A Comparison of Ground-Motion Parameters for the Vertical Components from the Western and the Southwestern Parts of China with Recent Ground-Motion Prediction Equations

2020 ◽  
Author(s):  
Hao Xing ◽  
John X. Zhao
Keyword(s):  
Ground Motion ◽  
Normal Fault ◽  
Constant Term ◽  
Site Effect ◽  
Prediction Equation ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Vertical Ground Motions ◽  
Standard Deviations ◽  
Suitable Reference

ABSTRACT This study evaluated the source, path, and site effects of the vertical ground motions from the western and the southwestern parts of China (referred to as SWC hereafter) using 2403 records from 449 earthquakes, including the records from the 2008 Mw 7.9 Wenchuan earthquake and its aftershocks. Only 677 records are from 73 mainshocks, and 259 events do not have a known focal mechanism. There is a large magnitude gap in the dataset, for example, there is only one event between Mw 6.3 and Mw 7.8. The average numbers of records per recording station and per earthquake are small, and many sites do not have a measured shear-wave velocity profile. These shortcomings make it difficult to develop a robust ground-motion prediction equation (GMPE) without adding overseas data or using a reference GMPE developed from a large dataset. We compared the SWC dataset with five recent GMPEs, three based on the Next Generation Attenuation-West2 dataset, one based on Europe and the Middle East, and one based on the shallow-crustal and upper-mantle earthquakes in Japan. We decomposed the total residuals for each model into constant term, between-event, and within-event residuals and calculated the corresponding standard deviations. The maximum log likelihood and the standard deviations suggest that, among the five GMPEs, the Zhao et al. (2017) model without the normal-fault term may be the most suitable GMPE for a probabilistic seismic hazard study in the SWC region. Correction functions based on simple magnitude, path, and site effect parameters were used to correct the residuals and to obtain the leftover between- and within-event standard deviations. These standard deviations appear to suggest that the GMPE from Zhao et al. (2017) without a normal-fault term may be the most suitable reference GMPE for developing a new GMPE for the SWC region.

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 Ground-motion prediction equation and site effect characterization for the central area of the Main Syncline, Upper Silesia Coal Basin, Poland

2018 ◽  
Vol 10 (1) ◽  
pp. 474-483 ◽  
Author(s):  
Maciej Jan Mendecki ◽  
Angelika Duda ◽  
Adam Idziak
Keyword(s):  
Ground Motion ◽  
Central Area ◽  
Site Effect ◽  
Prediction Equation ◽  
Motion Prediction ◽  
Ground Motion Prediction Equation ◽  
Ground Motion Prediction ◽  
Coal Basin ◽  
Ground Acceleration ◽  
Upper Silesia

Abstract The aim of the study was to find the best model of ground-motion prediction equation (GMPE) forecasting peak ground acceleration (PGA) caused by induced seismicity. The maximum values of PGA on the surface are a major seismic threat for the infrastructure, especially in the highly urbanized areas, such is the Upper Silesian Metropolitan Area. The forecasting equations were estimated based on the values of PGA, epicenter distances and mining tremor energy registered by 14 surface seismometer stations located in the central area of the Main Syncline of the Upper Silesia Coal Basin. Data were collected within the period from January 2010 to December 2016, and the total number of seismic events used in the calculations was 15 541. The final model predicted the PGA values and amplification coefficients representing the characteristics of the site effects under seismometer stations.

scholarly journals Ground motion prediction model for southeastern México removing site effects using the Earthquake horizontal-to-vertical ratio (EHVSR)

2020 ◽  
Vol 59 (4) ◽  
pp. 257-272
Author(s):  
Javier Lermo-Samaniego
Keyword(s):  
Ground Motion ◽  
Site Effects ◽  
Spectral Ratio ◽  
Site Effect ◽  
Prediction Equation ◽  
Reliable Estimate ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Attenuation Model ◽  
Hypocentral Distance

We propose a ground motion attenuation model (ground motion prediction equation, GMPE) for Southeast Mexico. We suppress site effects obtained from Earthquake Horizontal to Vertical Spectral Ratio (EHVSR) as a reliable estimate of site effects. (The attenuation model was built as a function of magnitude and hypocentral distance)). We used 86 seismic events with 5.0 ? Mw ? 8.2 (earthquake recordings for the 9/7/2017, Mw8.2 Tehuantepec earthquake are included), and distances between 52 ? R ? 618 km. They were recorded in nine stations of the Engineering Institute of the National Autonomous University of Mexico (II-UNAM) accelerometric network installed in the states of Chiapas, Oaxaca, Tabasco and Veracruz. Site effects at each of these stations were estimated by using the average EHVSR. Then, by means of this spectral ratio the site effects were suppressed at each station and for every record. This work points out the need to remove the site effect in the GMPE. The current models overestimate this effect. 

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.

A Comparison of Ground Motion Prediction Equations for Arias Intensity and Cumulative Absolute Velocity Developed Using a Consistent Database and Functional Form

2012 ◽  
Vol 28 (3) ◽  
pp. 931-941 ◽  
Author(s):  
Kenneth W. Campbell ◽  
Yousef Bozorgnia
Keyword(s):  
Ground Motion ◽  
Functional Form ◽  
Strong Motion ◽  
Prediction Equation ◽  
Absolute Velocity ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Arias Intensity ◽  
Intensity Measures ◽  
Cumulative Absolute Velocity

Arias intensity (AI) and cumulative absolute velocity (CAV) have been proposed as instrumental intensity measures that can incorporate the cumulative effects of ground motion duration and intensity on the response of structural and geotechnical systems. In this study, we have developed a ground motion prediction equation (GMPE) for the horizontal component of AI in order to compare its predictability to a similar GMPE for CAV. Both GMPEs were developed using the same strong motion database and functional form in order to eliminate any bias these factors might cause in the comparison. This comparison shows that AI exhibits significantly greater amplitude scaling and aleatory uncertainty than CAV. The smaller standard deviation and less sensitivity to amplitude suggests that CAV is more predictable than AI and should be considered as an alternative to AI in engineering and geotechnical applications where the latter intensity measure is traditionally used.

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