scholarly journals Ground‐Motion Prediction Models for Arias Intensity and Cumulative Absolute Velocity for Japanese Earthquakes Considering Single‐Station Sigma and Within‐Event Spatial Correlation

2015 ◽  
Vol 105 (4) ◽  
pp. 1903-1918 ◽  
Author(s):  
Roxane Foulser‐Piggott ◽  
Katsuichiro Goda
Keyword(s):  
Spatial Correlation ◽  
Ground Motion ◽  
Prediction Models ◽  
Absolute Velocity ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Arias Intensity ◽  
Cumulative Absolute Velocity ◽  
Single Station

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.

A suite of ground motion prediction equations for cumulative absolute velocity in shallow crustal earthquakes including epistemic uncertainty

2020 ◽  
pp. 875529302095734
Author(s):  
Zach Bullock ◽  
Abbie B Liel ◽  
Shideh Dashti ◽  
Keith A. Porter
Keyword(s):  
Ground Motion ◽  
Epistemic Uncertainty ◽  
Absolute Velocity ◽  
Motion Prediction ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Crustal Earthquakes ◽  
Cumulative Absolute Velocity ◽  
Earthquake Characteristics

Recent research has highlighted the usefulness of cumulative absolute velocity [Formula: see text] in several contexts, including using the [Formula: see text] at the ground surface for earthquake early warning and using the [Formula: see text] at rock reference conditions for evaluation of the liquefaction risk facing structures. However, there are relatively few ground motion prediction equations for CAV, they are based on relatively small data sets, and they give relatively similar results. This study develops nine ground motion prediction equations for [Formula: see text] based on a global database of ground motion records from shallow crustal earthquakes. Its provision of nine models enables characterization of epistemic uncertainty for ranges of earthquake characteristics that are sparsely populated in the regression database. The functional forms provide different perspectives on extrapolation to important ranges of earthquake characteristics, particularly large magnitude events and short distances. The variability and epistemic uncertainty in the models are characterized. Spatial autocorrelation of the models’ errors is investigated. The models’ predictions agree with existing broadly applicable models at small to moderate magnitudes and moderate to long distances. These models can be used to improve hazard analysis of [Formula: see text] that incorporates the influence of epistemic uncertainty.

scholarly journals Ground motion models for Arias intensity, cumulative absolute velocity, peak incremental ground velocity, and significant duration in New Zealand

2019 ◽  
Vol 52 (4) ◽  
pp. 193-207 ◽  
Author(s):  
Zach Bullock
Keyword(s):  
New Zealand ◽  
Ground Motion ◽  
Absolute Velocity ◽  
Taupo Volcanic Zone ◽  
Arias Intensity ◽  
Intensity Measures ◽  
Motion Models ◽  
Cumulative Absolute Velocity ◽  
Functional Forms ◽  
Ground Motion Models

This study proposes empirical ground motion models for a variety of non-spectral intensity measures and significant durations in New Zealand. Equations are presented for the prediction of the median and maximum rotated components of Arias intensity, cumulative absolute velocity, cumulative absolute velocity above a 5 cm/s2 acceleration threshold, peak incremental ground velocity, and the 5% to 75% and 5% to 95% significant durations. Recent research has highlighted the usefulness of these parameters in both structural and geotechnical engineering. The New Zealand Strong Motion Database provides the database for regression and includes many earthquakes from all regions of New Zealand with the exceptions of Auckland and Northland, Otago and Southland, and Taranaki. The functional forms for the proposed models are selected using cross validation. The possible influence of effects not typically included in ground motion models for these intensity measures is considered, such as hanging wall effects and basin depth effects, as well as altered attenuation in the Taupo Volcanic Zone. The selected functional forms include magnitude and rupture depth scaling, attenuation with distance, and shallow site effects. Finally, the spatial autocorrelation of the models’ within-event residuals is considered and recommendations are made for developing correlated maps of intensity predictions stochastically.

