Uncertainties in strong ground motion prediction with finite fault synthetic seismograms: an application to the 1984 M5.7 Gubbio, Central Italy, earthquake
EMOLO, ANTONIO (2009) Uncertainties in strong ground motion prediction with finite fault synthetic seismograms: an application to the 1984 M5.7 Gubbio, Central Italy, earthquake. [Pubblicazione su rivista scientifica]
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The aim of this paper is to investigate the engineering applicability of two conceptually different finite-fault simulation techniques. We focus our attention on two important aspects: first to quantify the capability of the different simulation techniques to reproduce the observed ground-motion parameters (e.g. Spectral Acceleration, PGA, PGV, Arias Intensity, Housner Intensity, Significant Duration); second to quantify the dependence of the different strong-motion parameters on the aleatory variability in the kinematic definition of the source (e.g. position of nucleation point, value of the rupture velocity and distribution of the final slip on the fault). In this study we applied an approximated simulation technique, Deterministic-Stochastic method DSM, and a broadband technique, the Hybrid-Integral-Composite method to model the 1984 Mw 5.7 Gubbio, central Italy, earthquake, at 5 accelerometric stations located at hypocentral distances less than 40 km. We first optimize the position of nucleation point and the value of rupture velocity, by minimizing an error function in terms of acceleration response spectra in the frequency band from 1 to 9 Hz. We found that the best model is given by a bilateral rupture propagating at about 2.5 km/s. In the second part of the paper we calculate, with both simulation techniques, more than 800 scenarios varying the kinematic source parameters. At the five sites we compute the Cumulative Distribution Functions for the various strong motion parameters and show that their shapes depend on the source-parameterization adopted by the two techniques. Furthermore we show that, regardless the simulation technique, the distributions are different for each strong-motion parameter, with the Arias Intensity characterized by the largest dispersion. The fact that the aleatory variability of the kinematic model affects the variability of different ground-motion parameters in different ways has to be taken into account when predicting ground motions for future events.
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