A scenario-based approach to generate empirical shaking maps in Central Italy from non-ergodic ground motion models

Abstract

Generation of seismic shaking maps as a tool to support decision making at a given site is a key-topic for civil protection planning and engineering purposes. Currently, shaking fields are based on Ground-Motion Models (GMMs), which estimate the intensity measures as a function of several parameters dependent on the reference earthquake scenario (magnitude, distance, soil category, etc.). However, GMMs are usually provided under the assumption of ergodic standard deviation (i.e. the spatial variability at many sites is assumed identical to the variability at a single site; Anderson and Brune, 1999). This assumption implies higher level of uncertainty associated to the model predictions, due to the fact that ergodic GMMs are calibrated on geographical areas where more records are available, thus neglecting region-specific features of ground motion behavior. However, in case of site-specific PSHA purposes or engineering applications at local scale (i.e. loss assessment and risk analyses of structures and infrastructures) there is the need to improve the ground motion prediction performance. In these cases, the simplified assumption of ergodicity may be not reliable to describe regional properties of the ground shaking (i.e. magnitude scaling, distance attenuation characteristics and site effects). More accurate predictions can thus be computed by relaxing the ergodic assumption in favor of non-ergodic approaches, in which the repeatable terms of variability due to source-, path- and –site effects are used to provide region-specific corrections of the median predictions, as well as to transfer part of the aleatory variability into epistemic uncertainty (e.g., Rodriguez-Marek et al., 2013; Villani and Abrahamson, 2015; Baltay et al., 2017; Lanzano et al., 2017). Following this concept, we propose a methodology for generating empirical shaking maps of the acceleration spectral ordinates based on a non-ergodic GMM calibrated on Central Italy, in which the systematic contributions of the variability are decomposed. The obtained corrective terms are then mapped by means of spatial correlation models to provide the local adjustments of the ground shaking, following the approaches proposed for California (Landwear, 2019; Sahakian et al., 2019) and Emilia region in Italy (Sgobba et al., 2019). We finally simulate the ground shaking empirically, by adding up the ground motion intensity field predicted by the GMM and the spatially correlated fields of the site (S2S), source region (L2L) and path (P2P) effects computed at any point of a regular grid. Implementation of such a modelling clearly requires a dense dataset in order to compute robust estimation of the repeatable terms. For this reason, we focus our study on Central Italy, where a huge quantity of high- quality strong-motion records (more than 30.000 waveforms) has become available after the occurrences of significant events in the last 10 years. Results show peculiar spatial patterns of the site and path effects in the region, that can be related to physical aspects not fully captured by the GMM. The impact of the corrections on the shaking pattern and spectral intensity amplitudes is also shown through empirical simulations of the ground motion scenarios related to past earthquakes.