GNGTS 2019 - Atti del 38° Convegno Nazionale

GNGTS 2019 S essione 2.1 321 Conclusions. Our results are compatible with the hypothesis of similar earthquake clustering properties in the three tectonic regions, superimposed to a background seismicity described by the Poisson process, whose rate may vary on short time periods. We conclude that, at least for active seismic crustal regions, the type of tectonic regime does not seem to play a key role in how seismic sequences evolve in the spatiotemporal domain. We reach the same conclusion when analyzing the influence of subregions with homogeneous deformation style. Inspecting whether clustering properties are universal or region-dependent could play a key role in the improvement of both probabilistic seismic hazard analysis and earthquake forecast. Our results show exclusively a non-significant influence of the tectonic regime on the earthquake clustering properties. Our findings have important implications for seismic hazard analysis and operational earthquake forecasting. It supports the use of common declustering models for different crustal tectonic areas, avoiding the need to re-calibrate them for each specific region. Then, in the prospect of earthquake forecast, our study suggests that calibrating operational earthquake forecast models based on the specific tectonic regime may not be as informative as believed, at least in the case of active seismic crustal regions. References Baiesi, M. & Paczuski, M. (2004). Scale-free networks of earthquakes and aftershock. Physical review E 69(6), 066106. Oth, A. (2013). On the characteristics of earthquake stress release variations in Japan. Earth and Planetary Science Letters ,  377 , 132-141. Roselli, P., Marzocchi, W., Mariucci, M. T., & Montone, P. (2017). Earthquake focal mechanism forecasting in Italy for PSHA purposes. Geophysical Journal International ,  212 (1), 491-508. Yang, W., & Hauksson, E. (2013). The tectonic crustal stress field and style of faulting along the Pacific North America Plate boundary in Southern California. Geophysical Journal International ,  194 (1), 100-117. Zaliapin, I., & Ben-Zion, Y. (2013). Earthquake clusters in southern California I: Identification and stability. Journal of Geophysical Research: Solid Earth , 118(6), 2847-2864. Zaliapin, I., Gabrielov, A., Keilis-Borok, V., & Wong, H. (2008). Clustering analysis of seismicity and aftershock identification. Physical Review Letters , 101(1), 018501. IS IT THE UCERF3 MODEL FRAMEWORK SUITABLE FOR ITALY? A. Valentini Università degli Studi “G.d’Annunzio” di Chieti-Pescara, DiSPUTer Department, Italy The first step in probabilistic seismic hazard analysis (PSHA) is the evaluation of all possible earthquakes that could occur in a region and the definition of all possible seismogenic sources. Fault-based PSHA uses active faults, as seismic source, in order to capture the occurrence of large-magnitude events. In the last 20 years, fault-based PSHA has become a consolidated approach in many countries characterized by high strain rates and seismic releases (e.g. Field et al. , 2014; Stirling et al. , 2012) and in regions with moderate-to-low strain rates (e.g. Garcia- Mayordomo et al. , 2007, Scotti et al. , 2014, Valentini et al. , 2017). Currently, the approach mainly used in Europe, and in particular in central Italy, for fault-based PSHA is a strictly segmented fault source model (e.g. Valentini et al. 2019) that considers each fault independently as an individual seismogenic source, an individual structure that can break entirely during an earthquake and for which the measured maximum rupture length and maximum rupture area can be used to infer the maximum expected magnitude and slip per event; with the occurrence