Non-ergodic FAS Ground-Motion Model for France

2020 ◽  
Author(s):  
Chih Hsuan Sung ◽  
Norman Abrahamson ◽  
Nicolas Kuehn ◽  
Paola Traversa ◽  
Irmela Zentner
Keyword(s):  
Standard Deviation ◽  
Ground Motion ◽  
Epistemic Uncertainty ◽  
Geographical Location ◽  
Gaussian Process Regression ◽  
Motion Model ◽  
Data Set ◽  
Small Magnitude ◽  
Ground Motion Model ◽  
Anelastic Attenuation

<p>In this study, we use an ergodic ground motion model (GMM) of California of Bayless and Abrahamson (2019) as a backbone and incorporate the varying-coefficient model (VCM) to develop a new French non-ergodic GMM based on the French RESIF data set (1996-2016). Most of the magnitudes of this database are small (Mw = 2.0 – 5.2), so we adopt the Fourier amplitude spectral GMM rather than the spectral acceleration model, which allows the use of small magnitude data to constrain path and site effects without the complication of the scaling being affected by differences in the response spectral shape. For the VCM, the coefficients of GMPE can vary by geographical location and they are estimated using Gaussian process regression. That is, 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. Moreover, the epistemic uncertainty associated with the predicted ground motions also varies spatially: it is small in locations where there are many events or stations and it is large in sparse data regions. Finally, we modify the anelastic attenuation term of a GMM by the cell-specific approach of Kuehn et al. (2019) to allow for azimuth-dependent attenuation for each source which reduces the standard deviation of residuals at long distances. The results show that combining the above two methods (VCM & cell-specific) to lead an aleatory standard deviation of residuals for the GMM that is reduced by ~ 47%, which can have huge implications for seismic-hazard calculations.</p>

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.

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

2021 ◽  
Author(s):  
Chih-Hsuan Sung ◽  
Norman Abrahamson ◽  
Nicolas M. Kuehn ◽  
Paola Traversa ◽  
Irmela Zentner
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Epistemic Uncertainty ◽  
Skewed Distribution ◽  
Motion Model ◽  
Data Set ◽  
Small Magnitude ◽  
Ground Motion Model ◽  
The Mean ◽  
Hazard Level

Abstract 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.

scholarly journals A Non-ergodic Spectral Acceleration Ground Motion Model for California Developed with Random Vibration Theory

2021 ◽  
Author(s):  
Grigorios Lavrentiadis ◽  
Norman A. Abrahamson
Keyword(s):  
Standard Deviation ◽  
Ground Motion ◽  
Random Vibration ◽  
Epistemic Uncertainty ◽  
Response Spectrum ◽  
Motion Model ◽  
Small Magnitude ◽  
Vibration Theory ◽  
Ground Motion Model ◽  
Random Vibration Theory

Abstract A new approach for creating a non-ergodic PSA ground-motion model (GMM) is presented which account for the magnitude dependence of the non-ergodic effects. In this approach, the average PSA scaling is controlled by an ergodic PSA GMM, and the non-ergodic effects are captured with non-ergodic PSA factors, which are the adjustment that needs to be applied to an ergodic PSA GMM to incorporate the non-ergodic effects. The non-ergodic PSA factors are based on EAS non-ergodic effects and are converted to PSA through Random Vibration Theory (RVT). The advantage of this approach is that it better captures the non-ergodic source, path, and site effects through the small magnitude earthquakes. Due to the linear properties of Fourier Transform, the EAS non-ergodic effects of the small events can be applied directly to the large magnitude events. This is not the case for PSA, as response spectrum is controlled by a range of frequencies, making PSA non-ergodic effects depended on the spectral shape which is magnitude dependent. Two PSA non-ergodic GMMs are derived using the ASK14 (Abrahamson et al., 2014) and CY14 (Chiou and Youngs, 2014) GMMs as backbone models, respectively. The non-ergodic EAS effects are estimated with the LAK21 (Lavrentiadis et al., In press) GMM. The RVT calculations are performed with the V75 (Vanmarcke, 1975) peak factor model, the Da0.05−0.85 estimate of AS96 (Abrahamson and Silva, 1996) for the ground-motion duration, and BT15 (Boore and Thompson, 2015) oscillator-duration model. The California subset of the NGAWest2 database (Ancheta et al., 2014) is used for both models. The total aleatory standard deviation of the two non-ergodic PSA GMMs is approximately 30 to 35% smaller than the total aleatory standard deviation of the corresponding ergodic PSA GMMs. This reduction has a significant impact on hazard calculations at large return periods. In remote areas, far from stations and past events, the reduction of aleatory variability is accompanied by an increase of epistemic uncertainty.

