A New Empirical Ground‐Motion Model for Iran

2019 ◽  
Vol 109 (2) ◽  
pp. 732-744 ◽  
Author(s):  
Z. Farajpour ◽  
S. Pezeshk ◽  
M. Zare
Keyword(s):  
Ground Motion ◽  
Motion Model ◽  
Ground Motion Model ◽  
Empirical Ground

Empirical ground motion model for Vrancea intermediate-depth seismic source

2014 ◽  
Vol 6 (2) ◽  
pp. 141-161 ◽  
Author(s):  
Radu Vacareanu ◽  
Sorin Demetriu ◽  
Dan Lungu ◽  
Florin Pavel ◽  
Cristian Arion ◽  
...  
Keyword(s):  
Ground Motion ◽  
Seismic Source ◽  
Motion Model ◽  
Intermediate Depth ◽  
Ground Motion Model ◽  
Empirical Ground

scholarly journals Simulation of non-stationary stochastic ground motions based on recent Italian earthquakes

2021 ◽  
Author(s):  
Fabio Sabetta ◽  
Antonio Pugliese ◽  
Gabriele Fiorentino ◽  
Giovanni Lanzano ◽  
Lucia Luzi
Keyword(s):  
Ground Motion ◽  
Ground Motions ◽  
Strong Motion ◽  
Stochastic Simulations ◽  
Model Parameters ◽  
Motion Model ◽  
Arias Intensity ◽  
Time Frequency ◽  
Ground Motion Model ◽  
Ground Motion Records

AbstractThis work presents an up-to-date model for the simulation of non-stationary ground motions, including several novelties compared to the original study of Sabetta and Pugliese (Bull Seism Soc Am 86:337–352, 1996). The selection of the input motion in the framework of earthquake engineering has become progressively more important with the growing use of nonlinear dynamic analyses. Regardless of the increasing availability of large strong motion databases, ground motion records are not always available for a given earthquake scenario and site condition, requiring the adoption of simulated time series. Among the different techniques for the generation of ground motion records, we focused on the methods based on stochastic simulations, considering the time- frequency decomposition of the seismic ground motion. We updated the non-stationary stochastic model initially developed in Sabetta and Pugliese (Bull Seism Soc Am 86:337–352, 1996) and later modified by Pousse et al. (Bull Seism Soc Am 96:2103–2117, 2006) and Laurendeau et al. (Nonstationary stochastic simulation of strong ground-motion time histories: application to the Japanese database. 15 WCEE Lisbon, 2012). The model is based on the S-transform that implicitly considers both the amplitude and frequency modulation. The four model parameters required for the simulation are: Arias intensity, significant duration, central frequency, and frequency bandwidth. They were obtained from an empirical ground motion model calibrated using the accelerometric records included in the updated Italian strong-motion database ITACA. The simulated accelerograms show a good match with the ground motion model prediction of several amplitude and frequency measures, such as Arias intensity, peak acceleration, peak velocity, Fourier spectra, and response spectra.

scholarly journals A Non-Ergodic Effective Amplitude Ground-Motion Model for California

2021 ◽  
Author(s):  
Grigorios Lavrentiadis ◽  
Norman A. Abrahamson ◽  
Nicolas M. Kuehn
Keyword(s):  
Standard Deviation ◽  
Ground Motion ◽  
Epistemic Uncertainty ◽  
Amplitude Spectrum ◽  
Time History ◽  
Motion Model ◽  
Small Magnitude ◽  
Varying Coefficients ◽  
Ground Motion Model ◽  
Systematic Effects

Abstract A new non-ergodic ground-motion model (GMM) for effective amplitude spectral (EAS) values for California is presented in this study. EAS, which is defined in Goulet et al. (2018), is a smoothed rotation-independent Fourier amplitude spectrum of the two horizontal components of an acceleration time history. The main motivation for developing a non-ergodic EAS GMM, rather than a spectral acceleration GMM, is that the scaling of EAS does not depend on spectral shape, and therefore, the more frequent small magnitude events can be used in the estimation of the non-ergodic terms. The model is developed using the California subset of the NGAWest2 dataset Ancheta et al. (2013). The Bayless and Abrahamson (2019b) (BA18) ergodic EAS GMM was used as backbone to constrain the average source, path, and site scaling. The non-ergodic GMM is formulated as a Bayesian hierarchical model: the non-ergodic source and site terms are modeled as spatially varying coefficients following the approach of Landwehr et al. (2016), and the non-ergodic path effects are captured by the cell-specific anelastic attenuation attenuation following the approach of Dawood and Rodriguez-Marek (2013). Close to stations and past events, the mean values of the non-ergodic terms deviate from zero to capture the systematic effects and their epistemic uncertainty is small. In areas with sparse data, the epistemic uncertainty of the non-ergodic terms is large, as the systematic effects cannot be determined. The non-ergodic total aleatory standard deviation is approximately 30 to 40% smaller than the total aleatory standard deviation of BA18. This reduction in the aleatory variability has a significant impact on hazard calculations at large return periods. The epistemic uncertainty of the ground motion predictions is small in areas close to stations and past event.

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