GNGTS 2021 - Atti del 39° Convegno Nazionale

225 GNGTS 2021 S essione 2.1 -sections have to be located at a distance less than or equal to 5 km from each other(measured at the surface). Thus, the number of main faults -sections that can be activated in a single event, in the illustrated example, ranged from one up to a maximum of seven main faults -sections. Finally, to compute seismicity rates, we use SHERIFS (Chartier et al., 2017), a code that converts slip-rates along faults into earthquake rates following a forward incremental approach. For the purpose of this presentation, we considered only one realization of the SHERFIS aleatory exploration of rupture scenarios based on the mean slip rates assigned to each main fault -section and mean magnitude scaling values. To compute seismic hazard, we use the synthetic seismicity rates resulting from the SHERIFS’ realization based on the 10 km deep seismogenic hypothesis, considering earthquake rates for M ≥ 5.0, the Bindi et al. (2011) ground motion prediction equation (GMPE) for the peak ground acceleration (PGA) and a soil coefficients of Vs30 = 800 m/s (hard rock).We used the Bindi GMPE, which received the highest score in the Lanzano et al. (2020) study, where a large number of GMPEs applicable in active shallow crustal regions were tested and ranked in the framework of a new Italian hazard model (Meletti et al., 2021). Seismic risk, as typically defined in engineering, is obtained by the convolution of hazard, vulnerability and exposure. Here we considered only the convolution of vulnerability and hazard for typological classes, thus referred to in the literature as “typological seismic risk” (Rosti et al. 2020). In particular, we show at each locality only computations for the annual probability of collapse for a single “historic” small edifice given its seismic hazard. Thus, unlike a full risk analysis, in the “typological seismic risk” calculations there is a systematic relationship between hazard and risk. We adopted a fragility model for residential masonry buildings (Rosti et al. 2020) developed within the framework of the Italian national platform for large-scale seismic risk assessment The resulting hazard and typological risk maps allow both data providers and end-users 1) to visualize the faults that threaten specific localities the most, 2) to appreciate the density of observations used for the computation of slip rate profiles, and 3) interrogate the degree of confidence on the fault parameters documented in the database (activity and location certainty). Finally, closing the loop, the methodology highlights priorities for future geological investigations in terms of where improvements in the density of data within the database would lead to the greatest decreases in epistemic uncertainties in the hazard and risk calculations. Key to this new generation of fault-based seismic hazard and risk methodology are the user-friendly open source codes provided with this publication, documenting, step-by-step, the link between the geological database and the relative contribution of each section to seismic hazard and risk at specific localities. References Biasi G. P. and Wesnousky S. G.; 2016: Steps and gaps in ground ruptures: empirical bounds on rupture propagation . Bull. Seismol. Soc. Am. 106 (3), 1110–1124. doi:10.1785/0120150175 Biasi G. P. and Wesnousky S. G.; 2017: Bends and ends of surface Ruptures Bends and ends of surface ruptures . Bull.Seismol. Soc. Am. 107 (6), 2543–2560. doi:10.1785/0120160292. Bindi D., Pacor F., Luzi L., Puglia R., Massa M., Ameri G. et al.; 2011: Ground motion prediction equations derived from the Italian strong motion database . Bull. Earthq. Eng. 9 (6), 1899–1920. doi:10.1007/ s10518-011-9313-z. Boncio P., Tinari D. P., Lavecchia G., Visini F. and Milana G.; 2009: The instrumental seismicity of the abruzzo region in Central Italy (1981–2003): seismotectonic implications. Ital. J. Geosci. (Boll. Soc. Geol. Italy) 128(2), 367–380.doi:10.3301/IJG.2009.128.2.367. Chartier T., Scotti O., Lyon-Caen H. and Boiselet A.; 2017: Methodology for earthquake rupture rate estimates of fault networks: example for the western Corinth rift, Greece . Natural Hazards Earth Syst. Sci. Eur. Geosci. Union 17 (10), 1857–1869. doi:10.5194/nhess-2017-124.