Framework for a Ground-Motion Model for Induced Seismic Hazard and Risk Analysis in the Groningen Gas Field, The Netherlands

2017 ◽  
Vol 33 (2) ◽  
pp. 481-498 ◽  
Author(s):  
Julian J. Bommer ◽  
Peter J. Stafford ◽  
Benjamin Edwards ◽  
Bernard Dost ◽  
Ewoud van Dedem ◽  
...  
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Quantitative Estimation ◽  
Epistemic Uncertainty ◽  
Induced Earthquakes ◽  
Gas Field ◽  
Small Magnitude ◽  
Ground Motion Model ◽  
Hazard And Risk ◽  
Groningen Gas Field

The potential for building damage and personal injury due to induced earthquakes in the Groningen gas field is being modeled in order to inform risk management decisions. To facilitate the quantitative estimation of the induced seismic hazard and risk, a ground motion prediction model has been developed for response spectral accelerations and duration due to these earthquakes that originate within the reservoir at 3 km depth. The model is consistent with the motions recorded from small-magnitude events and captures the epistemic uncertainty associated with extrapolation to larger magnitudes. In order to reflect the conditions in the field, the model first predicts accelerations at a rock horizon some 800 m below the surface and then convolves these motions with frequency-dependent nonlinear amplification factors assigned to zones across the study area. The variability of the ground motions is modeled in all of its constituent parts at the rock and surface levels.

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

2019 ◽  
Vol 109 (5) ◽  
pp. 2063-2072 ◽  
Author(s):  
Maxime Lacour ◽  
Norman A. Abrahamson
Keyword(s):  
Ground Motion ◽  
Traditional Approach ◽  
Epistemic Uncertainty ◽  
Hazard Analysis ◽  
Full Range ◽  
Motion Model ◽  
Uncertainty Distribution ◽  
Earthquake Scenarios ◽  
Ground Motion Model ◽  
Pc Method

Abstract 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.

NGA Ground Motion Model for the Geometric Mean Horizontal Component of PGA, PGV, PGD and 5% Damped Linear Elastic Response Spectra for Periods Ranging from 0.01 to 10 s

2008 ◽  
Vol 24 (1) ◽  
pp. 139-171 ◽  
Author(s):  
Kenneth W. Campbell ◽  
Yousef Bozorgnia
Keyword(s):  
Standard Deviation ◽  
Ground Motion ◽  
Geometric Mean ◽  
Response Spectra ◽  
Horizontal Component ◽  
Elastic Response ◽  
Motion Model ◽  
Linear Elastic ◽  
Ground Motion Model ◽  
Elastic Response Spectra

We present a new empirical ground motion model for PGA, PGV, PGD and 5% damped linear elastic response spectra for periods ranging from 0.01–10 s. The model was developed as part of the PEER Next Generation Attenuation (NGA) project. We used a subset of the PEER NGA database for which we excluded recordings and earthquakes that were believed to be inappropriate for estimating free-field ground motions from shallow earthquake mainshocks in active tectonic regimes. We developed relations for both the median and standard deviation of the geometric mean horizontal component of ground motion that we consider to be valid for magnitudes ranging from 4.0 up to 7.5–8.5 (depending on fault mechanism) and distances ranging from 0–200 km. The model explicitly includes the effects of magnitude saturation, magnitude-dependent attenuation, style of faulting, rupture depth, hanging-wall geometry, linear and nonlinear site response, 3-D basin response, and inter-event and intra-event variability. Soil nonlinearity causes the intra-event standard deviation to depend on the amplitude of PGA on reference rock rather than on magnitude, which leads to a decrease in aleatory uncertainty at high levels of ground shaking for sites located on soil.

