Wavelet Packet Characterization of Scenario Earthquake Ground Motions

2017 ◽  
Vol 11 (03) ◽  
pp. 1750006
Author(s):  
Ajin Baby ◽  
Manish Shrikhande
Keyword(s):  
Ground Motion ◽  
Prediction Models ◽  
Response Spectrum ◽  
Wavelet Packet ◽  
Time History ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Scenario Earthquake ◽  
Motion Time ◽  
Time Histories

With increased emphasis on performance-based seismic design, the need for appropriate ground motion time histories for use in nonlinear dynamic analyses is felt accutely. However, it is generally not possible to get a suitable recorded time history consistent with the estimated hazard at a specific site. The ground motion prediction models are therefore derived/developed from a statistical analysis of recorded ground motion for a variety of source and site conditions to address this need. Most often, the ground motion prediction models are developed to model the response spectrum amplitudes at a set of natural periods and the ground motion time history, if required, is then generated to be consistent with this predicted response spectrum. These simulated time histories often lack in modeling the wave arrivals and temporal variation in the distribution of energy with respect to frequency. In this paper, we present a wavelet-based ground motion prediction model for directly generating ground motion time history that is consistent with the postulated scenario earthquake at a site.

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

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.

The Variability of Ground-Motion Prediction Models and Its Components

2010 ◽  
Vol 81 (5) ◽  
pp. 794-801 ◽  
Author(s):  
L. A. Atik ◽  
N. Abrahamson ◽  
J. J. Bommer ◽  
F. Scherbaum ◽  
F. Cotton ◽  
...  
Keyword(s):  
Ground Motion ◽  
Prediction Models ◽  
Motion Prediction ◽  
Ground Motion Prediction
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