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

2021 ◽  
Author(s):  
Nicolas Kuehn
Keyword(s):  
Ground Motion ◽  
Gaussian Processes ◽  
Large Data ◽  
Local Source ◽  
Varying Coefficient Models ◽  
Data Set ◽  
Varying Coefficient ◽  
Motion Models ◽  
Ground Motion Model ◽  
Ground Motion Models

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.

Relational database used for ground-motion model development in the NGA-sub project

2021 ◽  
pp. 875529302110552
Author(s):  
Silvia Mazzoni ◽  
Tadahiro Kishida ◽  
Jonathan P Stewart ◽  
Victor Contreras ◽  
Robert B Darragh ◽  
...  
Keyword(s):  
Subduction Zone ◽  
Ground Motion ◽  
Data Storage ◽  
Relational Database ◽  
Model Development ◽  
Intensity Measures ◽  
Motion Models ◽  
Ground Motion Model ◽  
Ground Motion Models ◽  
Pseudo Spectral

The Next-Generation Attenuation for subduction zone regions project (NGA-Sub) has developed data resources and ground motion models for global subduction zone regions. Here we describe the NGA-Sub database. To optimize the efficiency of data storage, access, and updating, data resources for the NGA-Sub project are organized into a relational database consisting of 20 tables containing data, metadata, and computed quantities (e.g. intensity measures, distances). A database schema relates fields in tables to each other through a series of primary and foreign keys. Model developers and other users mostly interact with the data through a flatfile generated as a time-stamped output of the database. We describe the structure of the relational database, the ground motions compiled for the project, and the means by which the data can be accessed. The database contains 71,340 three-component records from 1880 earthquakes from seven global subduction zone regions: Alaska, Central America and Mexico, Cascadia, Japan, New Zealand, South America, and Taiwan. These data were processed on a component-specific basis to minimize noise effects in the data and remove baseline drifts. Provided ground motion intensity measures include peak acceleration, peak velocity, and 5%-damped pseudo-spectral accelerations for a range of oscillator periods.

scholarly journals Seismic hazard evaluation

2000 ◽  
Vol 33 (3) ◽  
pp. 371-386 ◽  
Author(s):  
Paul Somerville
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Ground Motions ◽  
Strong Motion ◽  
Earthquake Source ◽  
Hazard Evaluation ◽  
Data Set ◽  
Motion Models ◽  
The Past ◽  
Ground Motion Models

This paper reviews concepts and trends in seismic hazard characterization that have emerged in the past decade, and identifies trends and concepts that are anticipated during the coming decade. New methods have been developed for characterizing potential earthquake sources that use geological and geodetic data in conjunction with historical seismicity data. Scaling relationships among earthquake source parameters have been developed to provide a more detailed representation of the earthquake source for ground motion prediction. Improved empirical ground motion models have been derived from a strong motion data set that has grown markedly over the past decade. However, these empirical models have a large degree of uncertainty because the magnitude - distance - soil category parameterization of these models often oversimplifies reality. This reflects the fact that other conditions that are known to have an important influence on strong ground motions, such as near- fault rupture directivity effects, crustal waveguide effects, and basin response effects, are not treated as parameters of these simple models. Numerical ground motion models based on seismological theory that include these additional effects have been developed and extensively validated against recorded ground motions, and used to estimate the ground motions of past earthquakes and predict the ground motions of future scenario earthquakes. The probabilistic approach to characterizing the ground motion that a given site will experience in the future is very compatible with current trends in earthquake engineering and the development of building codes. Performance based design requires a more comprehensive representation of ground motions than has conventionally been used. Ground motions estimates are needed at multiple annual probability levels, and may need to be specified not only by response spectra but also by suites of strong motion time histories for input into time-domain non-linear analyses of structures.

Ranking of Ground-Motion Models (GMMs) for Use in Probabilistic Seismic Hazard Analysis for Iran Based on an Independent Data Set

2020 ◽  
Author(s):  
Zoya Farajpour ◽  
Milad Kowsari ◽  
Shahram Pezeshk ◽  
Benedikt Halldorsson
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Selection Process ◽  
Hazard Analysis ◽  
Probabilistic Seismic Hazard Analysis ◽  
Seismic Hazard Analysis ◽  
Probabilistic Seismic Hazard ◽  
Data Set ◽  
Motion Models ◽  
Ground Motion Models

