Insights into spatially correlated ground motion intensity measures using Italian earthquakes

Modelling the spatial correlation of ground motion intensity measures (IMs) has become a keystone in seismic hazard and risk analysis of portfolios of buildings, spatially distributed infrastructures and earthquake-induced phenomena. The quantification of the seismic performance of such systems over a region requires knowledge of the joint probability of occurrence of different ground motion IMs at multiple locations. Therefore, the classical Probabilistic Seismic Hazard Assessment (PSHA) tools, which are based on the hypothesis of independency between IMs at closely spaced sites, are not appropriate. Over the past decade, the spatial correlation of peak ground acceleration (PGA) and spectral acceleration (SA) has been widely studied. Although common findings suggest that the correlation of intra-event residuals decreases quite rapidly with increasing separation distances, these models feature different rates of decay. Among the causes that may lead to inconsistencies between models, with significant impact on hazard and loss estimates, are the multiple techniques used to estimate the correlation structure, the region and local site conditions, as well as the choice of the databases. Furthermore, little effort has been directed towards other IMs suitable to characterize the resulting damage to structures and predict ground failure: peak ground velocity (PGV), peak ground displacement (PGD) and spectral displacement (SD) as well as Arias intensity (퐼") and cumulative absolute velocity (CAV), to name but a few. A proper definition of the seismic action in terms of spectral displacement ordinates has progressively gained importance in performance-based seismic design, and 퐼" and CAV have been found to be adequate for many other earthquake engineering applications, such as evaluating the susceptibility to liquefaction and earthquake-induced landslides. In this study, we use geostatistical tools in order to compute the spatial correlations of such ground motion parameters. We perform comparisons with other existing models with the aim of: (1) identifying factors that most affect the correlation structure, and (2) quantifying the variability of correlation lengths between different events and regions. Moreover, spatial correlation models are usually calibrated on the within-event component of residuals, obtained based on ergodic ground motion prediction equations (GMPEs). Therefore, we also analyse the spatial correlation of event- and site- corrected residuals, retrieved relaxing the ergodic assumption, to further investigate the factors that determine the spatial dependency of IMs. In order to address these issues, we use the 2016-2017 Central Italy seismic sequence database, which includes nine Mw ≥ 5.0 earthquakes that occurred over a time period of five months. These data allow some uncertainties to be removed and an evaluation of the event-to-event variability of the spatial correlation because the same seismic region is considered. Our preliminary results will provide a more accurate picture of ground motions, and thus improve the modelling of earthquake losses for risk model development