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>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.

Equivalent Point-Source Ground-Motion Model for Subduction Earthquakes in Japan

2021 ◽  
Author(s):  
Behzad Hassani ◽  
Gail Marie Atkinson
Keyword(s):  
Point Source ◽  
Ground Motion ◽  
High Frequency ◽  
Source Model ◽  
Motion Model ◽  
Subduction Earthquakes ◽  
Ground Motion Model ◽  
Equivalent Point ◽  
Back Arc ◽  
Model Approach

ABSTRACT We use an equivalent point-source ground-motion model (GMM) to characterize subduction earthquakes (interface and in-slab) in Japan. The model, which is calibrated using the newly published Next Generation Attenuation (NGA) Subduction database (Bozorgnia et al., 2020), provides a useful complement to the more traditional empirical NGA models developed from the same database. The utility of the point-source model approach lies in its ability to aid in the interpretation of observed trends in the data and to guide modifications to the GMM for application to other regions having fewer data. Key trends in the data that are parameterized with the model include: (a) the enrichment of high-frequency amplitudes for in-slab versus interface events, as modeled by a depth-dependent stress parameter, and (b) attenuation attributes that vary with event type and region, including consideration of fore-arc versus back-arc settings. The developed GMMs of this study are applicable for M 4.5–9.2 for interface events, and M 4–8.5 for in-slab earthquakes, for rupture distances (Drup) from 10 to 600 km, and for 100  m/s<VS30<1500  m/s (time-averaged shear-wave velocity in the top 30 m).

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 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 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.

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.

A ground motion model for volcanic areas in Italy

2019 ◽  
Vol 18 (1) ◽  
pp. 57-76 ◽  
Author(s):  
Giovanni Lanzano ◽  
Lucia Luzi
Keyword(s):  
Ground Motion ◽  
Motion Model ◽  
Ground Motion Model
Sign in / Sign up

Export Citation Format

Share Document