ABSTRACT We apply three data-driven selection methods, log-likelihood (LLH), Euclidean distance-based ranking (EDR), and deviance information criterion (DIC), to objectively evaluate the predictive capability of 10 ground-motion models (GMMs) developed from Iranian and worldwide data sets against a new and independent Iranian strong-motion data set. The data set includes, for example, the 12 November 2017 Mw 7.3 Ezgaleh earthquake and the 25 November 2018 Mw 6.3 Sarpol-e Zahab earthquake and includes a total of 201 records from 29 recent events with moment magnitudes 4.5≤Mw≤7.3 with distances up to 275 km. The results of this study show that the prior sigma of the GMMs acts as the key measure used by the LLH and EDR methods in the ranking against the data set. In some cases, this leads to the resulting model bias being ignored. In contrast, the DIC method is free from such ambiguity as it uses the posterior sigma as the basis for the ranking. Thus, the DIC method offers a clear advantage of partially removing the ergodic assumption from the GMM selection process and allows a more objective representation of the expected ground motion at a specific site when the ground-motion recordings are homogeneously distributed in terms of magnitudes and distances. The ranking results thus show that the local models that were exclusively developed from Iranian strong motions perform better than GMMs from other regions for use in probabilistic seismic hazard analysis in Iran. Among the Next Generation Attenuation-West2 models, the GMMs by Boore et al. (2014) and Abrahamson et al. (2014) perform better. The GMMs proposed by Darzi et al. (2019) and Farajpour et al. (2019) fit the recorded data well at short periods (peak ground acceleration and pseudoacceleration spectra at T=0.2  s). However, at long periods, the models developed by Zafarani et al. (2018), Sedaghati and Pezeshk (2017), and Kale et al. (2015) are preferable.

Correlation of Spectral Acceleration Values from NGA Ground Motion Models

2008 ◽  
Vol 24 (1) ◽  
pp. 299-317 ◽  
Author(s):  
Jack W. Baker ◽  
Nirmal Jayaram
Keyword(s):  
Ground Motion ◽  
Hazard Analysis ◽  
Single Equation ◽  
Spectral Acceleration ◽  
Ground Motion Selection ◽  
Motion Models ◽  
Attenuation Relationships ◽  
Ground Motion Model ◽  
Analysis Application ◽  
Ground Motion Models

Ground motion models (or “attenuation relationships”) describe the probability distribution of spectral acceleration at an individual period, given a set of predictor variables such as magnitude and distance, but they do not address the correlations between spectral acceleration values at multiple periods or orientations. Those correlations are needed for several calculations related to seismic hazard analysis and ground motion selection. Four NGA models and the NGA ground motion database are used here to measure these correlations, and predictive equations are fit to the results. The equations are valid for periods from 0.01 seconds to 10 seconds, versus similar previous equations that were valid only between 0.05 and 5 seconds and produced unreasonable results if extrapolated. Use of the new NGA ground motion database also facilitates a first study of correlations from intra- and inter-event residuals. Observed correlations are not sensitive to the choice of accompanying ground motion model, and intra-event, inter-event, and total residuals all exhibit similar correlation structure. A single equation is thus applicable for a variety of correlation predictions. A simple example illustrates the use of the proposed equations for one hazard analysis application.

scholarly journals Estimating Ground-Motion Models via Bayesian Inference using Stan

2021 ◽  
Author(s):  
Nicolas Kuehn ◽  
Peter Stafford
Keyword(s):  
Bayesian Inference ◽  
Ground Motion ◽  
Programming Language ◽  
Motion Model ◽  
Probabilistic Programming ◽  
Motion Models ◽  
Ground Motion Model ◽  
Computer Environment ◽  
Ground Motion Models

We provide a simple introduction to the estimation of ground-motion models via Bayesian inference and the probabilistic programming language Stan.We show one ca implement a simple ground-motion model in Stan, and how can run the program from the computer environment R.We also show how one can access the results, and plot summaries of estimated parameters.A large number of different Stan models for the development https://github.com/pstafford/StanGMMTutorial.

Evaluation of Ground‐Motion Models for U.S. Geological Survey Seismic Hazard Models: 2018 Anchorage, Alaska, Mw 7.1 Subduction Zone Earthquake Sequence

2019 ◽  
Vol 91 (1) ◽  
pp. 183-194 ◽  
Author(s):  
Daniel E. McNamara ◽  
Emily Wolin ◽  
Peter M. Powers ◽  
Alison M. Shumway ◽  
Morgan P. Moschetti ◽  
...  
Keyword(s):  
Seismic Hazard ◽  
Subduction Zone ◽  
Ground Motion ◽  
Hazard Model ◽  
Earthquake Sequence ◽  
Geological Survey ◽  
Hazard Models ◽  
Data Set ◽  
Motion Models ◽  
Ground Motion Models

