A Nonergodic Ground‐Motion Model for California with Spatially Varying Coefficients

2016 ◽  
Vol 106 (6) ◽  
pp. 2574-2583 ◽  
Author(s):  
Niels Landwehr ◽  
Nicolas M. Kuehn ◽  
Tobias Scheffer ◽  
Norman Abrahamson
Keyword(s):  
Ground Motion ◽  
Motion Model ◽  
Varying Coefficients ◽  
Ground Motion Model ◽  
Spatially Varying

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.

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

2021 ◽  
Author(s):  
Giovanni Lanzano ◽  
Sara Sgobba ◽  
Luca Caramenti ◽  
Alessandra Menafoglio
Keyword(s):  
Ground Motion ◽  
Geographically Weighted Regression ◽  
Epistemic Uncertainty ◽  
Response Spectra ◽  
Weighted Regression ◽  
Multiple Sources ◽  
Ground Acceleration ◽  
Varying Coefficients ◽  
Ground Motion Model ◽  
Spatially Varying

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=0.01–10  s. 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.

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.

A ground motion model for volcanic areas in Italy

2019 ◽  
Vol 18 (1) ◽  
pp. 57-76 ◽  
Author(s):  
Giovanni Lanzano ◽  
Lucia Luzi
Keyword(s):  
Ground Motion ◽  
Motion Model ◽  
Ground Motion Model

Conditional Ground-Motion Model for Damaging Characteristics of Near-Fault Ground Motions Based on Instantaneous Power

2020 ◽  
Vol 110 (6) ◽  
pp. 2828-2842
Author(s):  
Esra Zengin ◽  
Norman Abrahamson
Keyword(s):  
Ground Motion ◽  
Ground Motions ◽  
Accurate Estimation ◽  
Spectral Acceleration ◽  
Motion Model ◽  
Velocity Pulse ◽  
Near Fault ◽  
Instantaneous Power ◽  
Ground Motion Model ◽  
Short Time

ABSTRACT The velocity pulse in near-fault ground motions has been used as a key characteristic of damaging ground motions. Characterization of the velocity pulse involves three parameters: presence of the pulse, period of the pulse, and amplitude of the pulse. The basic concept behind the velocity pulse is that a large amount of seismic energy is packed into a short time, leading to larger demands on the structure. An intensity measure for near-fault ground motions, which is a direct measure of the amount of energy arriving in short time, called instantaneous power (IP (T1)), is defined as the maximum power of the bandpass-filtered velocity time series measured over a time interval of 0.5T1, in which T1 is the fundamental period of the structure. The records are bandpass filtered in the period band (0.2T1−3T1) to remove the frequencies that are not expected to excite the structure. Zengin and Abrahamson (2020) showed that the drift is better correlated with the IP (T1) than with the velocity pulse parameters for records scaled to the same spectral acceleration at T1. A conditional ground-motion model (GMM) for the IP is developed based on the 5%-damped spectral acceleration at T1, the earthquake magnitude, and the rupture distance. This conditional GMM can be used for record selection for near-fault ground motions that captures the key features of velocity pulses and can lead to a better representation of the median and variability of the maximum interstory drift. The conditional GMM can also be used in a vector hazard analysis for spectral acceleration (T1) and IP (T1) that can be used for more accurate estimation of drift hazard and seismic risk.

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