scholarly journals Comparison of Soil Nonlinearity (In SituStress–Strain Relation and G/Gmax Reduction) Observed in Strong‐Motion Databases and Modeled in Ground‐Motion Prediction Equations

2018 ◽  
Vol 109 (1) ◽  
pp. 178-186 ◽  
Author(s):  
Philippe Guéguen ◽  
Luis Fabian Bonilla ◽  
John Douglas
Keyword(s):  
Ground Motion ◽  
Strong Motion ◽  
Motion Prediction ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Soil Nonlinearity ◽  
Strain Relation

scholarly journals Ground motion prediction equations derived from the Italian strong motion database

2011 ◽  
Vol 9 (6) ◽  
pp. 1899-1920 ◽  
Author(s):  
D. Bindi ◽  
F. Pacor ◽  
L. Luzi ◽  
R. Puglia ◽  
M. Massa ◽  
...  
Keyword(s):  
Ground Motion ◽  
Strong Motion ◽  
Motion Prediction ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Strong Motion Database

Improvement of the Quantification of Epistemic Uncertainty Using Single‐Station Ground‐Motion Prediction Equations

2019 ◽  
Vol 109 (4) ◽  
pp. 1358-1377
Author(s):  
Chih‐Hsuan Sung ◽  
Chyi‐Tyi Lee
Keyword(s):  
Ground Motion ◽  
Epistemic Uncertainty ◽  
Hazard Analysis ◽  
Strong Motion ◽  
Motion Prediction ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Ground Acceleration ◽  
Single Station

Abstract The results of probabilistic seismic hazard analysis (PSHA) are sensitive to the standard deviation of the residuals of the ground‐motion prediction equations (GMPEs), especially for long‐return periods. Recent studies have proven that the epistemic uncertainty should be incorporated into PSHA using a logic‐tree method instead of mixing it with the aleatory variability. In this study, we propose using single‐station GMPEs with a novel approach (an epistemic‐residual diagram) to improve the quantification of epistemic uncertainty per station. The single‐station attenuation model is established from the observational recordings of a single station, hence, site‐to‐site variability (σS) can be ignored. We use 20,006 records of 497 crustal earthquakes with moment magnitudes (Mw) greater than 4.0, obtained from the Taiwan Strong Motion Instrumentation Program network, to build the single‐station GMPEs for 570 stations showing the peak ground acceleration (PGA) and spectral accelerations. A comparison is made between the total sigma of the regional GMPE (σT), the single‐station sigma of the regional GMPE as estimated by the variance decomposition method (σSS), and the sigma of single‐station GMPEs (σSS,S), for different periods. For most stations (70%), the σSS,S is about 20%–50% smaller than the σT. Furthermore, we adopt the epistemic‐residual diagram to separate the σSS,S into the epistemic uncertainty (σEP,S) and the remaining unexplained variability (σSP,S) for each station. The results show that in most areas, the σSP,S for the PGA is about 50%–80% smaller than the σT. Finally, the variations in the various sigma and model coefficients are mapped with the geographical locations of the stations for analysis of different regional characteristics.

scholarly journals Ground Motion Prediction Equations for Malaysia Due to Subduction Zone Earthquakes in Sumatran Region

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 23920-23937
Author(s):  
M. S. Liew ◽  
Kamaluddeen Usman Danyaro ◽  
Mazlina Mohamad ◽  
Lim Eu Shawn ◽  
Aziz Aulov
Keyword(s):  
Subduction Zone ◽  
Ground Motion ◽  
Motion Prediction ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Subduction Zone Earthquakes

scholarly journals Ground motions in urban Los Angeles from the 2019 Ridgecrest earthquake sequence

2021 ◽  
pp. 875529302110039
Author(s):  
Filippos Filippitzis ◽  
Monica D Kohler ◽  
Thomas H Heaton ◽  
Robert W Graves ◽  
Robert W Clayton ◽  
...  
Keyword(s):  
Los Angeles ◽  
Ground Motion ◽  
Ground Motions ◽  
Earthquake Sequence ◽  
Motion Prediction ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Velocity Models ◽  
Spectral Accelerations

We study ground-motion response in urban Los Angeles during the two largest events (M7.1 and M6.4) of the 2019 Ridgecrest earthquake sequence using recordings from multiple regional seismic networks as well as a subset of 350 stations from the much denser Community Seismic Network. In the first part of our study, we examine the observed response spectral (pseudo) accelerations for a selection of periods of engineering significance (1, 3, 6, and 8 s). Significant ground-motion amplification is present and reproducible between the two events. For the longer periods, coherent spectral acceleration patterns are visible throughout the Los Angeles Basin, while for the shorter periods, the motions are less spatially coherent. However, coherence is still observable at smaller length scales due to the high spatial density of the measurements. Examining possible correlations of the computed response spectral accelerations with basement depth and Vs30, we find the correlations to be stronger for the longer periods. In the second part of the study, we test the performance of two state-of-the-art methods for estimating ground motions for the largest event of the Ridgecrest earthquake sequence, namely three-dimensional (3D) finite-difference simulations and ground motion prediction equations. For the simulations, we are interested in the performance of the two Southern California Earthquake Center 3D community velocity models (CVM-S and CVM-H). For the ground motion prediction equations, we consider four of the 2014 Next Generation Attenuation-West2 Project equations. For some cases, the methods match the observations reasonably well; however, neither approach is able to reproduce the specific locations of the maximum response spectral accelerations or match the details of the observed amplification patterns.

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