Erratum to Ground‐Motion Prediction Equation for the Chilean Subduction Zone

2017 ◽  
Vol 107 (5) ◽  
pp. 2541-2541 ◽  
Author(s):  
Gonzalo A. Montalva ◽  
Nicolás Bastías ◽  
Adrian Rodriguez‐Marek
Keyword(s):  
Subduction Zone ◽  
Ground Motion ◽  
Prediction Equation ◽  
Motion Prediction ◽  
Ground Motion Prediction Equation ◽  
Ground Motion Prediction

Ground‐Motion Prediction Equation for the Chilean Subduction Zone

2017 ◽  
Vol 107 (2) ◽  
pp. 901-911 ◽  
Author(s):  
Gonzalo A. Montalva ◽  
Nicolás Bastías ◽  
Adrian Rodriguez‐Marek
Keyword(s):  
Subduction Zone ◽  
Ground Motion ◽  
Prediction Equation ◽  
Motion Prediction ◽  
Ground Motion Prediction Equation ◽  
Ground Motion Prediction

A Ground-Motion Prediction Equation for the Western and the Southwestern Parts of China Based on Local Strong-Motion Records and an Overseas Reference Model for the Vertical Component

2021 ◽  
Author(s):  
Hao Xing ◽  
John X. Zhao
Keyword(s):  
Ground Motion ◽  
Earthquake Engineering ◽  
Reference Model ◽  
Prediction Equation ◽  
Motion Prediction ◽  
Ground Motion Prediction Equation ◽  
Ground Motion Prediction ◽  
Magnitude Scaling ◽  
The Absolute ◽  
Standard Deviations

ABSTRACT A ground-motion prediction equation for the vertical ground motions from the western and the southwestern parts of China (referred to as SWC) is presented in this study. Based on the Xing and Zhao (2021) study, the Zhao et al. (2017) model (referred to as ZHAO2017) for the shallow crustal earthquakes in Japan was used as the reference model. We used a bilinear magnitude-scaling function hinged at a moment magnitude (Mw) of 7.1. The magnitude-scaling rate for events with Mw>7.1 was determined by records from the SWC dataset and the large events in the Pacific Earthquake Engineering Research Center Next Generation Attenuation-West2 dataset. Site classes (SCs) were used as the site response proxy. All other parameters were derived from the SWC dataset only. The magnitude-scaling rates for events with Mw≤7.1 in this study are larger than in the ZHAO2017 model at most periods. The absolute values of the geometric attenuation rates are larger, and the absolute values of the anelastic attenuation rates are smaller than in the ZHAO2017 model. The between-event standard deviations are smaller than in the ZHAO2017 model at short periods, and the within-event standard deviations are larger than in the ZHAO2017 model at all periods. The differences in the between-site standard deviations vary significantly from one SC to another. We also find that the between-event and within-event residuals are almost independent of magnitude and source distance. The response spectrum attenuates less rapidly than in the ZHAO2017 model at distances less than 30 km.

scholarly journals A New Approach to Estimate a Mixed Model–Based Ground Motion Prediction Equation

2007 ◽  
Vol 23 (3) ◽  
pp. 665-684 ◽  
Author(s):  
Behrooz Tavakoli ◽  
Shahram Pezeshk
Keyword(s):  
Ground Motion ◽  
Mixed Model ◽  
Prediction Equation ◽  
Regression Coefficients ◽  
Motion Prediction ◽  
Ground Motion Prediction Equation ◽  
Predictive Equation ◽  
Ground Motion Prediction ◽  
Data Set ◽  
Motion Data

A derivative-free approach based on a hybrid genetic algorithm (HGA) is proposed to estimate a mixed model–based ground motion prediction equation (attenuation relationship) with several variance components. First, a simplex search algorithm (SSA) is used to reduce the search domain to improve the convergence speed. Then, a genetic algorithm (GA) is employed to obtain the regression coefficients and the uncertainties of a predictive equation in a unified framework using one-stage maximum-likelihood estimation. The proposed HGA results in a predictive equation that best fits a given ground motion data set. The proposed HGA is able to handle changes in the functional form of the equation. To demonstrate the solution quality of the proposed HGA, the regression coefficients and the uncertainties of a test function based on a simulated ground motion data set are obtained. Then, the proposed HGA is applied to fit two functional attenuation forms to an actual data set of ground motion. For illustration, the results of the HGA are compared with those used by previous conventional methods. The results indicate that the HGA is an appropriate algorithm to overcome the shortcomings of the previous methods and to provide reliable and stable solutions.