Abstract Instrumental ground‐motion recordings from the 2018 Anchorage, Alaska (Mw 7.1), earthquake sequence provide an independent data set allowing us to evaluate the predictive power of ground‐motion models (GMMs) for intraslab earthquakes associated with the Alaska subduction zone. In this study, we evaluate 15 candidate GMMs using instrumental ground‐motion observations of peak ground acceleration and 5% damped pseudospectral acceleration (0.02–10 s) to inform logic‐tree weights for the update of the U.S. Geological Survey seismic hazard model for Alaska. GMMs are evaluated using two methods. The first is a total residual visualization approach that compares the probability density function, mean, and standard deviations σ of the observed and predicted ground motion. The second GMM evaluation method we use is the common total residual probabilistic scoring method (log likelihood [LLH]). The LLH method provides a single score that can be used to weight GMMs in the Alaska seismic hazard model logic trees. To test logic branches in previous seismic hazard models, we evaluate GMM performance as a function of depth and we demonstrate that some GMMs show improved performance for earthquakes with focal depths greater than 50 km. Ten of the initial 15 candidate GMMs fit the observed ground motions and meet established criteria for inclusion in the next update of the Alaska seismic hazard model.

A Suite of Alternative Ground-Motion Models (GMMs) for Israel

2021 ◽  
Author(s):  
Soumya Kanti Maiti ◽  
Gony Yagoda-Biran ◽  
Ronnie Kamai
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Empirical Data ◽  
Epistemic Uncertainty ◽  
Ground Motions ◽  
Plate Boundary ◽  
Strong Motion ◽  
Engineering Applications ◽  
Motion Models ◽  
Ground Motion Models

ABSTRACT Models for estimating earthquake ground motions are a key component in seismic hazard analysis. In data-rich regions, these models are mostly empirical, relying on the ever-increasing ground-motion databases. However, in areas in which strong-motion data are scarce, other approaches for ground-motion estimates are sought, including, but not limited to, the use of simulations to replace empirical data. In Israel, despite a clear seismic hazard posed by the active plate boundary on its eastern border, the instrumental record is sparse and poor, leading to the use of global models for hazard estimation in the building code and all other engineering applications. In this study, we develop a suite of alternative ground-motion models for Israel, based on an empirical database from Israel as well as on four data-calibrated synthetic databases. Two host models are used to constrain model behavior, such that the epistemic uncertainty is captured and characterized. Despite the lack of empirical data at large magnitudes and short distances, constraints based on the host models or on the physical grounds provided by simulations ensure these models are appropriate for engineering applications. The models presented herein are cast in terms of the Fourier amplitude spectra, which is a linear, physical representation of ground motions. The models are suitable for shallow crustal earthquakes; they include an estimate of the median and the aleatory variability, and are applicable in the magnitude range of 3–8 and distance range of 1–300 km.

Testing non-linear amplification factors used in ground motion models

2021 ◽  
Author(s):  
Karina Loviknes ◽  
Danijel Schorlemmer ◽  
Fabrice Cotton ◽  
Sreeram Reddy Kotha
Keyword(s):  
Ground Motion ◽  
Site Effects ◽  
Ground Motions ◽  
Site Amplification ◽  
Building Codes ◽  
Linear Amplification ◽  
Motion Models ◽  
Earthquake Predictability ◽  
Non Linear ◽  
Ground Motion Models

<p>Non-linear site effects are mainly expected for strong ground motions and sites with soft soils and more recent ground-motion models (GMM) have started to include such effects. Observations in this range are, however, sparse, and most non-linear site amplification models are therefore partly or fully based on numerical simulations. We develop a framework for testing of non-linear site amplification models using data from the comprehensive Kiban-Kyoshin network in Japan. The test is reproducible, following the vision of the Collaboratory for the Study of Earthquake Predictability (CSEP), and takes advantage of new large datasets to evaluate <span>whether or not</span> non-linear site effects predicted by site-amplification models are supported by empirical data. The site amplification models are tested using residuals between the observations and predictions from a GMM based only on magnitude and distance. When the GMM is derived without any site term, the site-specific variability extracted from the residuals is expected to capture the site response of a site. The non-linear site amplification models are tested against a linear amplification model on individual well-record<span>ing</span> stations. Finally, the result is compared to building codes where non-linearity is included. The test shows that for most of the sites selected as having sufficient records, the non-linear site-amplification models do not score better than the linear amplification model. This suggests that including non-linear site amplification in GMMs and building codes may not yet be justified, at least not in the range of ground motions considered in the test (peak ground acceleration < 0.2 g).</p>

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