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

Lavrentiadis, Grigorios; Abrahamson, Norman A.; Kuehn, Nicolas

doi:10.21203/rs.3.rs-287658/v1

Cited by 6 publications

(8 citation statements)

References 36 publications

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“…Calculating the predictive distribution p(δc * |D) from samples of the posterior distribution requires calculating and inverting the covariance matrix K for each sample of the posterior distribution, which can be computationally expensive. If one considers only uncertainty in the latent variables δc, and assumes that this uncertainty is Gaussian, then one can calculate the predictive distribution as (Lavrentiadis et al, 2021)…”

Section: Calculating Predictionsmentioning

confidence: 99%

Comparison of Bayesian Varying Coefficient Models for the Development of Nonergodic Ground-Motion Models

Kuehn¹

2021

Preprint

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A nonrgodic ground-motion model explicitly takes systematic local source, path and site effects on the predicted ground-motion into account. With an increasing number of ground-motion records, it s possible to estimate these effects. Landwehr et al. (2016) proposed a varying coefficient model as a tool to estimate nonergodic ground-motion models, based on Gaussian processes. Gaussian processes are computationally expen- sive, so for large data sets, some approximations have to be made. Here, we compare different Bayesian implementations of varying coefficient models, using the probabilistic programming language Stan (Carpenter et al., 2017), and the integrated nested Laplace approximation (INLA) (Rue et al., 2009). The models are used to fit nonergodic models on the California subset of the NGA-West2 data set (Ancheta et al., 2014). We find that both implementations lead to very similar results, both in the estimated parameters and the predicted ground motions.

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Section: Calculating Predictionsmentioning

confidence: 99%

Comparison of Bayesian Varying Coefficient Models for the Development of Nonergodic Ground-Motion Models

Kuehn¹

2021

Preprint

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“…For the models that do not include event and site terms, the total standard deviation σ is plotted. In general, there is a reduction in the values of φ SS , φ S2S , and τ due to the inclusion of spatial terms, which is expected, and has been observed before (Kuehn et al, 2019;Lavrentiadis et al, 2021;Lin et al, 2011). For the MS-GWR model, the total variability is about σ = 0.3, which is larger than for the other spatial models.…”

Section: Model Comparison Based On Predictive Measuresmentioning

confidence: 55%

“…Nonergodic GMMs typically provide a method to calculate within-model uncertainty due to the spatial effects (e.g. Caramenti et al, 2020;Landwehr et al, 2016;Lavrentiadis et al, 2021), which can be incorporated into a nonergodic PSHA (Abrahamson et al, 2019). However, between-model uncertainty is generally not Both GWR and GP based SVCM methods have their strengths and weaknesses.…”

Section: Discussionmentioning

confidence: 99%

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A Comparison of Nonergodic Ground-Motion Models based on Geographically Weighted Regression and the Integrated Nested Laplace Approximation

Kuehn¹

2021

Preprint

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Different nonergodic Ground-Motion Models based on spatially varying coefficient models are compared for ground-motion data in Italy. The models are based different methodologies: Multi-source geographically weighted regression (Caramenti et al., 2020), and Bayesian hierarchical models estimated with the integrated nested Laplace approximation (Rue et al., 2009). The different models are compared in terms of their predictive performance, their spatial coefficients, and their predictions. Models that include spatial terms perform slightly better than a simple base model that includes only event and station terms, in terms of out-of sample error based on cross-validation. The Bayesian spatial models have slightly lower generalization error, which can be attributed to the fact that they can include random effects for events and stations. The different methodologies give rise to different dependencies of the spatially varying terms on event and station locations, leading to between-model uncertainty in their predictions, which should be accommodated in a nonergodic seismic hazard assessment.

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“…The epistemic uncertainty of , can be accounted in predicting * by using the marginal distribution of * . Based on these assumptions, the mean prediction and the epistemic uncertainty associated with non-ergodic coefficient predictions for new locations is given by (Lavrentiadis et al 2021…”

Section: Source and Site Terms For The Vcmmentioning

confidence: 99%

A Non-Ergodic Ground-Motion Model of Fourier Amplitude Spectra for France

Sung

Abrahamson

Kuehn

et al. 2021

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We used an ergodic ground-motion model (GMM) of California of Bayless and Abrahamson (Bull Seismol Soc Am 109(5):2088–2105, 2019) as a backbone model and incorporated the varying-coefficient model (VCM), with a modification for anisotropic path effects, to develop a new non-ergodic GMM for France based on the French RESIF data set (1996-2016). Most of the earthquakes in this database have small-to-moderate magnitudes (M2.0 – M5.2). We developed the GMM for the smoothed effective amplitude spectrum (EAS) rather than for elastic spectral acceleration because it allows the use of small magnitude data to constrain linear effects of the path and site without the complication of the scaling being affected by differences in the response spectral shape. For the VCM, the coefficients of GMM can vary by geographical location and they are estimated using Gaussian-process regression. There is a separate set of coefficients for each source and site coordinate, including both the mean coefficients and the epistemic uncertainty in the coefficients. We further modify the anelastic attenuation term of a GMM by the cell-specific approach of Kuehn et al. (Bull Seismol Soc Am 109 (2): 575–585, 2019) to allow for azimuth-dependent attenuation for each source which reduces the standard deviation of the residuals at long distances. As an example, we compute the 5Hz seismic hazard for two sites using the non-ergodic EAS GMM. At the 1 10-4 annual frequency of exceedance hazard level, there can be a large difference between the ergodic hazard and the non-ergodic hazard if the site is close to the available data. The combination of the non-ergodic median ground motion and the reduced aleatory variability can have large implications for seismic-hazard estimation for long return periods. For some sites, the estimated hazard will increase and for other sites the estimated hazard will decrease compared to the traditional ergodic GMM approach. Due to the skewed distribution of the epistemic uncertainty of the hazard, more of the sites will see a decrease in the mean hazard mean hazard at the 1 10-4 hazard level than will see an increase as a result of using the non-ergodic GMM.

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

Cited by 6 publications

References 36 publications

Comparison of Bayesian Varying Coefficient Models for the Development of Nonergodic Ground-Motion Models

Comparison of Bayesian Varying Coefficient Models for the Development of Nonergodic Ground-Motion Models

A Comparison of Nonergodic Ground-Motion Models based on Geographically Weighted Regression and the Integrated Nested Laplace Approximation

A Non-Ergodic Ground-Motion Model of Fourier Amplitude Spectra for France

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