STRONG MOTION PREDICTION CONSIDERING BOTH NON-LINEAR BEHAVIOR OF SOIL AND THE STATISTICAL CHARACTERISTICS OF GROUND MOTION EXTRACTED FROM OBSERVED DATA

1994 ◽  
Vol 59 (463) ◽  
pp. 27-37
Author(s):  
Toshimi SATOH ◽  
Hiroshi KAWASE ◽  
Toshiaki SATO
Keyword(s):  
Ground Motion ◽  
Strong Motion ◽  
Statistical Characteristics ◽  
Motion Prediction ◽  
Linear Behavior ◽  
Non Linear

Ground-motion model for subduction earthquakes in northern South America

2021 ◽  
pp. 875529302110275
Author(s):  
Carlos A Arteta ◽  
Cesar A Pajaro ◽  
Vicente Mercado ◽  
Julián Montejo ◽  
Mónica Arcila ◽  
...  
Keyword(s):  
South America ◽  
Ground Motion ◽  
Ground Motions ◽  
Strong Motion ◽  
Motion Prediction ◽  
Depth Range ◽  
Ground Motion Prediction ◽  
Natural Period ◽  
Nazca Plate ◽  
Northern South America

Subduction ground motions in northern South America are about a factor of 2 smaller than the ground motions for similar events in other regions. Nevertheless, historical and recent large-interface and intermediate-depth slab earthquakes of moment magnitudes Mw = 7.8 (Ecuador, 2016) and 7.2 (Colombia, 2012) evidenced the vast potential damage that vulnerable populations close to earthquake epicenters could experience. This article proposes a new empirical ground-motion prediction model for subduction events in northern South America, a regionalization of the global AG2020 ground-motion prediction equations. An updated ground-motion database curated by the Colombian Geological Survey is employed. It comprises recordings from earthquakes associated with the subduction of the Nazca plate gathered by the National Strong Motion Network in Colombia and by the Institute of Geophysics at Escuela Politécnica Nacional in Ecuador. The regional terms of our model are estimated with 539 records from 60 subduction events in Colombia and Ecuador with epicenters in the range of −0.6° to 7.6°N and 75.5° to 79.6°W, with Mw≥4.5, hypocentral depth range of 4 ≤  Zhypo ≤ 210 km, for distances up to 350 km. The model includes forearc and backarc terms to account for larger attenuation at backarc sites for slab events and site categorization based on natural period. The proposed model corrects the median AG2020 global model to better account for the larger attenuation of local ground motions and includes a partially non-ergodic variance model.

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.

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.

Exploring the dimensionality of ground motion data

2020 ◽  
Author(s):  
Reza Dokht Dolatabadi Esfahani ◽  
Kristin Vogel ◽  
Fabrice Cotton ◽  
Matthias Ohrnberger ◽  
Frank Scherbaum ◽  
...  
Keyword(s):  
Ground Motion ◽  
Predictive Power ◽  
Epistemic Uncertainty ◽  
Dimensional Space ◽  
Amplitude Spectrum ◽  
Strong Motion ◽  
Motion Prediction ◽  
Dimensional Manifold ◽  
Ground Motion Prediction ◽  
Motion Data

<p>For years, engineering seismologists aim to reduce the epistemic uncertainty related to ground motion prediction. Assuming that simple models with few variables are not sufficient to describe the complex phenomena, there is a trend in present-day science to increase complexity of ground motion models. Therefore, some of the most recent ground motion prediction equations use more than 20 variables to improve the predictive power of the model. However, the legitimate question to ask is whether the inclusion of additional variables leads to an improved predictive power of the model. In other words, what is the smallest number of predictive variables needed to reconstruct the distribution of ground motion induced shaking observed in data? In this study, by taking advantage of the exponential growth of ground motion data and new machine learning methods, we present a data-driven approach to derive the dimensionality of ground motion data in the Fourier amplitude spectrum (FAS) metric. We apply an autoencoder architecture, which is commonly used for mapping high dimensional data to a lower dimensional space (bottleneck) and search for the lowest dimensionality (minimum number of nodes in the bottleneck) required to reconstruct the FAS input data. The approach is tested on synthetic ground motion data with known dimensionality (2D and 4D) and finally applied to the FAS of recorded ground motion data. A simple autoencoder with variable nodes in the bottleneck is used to explore the dimensionality of the ground motion data. We use the relation between the total residual of the network with the number of codes in the bottleneck as an indicator of dimensionality. Its numerical value is estimated based on the reduction of residuals by increasing the number of codes in the bottleneck layer. In addition, we use the low dimensional manifold of the ground motion data to predict the ground motion shaking for a given scenario. The residual analyses between observed and reconstructed data and observed and predicted data are used to validate the training and prediction steps. We applied the method on different scenarios in two synthetic data sets which are simulated by a stochastic simulation method and secondly the Pan-European engineering strong motion data (EMS) to show the performance of the proposed method. The results show that the statistical properties of ground motion data can be captured by using a limited number of three to five parameters. Especially for low frequency data the most dominant features are already captured by two parameters (codes), which roughly correspond to magnitude and distance. For higher frequencies additional parameters, e.g. corresponding to stress drop and kappa, become more relevant. The standard deviation of the residuals can be reduced to its lower bound in comparison with the standard deviations of conventional methods. Finally, we use a two-dimensional manifold to predict the FAS for given magnitude and distance values.</p>

scholarly journals Ground motion prediction equations derived from the Italian strong motion database

2011 ◽  
Vol 9 (6) ◽  
pp. 1899-1920 ◽  
Author(s):  
D. Bindi ◽  
F. Pacor ◽  
L. Luzi ◽  
R. Puglia ◽  
M. Massa ◽  
...  
Keyword(s):  
Ground Motion ◽  
Strong Motion ◽  
Motion Prediction ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Strong Motion Database

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.

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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