A procedure to develop a backbone ground-motion model: A case study for its implementation

2021 ◽  
pp. 875529302110145
Author(s):  
Sinan Akkar ◽  
Özkan Kale ◽  
M Abdullah Sandıkkaya ◽  
Emrah Yenier
Keyword(s):  
Normal Distribution ◽  
Ground Motion ◽  
Epistemic Uncertainty ◽  
Motion Model ◽  
Logic Tree ◽  
Ground Motion Model ◽  
Stable Continental Region ◽  
Trivariate Normal ◽  
Motion Characterization

The backbone modeling in ground-motion characterization (GMC) is a useful methodology to describe the epistemic uncertainty in median ground-motion predictions. The approach uses a backbone ground-motion model (GMM) and populates the GMC logic tree with the scaled and/or adjusted versions of the backbone GMM to capture the epistemic uncertainty in median ground motions. The scaling and/or adjustment should represent the specific features and uncertainties involved in source, path, and site effects at the target site. The identification of the backbone model requires different considerations specific to the nature of the ground-motion hazard problem. In this article, we present a scaled backbone modeling approach that considers the magnitude- and distance-scaling predictors as well as their correlation to address the epistemic uncertainty in median ground-motion predictions. This approach results in a trivariate normal distribution to fully define a range of epistemic uncertainty in a model sample space. The simultaneous consideration of magnitude and distance scaling while defining the epistemic uncertainty and the methodology followed for the simplified representation of trivariate normal distribution in ground-motion logic tree are the two important features in our procedure. We first present the proposed approach that is followed by a case study for Central and Eastern North America (CENA) stable continental region. The case study discusses the underlying assumptions and limitations of the proposed approach.

A Ground-Motion Model for GNSS Peak Ground Displacement

2021 ◽  
Vol 111 (5) ◽  
pp. 2393-2407 ◽  
Author(s):  
Dara E. Goldberg ◽  
Diego Melgar ◽  
Gavin P. Hayes ◽  
Brendan W. Crowell ◽  
Valerie J. Sahakian
Keyword(s):  
Point Source ◽  
Ground Motion ◽  
Point Sources ◽  
Motion Model ◽  
Ground Displacement ◽  
Large Magnitude ◽  
Observation Distance ◽  
Data Set ◽  
Ground Motion Model ◽  
Earthquake Data

ABSTRACT We present an updated ground-motion model (GMM) for Mw 6–9 earthquakes using Global Navigation Satellite Systems (GNSS) observations of the peak ground displacement (PGD). Earthquake GMMs inform a range of Earth science and engineering applications, including source characterization, seismic hazard evaluations, loss estimates, and seismic design standards. A typical GMM is characterized by simplified metrics describing the earthquake source (magnitude), observation distance, and site terms. Most often, GMMs are derived from broadband seismometer and accelerometer observations, yet during strong shaking, these traditional seismic instruments are affected by baseline offsets, leading to inaccurate recordings of low-frequency ground motions such as displacement. The incorporation of geodetic data sources, particularly for characterizing the unsaturated ground displacement of large-magnitude events, has proven valuable as a complement to traditional seismic approaches and led to the development of an initial point-source GMM based on PGD estimated from high-rate GNSS data. Here, we improve the existing GMM to more effectively account for fault finiteness, slip heterogeneity, and observation distance. We evaluate the limitations of the currently available GNSS earthquake data set to calibrate the GMM. In particular, the observed earthquake data set is lacking in observations within 100 km of large-magnitude events (Mw&gt;8), inhibiting evaluation of fault dimensions for earthquakes too large to be represented as point sources in the near field. To that end, we separately consider previously validated synthetic GNSS waveforms within 10–1000 km of Mw 7.8–9.3 Cascadia subduction zone scenario ruptures. The synthetic data highlight the importance of fault distance rather than point-source metrics and improve our preparedness for large-magnitude earthquakes with spatiotemporal qualities unlike those in our existing data set.

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.

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