Non-isotropic and Isotropic Ground Motion Prediction Models

2019 ◽  
Vol 177 (2) ◽  
pp. 801-819
Author(s):  
Saman Yaghmaei-Sabegh ◽  
Mehdi Ebrahimi-Aghabagher
Keyword(s):  
Ground Motion ◽  
Prediction Models ◽  
Motion Prediction ◽  
Ground Motion Prediction

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 models evoked by seismicity in the Upper Silesia Coal Basin, Poland, the review with case studies

2020 ◽  
Vol 224 (2) ◽  
pp. 1381-1403
Author(s):  
Maciej J Mendecki ◽  
Judyta Odrobińska ◽  
Renata Patyńśka ◽  
Adam F Idziak
Keyword(s):  
Ground Motion ◽  
Regression Models ◽  
Prediction Models ◽  
Motion Prediction ◽  
Model Parameters ◽  
Ground Motion Prediction ◽  
Coal Basin ◽  
Upper Silesia ◽  
Regression Parameters ◽  
The Impact

SUMMARY This paper presents the results of new research on ground-motion relations from three areas in the Upper Silesia Coal Basin (USCB) in Poland and compares them with of ground-motion relations. These three mining areas of the USCB were investigated in order to better predict ground motion caused by seismic events. The study focused on variations in regression parameters and predicted PGA (peak ground acceleration) for different areas to better understand the influence of geology. To compare our results to previous models we had to unify the known ground-motion prediction equations (GMPE). Then, we used various regression models to predict the corresponding PGA values of a relatively strong USCB seismic event with an energy level of 108 J (ML = 3.3) and compared their results. The regression model parameters were compared to each other, particularly those related to energy and distance, which corresponds to a geometrical scattering (attenuation) of seismic waves as well as the influence of wave type (body or surface). Finally, building upon several established regression models, our analysis showed a strong linear correlation between two regression parameters corresponding to energy and distance. However, an open question remains whether this relation can be explained by physics, or, from a mathematical point of view, it is the effect of linear dependence of matrix vectors logE and logR. A comparison of different GMPEs allows for better verification of knowledge about the impact of tremors on ground motion in the USCB.

Watts Bar Nuclear Power Plant Strong-Motion Records of the 12 December 2018 M 4.4 Decatur, Tennessee, Earthquake

2020 ◽  
Vol 91 (3) ◽  
pp. 1579-1592 ◽  
Author(s):  
Vladimir Graizer ◽  
Dogan Seber ◽  
Scott Stovall
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Ground Motion ◽  
Nuclear Power ◽  
Prediction Models ◽  
Ground Motions ◽  
Strong Motion ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Digital Recordings

Abstract The moment magnitude M 4.4 on 12 December 2018 Decatur, Tennessee, earthquake occurred in the eastern Tennessee seismic zone. Although the causative fault is not known, the earthquake had a predominantly strike-slip mechanism with an estimated hypocentral depth of about 8 km. It was felt over a distance of 500 km stretching from Southern Kentucky to Georgia. Strong shaking, capable of causing slight damage, was reported near the epicenter. The Watts Bar nuclear power plant (NPP) is only 4.9 km from the epicenter of the earthquake and experienced only slight shaking. The earthquake was recorded by the plant’s seismic strong-motion instrumentation installed at four different locations. Near-real-time calculations by the plant operators indicated that the operating basis earthquake (OBE) ground motion was not exceeded during the earthquake. We obtained and processed the recorded motions to calculate corrected accelerations, velocities, and displacements. In addition, we computed the Fourier and 5% damped response spectra to compare them with the plant’s OBE. Comparisons of the ground-motion prediction models with the digital recordings at the plant site indicated that recorded ground motions were significantly below the predicted results calculated using the ground-motion prediction models approved for regulatory use. Availability of high-quality, digital recordings in this case helped make a quick decision about the ground motions not exceeding the OBE and hence prevented unnecessary shutdown of the NPP. Availability of earthquake recordings from the four locations in the NPP also presented an opportunity to analyze the linear response of plant structures.

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