Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/15843
Authors: Sgobba, Sara* 
Faenza, Licia* 
Brunelli, Giulio* 
Lanzano, Giovanni* 
Title: Assessing the impact of an updated spatial correlation model of ground motion parameters on the italian shakemap
Journal: Bulletin of Earthquake Engineering 
Series/Report no.: /21 (2023)
Publisher: Springer
Issue Date: 2023
DOI: 10.1007/s10518-022-01581-y
Abstract: This study develops a new spatial correlation model for Italy using the most up-to-date and densest dataset of accelerometer and velocimeter records available. The objective is to estimate the average correlation length and assess its impact on the prediction accuracy of the Italian Shakemap compared to the global model (Loth and Baker, 2013–LB13) adopted in the default configuration of the program. We compute the spatial covariance structure using a geostatistical approach based on traditional variography applied to standardized residuals within the events of a reference ground motion model (ITA10). We observe spatial clusters of the correlation lengths and a wide variability over the Italian territory linked to the profound heterogeneity of the geological and geomorphological context. The obtained estimates are then implemented within the LB13 co-regionalization model in place of the default values while assuming the same cross-correlation coefficients among spectral parameters. Although our results are quite consistent with previous models calibrated for Italy, we find that the inclusion of the new correlation lengths in the Shakemap predictions, assessed through a leave-one-out cross-validation technique, results in a non-appreciable improvement over the global model, thus indicating that the adopted approach is not able to resolve the regional features and the corresponding spatial correlation with reference to individual scenarios. These findings may suggest the need to move towards nonergodic models in the Shakemap computing to better capture the spatial variability or to determine different co-regionalisation matrices more suitable for the regional applications.
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