scholarly journals A Methodological Approach to Update Ground Motion Prediction Models Using Bayesian Inference

2021 ◽  
Author(s):  
Saran Srikanth Bodda ◽  
Merlin Keller ◽  
Abhinav Gupta ◽  
Gloria Senfaute
Keyword(s):  
Bayesian Inference ◽  
Ground Motion ◽  
Prediction Models ◽  
Methodological Approach ◽  
Motion Prediction ◽  
Ground Motion Prediction

scholarly journals A methodological approach to update Ground Motion Prediction Models using Bayesian Inference

2021 ◽  
Author(s):  
Saran Srikanth Bo ◽  
Merlin Keller ◽  
Abhinav Gupta ◽  
Gloria Senfaute
Keyword(s):  
Bayesian Inference ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Methodological Approach ◽  
Strong Motion ◽  
Motion Prediction ◽  
Data Sets ◽  
Ground Motion Prediction ◽  
Small Magnitude ◽  
Moderate Seismicity

Abstract In recent decades, prediction of ground motion at a specific site or a region is of primary interest in probabilistic seismic hazard assessment (PSHA). Historically, several ground motion prediction equation (GMPE) models with different functional forms have been published using strong ground motion records available from NGA-West and European databases. However, low-to-moderate seismicity regions, such as Central & Eastern United States and western Europe, is characterized by limited strong-motion records in the magnitude-distance range of interest for PSHA. In these regions, the available data for the development of empirical GMPEs is very scarce and limited to small magnitude events. For these regions, the general practice in PSHA is to consider a set of GMPEs developed from data sets collected in other regions with high seismicity. This practice generates an overestimation of the seismic hazard for the low seismicity regions. There are two potential solutions to overcome this problem: (i) a new GMPE model can be developed; however, development of such a model can require significant amount of data which is not usually available, and (ii) the existing GMPE models can be recalibrated based on the data sets collected in the new region rather than developing a new GMPE model. In this paper, we propose a methodological approach to recalibrate the coefficients in a GMPE model using different algorithms to perform Bayesian inference. The coefficients are recalibrated for a subset of European Strong-Motion (ESM) database that corresponds to low-to-moderate seismicity records. In this study, different statistical models are compared based on the functional form given by the chosen GMPE, and the best model and algorithm are recommended using the concept of information criteria.

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

Ground-Motion Attenuation Model for Small-To-Moderate Shallow Crustal Earthquakes in California and Its Implications on Regionalization of Ground-Motion Prediction Models

2010 ◽  
Vol 26 (4) ◽  
pp. 907-926 ◽  
Author(s):  
Brian Chiou ◽  
Robert Youngs ◽  
Norman Abrahamson ◽  
Kofi Addo
Keyword(s):  
Ground Motion ◽  
Regional Differences ◽  
Regional Difference ◽  
Prediction Models ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Small Magnitude ◽  
Attenuation Model ◽  
Crustal Earthquakes ◽  
Active Tectonic

This paper presents the development of a ground-motion prediction model for small-to-moderate shallow crustal earthquakes (3M5.5, up to 200 km distance) using data from the California ShakeMap systems. Our goal is to provide an empirical model that can be confidently used in the investigation of ground-motion difference between California and other active tectonic regions (such as the Pacific Northwest and British Columbia, Canada) where the bulk of ground-motion data from shallow crustal earthquakes is in the small-to-moderate magnitude range. This attenuation model is developed as a small-magnitude extension of the Chiou and Youngs NGA model (CY2008). We observe, and incorporate into this model, a regional difference in median amplitude between central and southern California earthquakes. The strength of the regional difference diminishes with increasing spectral period. More importantly, it is magnitude dependent and becomes insignificant for M6 earthquakes, as indicated by the large-magnitude California data used in CY2008. Together, these findings have important implications on the practice of utilizing the regional differences observed in small-to-moderate earthquakes to infer the regional differences expected in large earthquakes, including the NGA model applicability in active tectonic regions outside California.

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