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).

A horizontal ground-motion model for crustal and subduction earthquakes in Taiwan

2020 ◽  
Vol 36 (2) ◽  
pp. 463-506 ◽  
Author(s):  
Shu-Hsien Chao ◽  
Brian Chiou ◽  
Chiao-Chu Hsu ◽  
Po-Shen Lin
Keyword(s):  
Ground Motion ◽  
Hazard Analysis ◽  
Response Spectrum ◽  
Likelihood Method ◽  
Biased Sampling ◽  
Motion Model ◽  
Subduction Earthquakes ◽  
Ground Motion Model ◽  
Single Station ◽  
Horizontal Ground Motion

In this study, a new horizontal ground-motion model is developed for crustal and subduction earthquakes in Taiwan. A novel two-step maximum-likelihood method is used as a regression tool to develop this model. This method simultaneously considers both the correlation between records and the biased sampling because of random truncation. Moreover, additional ground-motion data can be considered to derive more reliable analysis results. The functional form of the proposed ground-motion model is constructed using the response spectrum of the reference ground-motion scenario and different scalings of the source, path, and site to illustrate the ground-motion characteristics. The variabilities in the ground-motion intensity that result from different events, stations, and records are developed individually to derive a single-station sigma. The proposed ground-motion model may be useful for predicting ground-motion intensity and performing site-specific probabilistic seismic hazard analysis in Taiwan.

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.

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.

On the Nature of Logic Trees in Probabilistic Seismic Hazard Assessment

2012 ◽  
Vol 28 (3) ◽  
pp. 1291-1296 ◽  
Author(s):  
Roger Musson
Keyword(s):  
Seismic Hazard Assessment ◽  
Seismic Source ◽  
Source Model ◽  
Probabilistic Seismic Hazard Assessment ◽  
Motion Model ◽  
Logic Tree ◽  
Ground Motion Model ◽  
Seismic Source Model ◽  
Kolmogorov Axioms ◽  
Better Than

An objection sometimes made against treating the weights of logic tree branches as probabilities relates to the Kolmogorov axioms, but these are only an obstacle if one believes that logic tree branches represent a seismic source model or ground motion model as being “true.” Models are never true, but some models are better than others. It is argued here that a logic tree weight represents the probability that the model in question is better than the others considered. Only one branch can be the best one, and one branch must be the best one. It is also argued that there are situations in PSHA where uncertainty exists but the analyst lacks the means to express it. Therefore it is not necessarily the case that more information increases uncertainty; it may be that more information increases the possibility of expressing uncertainty that was previously unmanageable.

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
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