Manifold aligned ground motion prediction equations for regional datasets

Gianniotis, Nikolaos; Kuehn, Nicolas; Scherbaum, Frank

doi:10.1016/j.cageo.2014.04.014

Cited by 7 publications

(12 citation statements)

References 18 publications

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“…Typically, the aforementioned studies estimate spatial correlations of within-event residuals separately for different events, using a covariance function such as Equations (12) to (15). Thus, for a well-recorded event, the semivariogram is calculated based on the distances between the recording stations.…”

Section: Correlation For Path Effectsmentioning

confidence: 99%

“…Based on the between-event residuals δB calculated in the previous section, we estimate the parameters of the four covariance functions (exponential, squared exponential, spherical, and Matérn [ν=3/2]) of Equations (12), (13), (14), and (15) and calculate the MSE and MSLL for the Taiwanese and ANZA data sets. These are shown in Figure 7.…”

Section: Spatial Correlation Of Source Effectsmentioning

confidence: 99%

“…In particular, the estimation of systematic site effects from repeated measurements at one station is becoming more common in practice, which leads to the application of so-called single station sigma in PSHA. [11][12][13][14] Recently, Landwehr et al 15 proposed a non-ergodic GMM for California where the coefficients vary smoothly by event and station location, but are constrained to be spatially correlated. In this case, a model estimated on a data set covering a large region is adjusted in some of its components to account for regional differences in ground-motion scaling (usually in a scale factor for the distance attenuation).…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, methods have been proposed to simultaneously estimate GMMs for different regions, where the coefficients are constrained to be similar. [11][12][13][14] Recently, Landwehr et al 15 proposed a non-ergodic GMM for California where the coefficients vary smoothly by event and station location, but are constrained to be spatially correlated. Small, localized data sets may be used to estimate the values of non-ergodic variance components.…”

Section: Introductionmentioning

confidence: 99%

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Spatial correlations of ground motion for non‐ergodic seismic hazard analysis

Kuehn

Abrahamson

2019

Earthq Engng Struct Dyn

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Traditional probabilistic seismic hazard analysis (PSHA) uses ground-motion models that are based on the ergodic assumption, which means that the distribution of ground motions over time at a given site is the same as their spatial distribution over different sites. Evaluations of ground-motion data sets with multiple measurements at a given site and multiple earthquakes in a given region have shown that the ergodic assumption is not appropriate as there are strong systematic region-specific source terms and site-specific path and site terms that are spatially correlated. We model these correlations using a spatial Gaussian process model. Different correlations functions are employed, both stationary and nonstationary, and the results are compared in terms of their predictive power. Spatial correlations of residuals are investigated on a Taiwanese strong-motion data set, and ground motions are collected at the ANZA, CA array. Source effects are spatially correlated, but provide a much stronger benefit in terms of prediction for the ANZA data set than for the Taiwanese data set. We find that systematic path effects are best modeled by a non-stationary covariance function that is dependent on source-to-site distance and magnitude. The correlation structure estimated from Californian data can be transferred to Taiwan if one carefully accounts for differences in magnitudes. About 50% of aleatory variance can be explained by accounting for spatial correlation.

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Section: Correlation For Path Effectsmentioning

confidence: 99%

Section: Spatial Correlation Of Source Effectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial correlations of ground motion for non‐ergodic seismic hazard analysis

Kuehn

Abrahamson

2019

Earthq Engng Struct Dyn

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“…The model presented in this work shares similarities with the work of [30,33,37]. We use the same data as [30], but constrain the coefficients for different regions in a different way. The model is cast in the formulation of [33].…”

Section: Introductionmentioning

confidence: 99%

A partially non-ergodic ground-motion prediction equation for Europe and the Middle East

Kuehn

Scherbaum

2016

Bull Earthquake Eng

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A partially non-ergodic ground-motion prediction equation is estimated for Europe and the Middle East. Therefore, a hierarchical model is presented that accounts for regional differences. For this purpose, the scaling of ground-motion intensity measures is assumed to be similar, but not identical in different regions. This is achieved by assuming a hierarchical model, where some coefficients are treated as random variables which are sampled from an underlying global distribution. The coefficients are estimated by Bayesian inference. This allows one to estimate the epistemic uncertainty in the coefficients, and consequently in model predictions, in a rigorous way. The model is estimated based on peak ground acceleration data from nine different European/Middle Eastern regions. There are large differences in the amount of earthquakes and records in the different regions. However, due to the hierarchical nature of the model, regions with only few data points borrow strength from other regions with more data. This makes it possible to estimate a separate set of coefficients for all regions. Different regionalized models are compared, for which different coefficients are assumed to be regionally dependent. Results show that regionalizing the coefficients for magnitude and distance scaling leads to better performance of the models. The models for all regions are physically sound, even if only very few earthquakes comprise one region.

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