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Ground‐motion intensity measure correlations observed in Italian strong‐motion records

Huang, Chen ; Galasso, Carmine

Earthquake engineering & structural dynamics, 2019-12, Vol.48 (15), p.1634-1660 [Periódico revisado por pares]

Bognor Regis: Wiley Subscription Services, Inc

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  • Título:
    Ground‐motion intensity measure correlations observed in Italian strong‐motion records
  • Autor: Huang, Chen ; Galasso, Carmine
  • Assuntos: Acceleration ; Algorithms ; Coefficients ; Correlation ; Correlation analysis ; Correlation coefficient ; Correlation coefficients ; Earthquakes ; Empirical analysis ; Geological hazards ; ground‐motion model ; Hazard assessment ; intensity measure correlation ; Italy ; Model testing ; Movement ; Probability theory ; Records ; Regression analysis ; Seismic activity ; Seismic analysis ; Seismic hazard ; spatial correlation ; Statistical analysis
  • É parte de: Earthquake engineering & structural dynamics, 2019-12, Vol.48 (15), p.1634-1660
  • Descrição: Summary Ground‐motion models (GMMs) are widely used in probabilistic seismic hazard analysis (PSHA) to estimate the probability distributions of earthquake‐induced ground‐motion intensity measures (IMs) at a site, given an earthquake of a certain magnitude occurring at a nearby location. Accounting for spatial and cross‐IM correlations in earthquake‐induced ground motions has important implications on probabilistic seismic hazard and loss estimates. This study first develops a new Italian GMM with spatial correlation for 31 amplitude‐related IMs, including peak ground acceleration (PGA), peak ground velocity (PGV), and 5%‐damped elastic pseudo‐spectral accelerations (PSAs) at 29 periods ranging from 0.01 to 4 seconds. The model estimation is performed through a recently developed one‐stage nonlinear regression algorithm proposed by the authors, known as the Scoring estimation approach. In fact, current state‐of‐practice approaches estimate spatial correlation separately from the GMM estimation, resulting in inconsistent and statistically inefficient estimators of interevent and intraevent variances and parameters in the spatial correlation model. We test whether this affects the subsequent cross‐IM correlation analysis. To this aim, based on the newly developed GMM, the empirical correlation coefficients from interevent and intraevent residuals are investigated. Finally, a set of analytical correlation models between the selected IMs are proposed. This is of special interest as several correlation models between different IMs have been calibrated and validated based on advanced GMMs and global datasets, lacking earthquakes in extensional regions; however, modeling the correlation between different IM types has not been adequately addressed by current, state‐of‐the‐art GMMs and recent ground‐motion records for Italy.
  • Editor: Bognor Regis: Wiley Subscription Services, Inc
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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