Modelling the spatial correlation of earthquake ground motion : insights from the literature, data from the 2016-2017 central Italy earthquake sequence and ground-motion simulations

Schiappapietra, Erika and Douglas, John (2020) Modelling the spatial correlation of earthquake ground motion : insights from the literature, data from the 2016-2017 central Italy earthquake sequence and ground-motion simulations. Earth-Science Reviews, 203. 103139. ISSN 1872-6828

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    Abstract

    Over the past decades, researchers have given increasing attention to the modelling of the spatial correlation of earthquake ground motion intensity measures (IMs), particularly when the seismic risk of spatially distributed systems is being assessed. The quantification of the seismic performance of these systems requires the estimation of simultaneous IMs at multiple locations during the same earthquake, for which the correlation between pairs of locations needs to be defined. Numerous spatial correlation models of common IMs, such as peak ground acceleration and spectral acceleration, have been published. Although the functional forms of the models are generally similar, significant discrepancies exist in terms of the rate of decay of the correlation with increasing inter-site separation distance. The main reasons for such differences lie with the selected databases, the ground-motion models used to derive the spatial correlation models, estimation approaches and regional geological conditions. In this study, we aim to provide a comprehensive review of spatial correlation models, analysing factors that most affect the spatial dependency of IMs. We use strong-motion records from the 2016-2017 Central Italy earthquake sequence combined with ground-motion simulations to examine the influence of various factors on spatial correlation models. We investigate the dependency on: (1) the estimation method and model fitting technique; (2) the magnitude; (3) the response-spectral period; and (4) local-soil conditions. Our results suggest that the rate of decay is not only period-dependent, but also regionally-dependent, so that a single universal correlation model based on large datasets is not appropriate when describing the correlation behaviour of small geographical areas. Our outcomes could be used to guide the development of new spatial correlation models.