Implementing horizontal-to-vertical Fourier spectral ratios and spatial correlation in a ground-motion prediction equation to predict site effects

2020 ◽  
pp. 875529302095244
Author(s):  
Shu-Hsien Chao ◽  
Che-Min Lin ◽  
Chun-Hsiang Kuo ◽  
Jyun-Yan Huang ◽  
Kuo-Liang Wen ◽  
...  
Keyword(s):  
Spatial Correlation ◽  
Ground Motion ◽  
Prediction Accuracy ◽  
Site Effects ◽  
Strong Motion ◽  
Prediction Equation ◽  
Motion Prediction ◽  
Ground Motion Prediction Equation ◽  
Ground Motion Prediction ◽  
Spectral Ratios

We propose a methodology to implement horizontal-to-vertical Fourier spectral ratios (HVRs) evaluated from strong ground motion induced by earthquake (EHVRs) or ambient ground motion observed from microtremor (MHVRs) individually and simultaneously with the spatial correlation (SC) in a ground-motion prediction equation (GMPE) to improve the prediction accuracy of site effects. We illustrated the methodology by developing an EHVRs-SC-based model which supplements Vs30 and Z1.0 with the SC and EHVRs collected at strong motion stations, and a MHVRs-SC-based model that supplements Vs30 and Z1.0 with the SC and MHVRs observed from microtremors at sites which were collocated with strong motion stations. The standard deviation of the station-specific residuals can be reduced by up to 90% when the proposed models are used to predict site effects. In the proposed models, the spatial distribution of the predicted station terms for peak ground acceleration (PGA) from MHVRs at 3699 sites is consistent with that of the predicted station terms for PGA from EHVRs at 721 strong motion stations. Prediction accuracy for stations with inferred Vs30 is similar to that of stations with measured Vs30 with the proposed models. This study provides a methodology to simultaneously implement SC and EHVRs, or SC and MHVRs in a GMPE to improve the prediction accuracy of site effects for a target site with available EHVRs or MHVRs information.

Ground motion prediction equation for Taiwan subduction zone earthquakes

2020 ◽  
Vol 36 (3) ◽  
pp. 1331-1358 ◽  
Author(s):  
Van-Bang Phung ◽  
Chin Hsiung Loh ◽  
Shu Hsien Chao ◽  
Norman A Abrahamson
Keyword(s):  
Shear Wave ◽  
Ground Motion ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Prediction Equation ◽  
Motion Prediction ◽  
Ground Motion Prediction Equation ◽  
Ground Motion Prediction ◽  
Magnitude Scaling ◽  
Site Term

A ground motion prediction equation (GMPE) is presented for computing the median and standard deviation of peak ground acceleration (PGA) and 5% damped pseudo-spectral acceleration (PSA) for periods between 0.01 s and 5.0 s for probabilistic seismic hazard analysis (PSHA) and engineering applications in Taiwan. An integrated strong motion dataset consisting of two subduction earthquake regions was selected from 3314 recordings from Taiwan with M4.5 to M7.1 and 3376 recordings from Japan with M6.5 to M9.1. This dataset was then used to validate, and refit where necessary, the function form provided by Abrahamson et al. for application to Taiwan subduction earthquakes. The proposed model accounts for the extrapolation behaviors associated with the large-magnitude scaling and the near-source scaling terms, both of which were developed empirically by using the combined Taiwan–Japan dataset. The distance attenuation and site term were developed specifically for the Taiwan region. The site term is based on two parameters; the time-averaged shear wave velocity of the top 30 m depth ( VS30) and the depth-to-the-shear wave velocity horizon of 1.0 km/s ( Z1.0).

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