In this article, we implement a new approach to calibrate ground‐motion models (GMMs) characterized by spatially varying coefficients, using the calibration dataset of an existing GMM for crustal events in Italy. The model is developed in the methodological framework of the multisource geographically weighted regression (MS‐GWR, Caramenti et al., 2020), which extends the theory of multiple linear regression to the case with model coefficients that are spatially varying, thus allowing for capturing the multiple sources of nonstationarity in ground motion related to event and station locations. In this way, we reach the aim of regionalizing the ground motion in Italy by specializing the model in a nonergodic framework. Such an attempt at regionalization also addresses the purpose of capturing the regional effects in the modeling, which is needed for the Italian country, where ground‐motion properties vary significantly across space. Because the proposed model relies on the italian GMM (ITA18) (Lanzano et al., 2019) dataset and functional form, it could be considered the ITA18 nonstationary version, thus allowing one to predict peak ground acceleration and velocity, as well as 36 ordinates of the 5%‐damped acceleration response spectra in the period interval T=0.01–10s ⁠. The resulting MS‐GWR model shows an improved ability to predict the ground motion locally, compared with stationary ITA18, leading to a significant reduction of the total variability at all periods of about 15%–20%. The article also provides scenario‐dependent uncertainties associated with the median predictions to be used as a part of the epistemic uncertainty in the context of probabilistic seismic hazard analyses. Results show that the approach is promising for improving the model predictions, especially on densely sampled areas, although further studies are necessary to resolve the observed trade‐off inherent to site and path effects, which limits their physical interpretation.

Ground‐Motion Model for Crustal Events in Italy by Applying the Multisource Geographically Weighted Regression (MS‐GWR) Method

Luca Caramenti;Alessandra Menafoglio
2021

Abstract

In this article, we implement a new approach to calibrate ground‐motion models (GMMs) characterized by spatially varying coefficients, using the calibration dataset of an existing GMM for crustal events in Italy. The model is developed in the methodological framework of the multisource geographically weighted regression (MS‐GWR, Caramenti et al., 2020), which extends the theory of multiple linear regression to the case with model coefficients that are spatially varying, thus allowing for capturing the multiple sources of nonstationarity in ground motion related to event and station locations. In this way, we reach the aim of regionalizing the ground motion in Italy by specializing the model in a nonergodic framework. Such an attempt at regionalization also addresses the purpose of capturing the regional effects in the modeling, which is needed for the Italian country, where ground‐motion properties vary significantly across space. Because the proposed model relies on the italian GMM (ITA18) (Lanzano et al., 2019) dataset and functional form, it could be considered the ITA18 nonstationary version, thus allowing one to predict peak ground acceleration and velocity, as well as 36 ordinates of the 5%‐damped acceleration response spectra in the period interval T=0.01–10s ⁠. The resulting MS‐GWR model shows an improved ability to predict the ground motion locally, compared with stationary ITA18, leading to a significant reduction of the total variability at all periods of about 15%–20%. The article also provides scenario‐dependent uncertainties associated with the median predictions to be used as a part of the epistemic uncertainty in the context of probabilistic seismic hazard analyses. Results show that the approach is promising for improving the model predictions, especially on densely sampled areas, although further studies are necessary to resolve the observed trade‐off inherent to site and path effects, which limits their physical interpretation.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1209191
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