Probabilistic Seismic Hazard Analysis in California Using Nonergodic Ground Motion Models

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

Seismic cumulative failure effects on a reservoir bank slope with a complex geological structure considering plastic deformation characteristics using shaking table tests

The selection of ground motion models, and the representation of their epistemic uncertainty in the form of a logic tree, is one of the fundamental components of probabilistic seismic hazard and risk analysis. A new ground motion model (GMM) logic tree has been developed for the 2020 European seismic hazard model, which develops upon recently compiled ground motion data sets in Europe. In contrast to previous European seismic hazard models, the new ground model logic tree is built around the scaled backbone concept. Epistemic uncertainties are represented as calibrations to a reference model and aim to characterise the potential distributions of median ground motions resulting from variability in source scaling and attenuation. These scaled backbone logic trees are developed and presented for shallow crustal seismic sources in Europe. Using the new European strong motion flatfile, and capitalising on recent perspectives in ground motion modelling in the scientific literature, a general and transferable procedure is presented for the construction of a backbone model and the regionalisation of epistemic uncertainty. This innovative approach forms a general framework for revising and updating the GMM logic tree at national and European scale as new strong motion data emerge in the future.

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A regionally-adaptable “scaled backbone” ground motion logic tree for shallow seismicity in Europe: application to the 2020 European seismic hazard model

New developments for the performance-based assessment of seismically-induced slope displacements

\n A computationally efficient methodology for propagating the epistemic uncertainty in the median ground motion in probabilistic seismic hazard analysis is developed using the polynomial chaos (PC) approach. For this application, the epistemic uncertainty in the median ground motion for a specific scenario is assumed to be lognormally distributed and fully correlated across earthquake scenarios. In the hazard calculation, a single central ground‐motion model (GMM) is used for the median along with the epistemic standard error of the median for each scenario. A set of PC coefficients is computed for each scenario and each test ground‐motion level. The additional computation burden in computing these PC coefficients depends on the order of the approximation but is less than computing the median ground motion from one additional GMM. With the PC method, the mean and fractiles of the hazard due to the epistemic uncertainty distribution of the median ground motion are computed as a postprocess that is very fast computationally. For typical values of the standard deviation of epistemic uncertainty in the median ground motion (<0.2 natural log units), the methodology accurately estimates the epistemic uncertainty distribution of the hazard over the 1%–99% range. This full epistemic range is not well modeled with just a small number of GMM branches uses in the traditional logic‐tree approach. The PC method provides more accuracy, faster computation, and reduced memory requirements than the traditional approach. For large values of the epistemic uncertainty in the median ground motion, a higher order of the PC expansion may be needed to be included to capture the full range of the epistemic uncertainty.

Efficient Propagation of Epistemic Uncertainty in the Median Ground-Motion Model in Probabilistic Hazard Calculations

This paper provides an overview and introduction to the development of non-ergodic ground motion models, GMMs. It is intended for a reader who is familiar with the standard approach for developing ergodic GMMs. It starts with a brief summary of the development of ergodic GMMs and then describes different methods that are used in the development of non-ergodic GMMs with an emphasis on Gaussian Process (GP) regression, as that is currently the method preferred by most researchers contributing in this special issue. Non-ergodic modeling requires the definition of locations for the source and site characterizing the systematic source and site effects; the non-ergodic domain is divided in cells for describing the systematic path effects. Modeling the cell-specific anelastic attenuation as a GP, and considerations on constraints for extrapolation of the non-ergodic GMMs are also discussed. An updated unifying notation for non-ergodic GMMs is also presented, which has been adopted by the authors of this issue.

Maxime Lacour

Papers

New developments for the performance-based assessment of seismically-induced slope displacements

Efficient Propagation of Epistemic Uncertainty in the Median Ground-Motion Model in Probabilistic Hazard Calculations

Overview and Introduction to Development of Non-Ergodic Earthquake Ground-Motion Models

Stochastic finite element method for non-linear material models

Dynamic stochastic finite element method using time‐dependent generalized polynomial chaos