GNGTS 2014 - Atti del 33° Convegno Nazionale

10 GNGTS 2014 S essione 2.1 Gruppo di Lavoro MPS; 2004: Redazione della mappa di pericolosità sismica prevista dall’Ordinanza PCM 3274 del 20 marzo 2003, Rapporto conclusivo per il dipartimento di Protezione Civile , INGV, Milano—Roma, aprile 2004, 65 pp. + 5 appendici, http://zonesismiche.mi.ingv.it/documenti/rapporto_conclusivo.pdf. Ornthammarath T., Douglas J., Sigbjörnsson R., Lai C.G.; 2011: Assessment of ground motion variability and its effects on seismic hazard analysis: a case study for Iceland . Bulletin of Earthquake Engineering, 9 , 931-953. Ripperger J., Mai P.M., Ampuero J.P.; 2008: Variability of Near-Field Ground Motion from Dynamic Earthquake Rupture Simulations . Bulletin of the Seismological Society of America, 98 , 1207-1228. Sabetta F. and Pugliese A.; 1996: Estimation of response spectra and simulation of nonstationary earthquake ground motions . Bulletin of the Seismological Society of America, 2 , 337-352. Strasser F.O., Bommer J.J. and Abrahamson N.A.; 2008: Estimating ground-motion variability: issues, insights & challenges . Proceedings of the 14 th World Conference on Earthquake Engineering, October 12-17, Beijing, China. Strasser F.O., Bommer J.J. and Abrahamson N.A.; 2009: Sigma: issues, insights, and challenges. Seismological Research Letters, 80 , 40-56. on the infulence of ground motion predictive equationS on Probabilistic Seismic Hazard analysis, part 2: testing and scoring past and recent attenuation models S. Barani 1 , D. Albarello 2 , M. Massa 3 , D. Spallarossa 1 1 Dipartimento di Scienze della Terra dell’Ambiente e della Vita, Università di Genova, Italy 2 Dipartimento di Scienze Fisiche della Terra e dell’Ambiente, Università di Siena, Italy 3 Istituto Nazionale di Geofisica e Vulcanologia, Milano, Italy Foreword and scope of work. As known, ground motion prediction equations (GMPE) are stochastic models that estimate the probability distribution associated to the possible shaking levels induced at a site by an earthquake as a function of several parameters, such as magnitude, source-to-site distance, style of faulting, and ground type. Their parameterization implies statistical analyses on a large number of observations which, in turn, are subjected to a heavy work of processing in order to achieve uniform data sets. The signal processing and the homogenization of data coming from different institutions are certainly the most critical steps in the derivation of a GMPE. Signals have to be corrected and filtered; earthquake magnitudes are estimated using a uniform scale as well as distances are determined adopting a uniform distance metrics; site conditions at recording stations have to be determined (at least in the form of soil categories) along with the fault mechanism of the relevant seismic source. Despite of the great efforts in developing GMPEs, it is not so unusual to listen that predictions are biased because of inaccuracies in the regression data sets. For instance, it is anything but a joke to listen that ground motion values are wrong due to a wrong setup (e.g., wrong seismometer’s generator constant) of the recording instruments. It is more frequent to discuss that the reliability and accuracy of predictions is affected by gross site classifications based on large-scale geological mapping. It is also frequent listening that GMPEs neglect topographic effects or, better, that ridges and crests are lost inside the large number of sites considered in the definition of a GMPE (e.g., Barani et al. , 2014). The sensation of the writers is that the time goes by and regression data sets become larger and larger (Fig. 1a), functional forms are increasingly complex, and the variability of ground motion increases (Fig. 1b) although additional explanatory variables (e.g., variables that allow for soil nonlinear behavior, rupture directivity, high-frequency attenuation) are incorporated into the mathematical models. Throughout this proliferation of data sets and GMPEs, scientists (including the writers) are losing sight of the limitations of their models and, possibly, the steady improvement in the performance of brand new GMPEs. This is reflected in Probabilistic Seismic Hazard (PSH)