: Variable seismic responsiveness to stress perturbations along the shallow section of subduction zones: The role of different slip modes and implications for the stability of fault segments. Front. Earth Sci., 10, 989697. Zaccagnino D., Telesca L. and Doglioni, C.; 2023: Global versus local clustering of seismicity: Implications with earthquake prediction. Chaos Solit. Fractals, 170, 113419. Zaccagnino D., Telesca L., Tan O. and Doglioni, C.; 2023: Clustering Analysis of Seismicity in the Anatolian Region with Implications for Seismic Hazard. Entropy, 25(6), 835.