scholarly journals Compression of ground-motion data

10.2172/59428 ◽  
1981 ◽  
Author(s):  
J.W. Long
Keyword(s):  
Ground Motion ◽  
Motion Data

Characteristics of strong ground motion data recorded in the Gubbio sedimentary basin (Central Italy)

2006 ◽  
Vol 5 (1) ◽  
pp. 27-43 ◽  
Author(s):  
F. Pacor ◽  
D. Bindi ◽  
L. Luzi ◽  
S. Parolai ◽  
S. Marzorati ◽  
...  
Keyword(s):  
Ground Motion ◽  
Strong Ground Motion ◽  
Sedimentary Basin ◽  
Central Italy ◽  
Motion Data ◽  
Strong Ground

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.

Ground-Motion Prediction Model Based on Neural Networks to Extract Site Properties from Observational Records

2021 ◽  
Author(s):  
Tomohisa Okazaki ◽  
Nobuyuki Morikawa ◽  
Asako Iwaki ◽  
Hiroyuki Fujiwara ◽  
Tomoharu Iwata ◽  
...  
Keyword(s):  
Ground Motion ◽  
Ground Acceleration ◽  
Site Specific ◽  
Motion Data ◽  
Motion Models ◽  
Proposed Model ◽  
Single Station ◽  
Input Variables ◽  
Ground Condition ◽  
Ground Motion Models

ABSTRACT Choosing the method for inputting site conditions is critical in reducing the uncertainty of empirical ground-motion models (GMMs). We apply a neural network (NN) to construct a GMM of peak ground acceleration that extracts site properties from ground-motion data instead of referring to ground condition variables given for each site. A key structure of the model is one-hot representations of the site ID, that is, specifying the collection site of each ground-motion record by preparing input variables corresponding to all observation sites. This representation makes the best use of the flexibility of NN to obtain site-specific properties while avoiding overfitting at sites where a small number of strong motions have been recorded. The proposed model exhibits accurate and robust estimations among several compared models in different aspects, including data-poor sites and strong motions from large earthquakes. This model is expected to derive a single-station sigma that evaluates the residual uncertainty under the specification of estimation sites. The proposed NN structure of one-hot representations would serve as a standard ingredient for constructing site-specific GMMs in general regions.

NGA-Subduction research program

2021 ◽  
pp. 875529302110560
Author(s):  
Yousef Bozorgnia ◽  
Norman A Abrahamson ◽  
Sean K Ahdi ◽  
Timothy D Ancheta ◽  
Linda Al Atik ◽  
...  
Keyword(s):  
Ground Motion ◽  
Research Program ◽  
Epistemic Uncertainty ◽  
Specific Model ◽  
Peak Ground Velocity ◽  
Ground Acceleration ◽  
Motion Data ◽  
Motion Models ◽  
Aleatory Variability ◽  
Adjustment Factors

This article summarizes the Next Generation Attenuation (NGA) Subduction (NGA-Sub) project, a major research program to develop a database and ground motion models (GMMs) for subduction regions. A comprehensive database of subduction earthquakes recorded worldwide was developed. The database includes a total of 214,020 individual records from 1,880 subduction events, which is by far the largest database of all the NGA programs. As part of the NGA-Sub program, four GMMs were developed. Three of them are global subduction GMMs with adjustment factors for up to seven worldwide regions: Alaska, Cascadia, Central America and Mexico, Japan, New Zealand, South America, and Taiwan. The fourth GMM is a new Japan-specific model. The GMMs provide median predictions, and the associated aleatory variability, of RotD50 horizontal components of peak ground acceleration, peak ground velocity, and 5%-damped pseudo-spectral acceleration (PSA) at oscillator periods ranging from 0.01 to 10 s. Three GMMs also quantified “within-model” epistemic uncertainty of the median prediction, which is important in regions with sparse ground motion data, such as Cascadia. In addition, a damping scaling model was developed to scale the predicted 5%-damped PSA of horizontal components to other damping ratios ranging from 0.5% to 30%. The NGA-Sub flatfile, which was used for the development of the NGA-Sub GMMs, and the NGA-Sub GMMs coded on various software platforms, have been posted for public use.

Verification of attenuation models based on strong ground motion data in Northern Thailand

2020 ◽  
Vol 133 ◽  
pp. 106145
Author(s):  
Weeradetch Tanapalungkorn ◽  
Lindung Zalbuin Mase ◽  
Panon Latcharote ◽  
Suched Likitlersuang
Keyword(s):  
Ground Motion ◽  
Strong Ground Motion ◽  
Northern Thailand ◽  
Motion Data ◽  
Attenuation Models ◽  
Strong Ground

Analysis and Application on Measured Ground Motion Data from Blasting

2018 ◽  
Author(s):  
SONG Shiqian ◽  
CUI Yunxiao ◽  
WANG Wanpeng ◽  
GUO Xian ◽  
HUANG Xiaofei
Keyword(s):  
Ground Motion ◽  
Motion Data

Empirical Linear Seismic Site Amplification in Central and Eastern North America

2019 ◽  
Vol 35 (2) ◽  
pp. 849-881 ◽  
Author(s):  
Grace A. Parker ◽  
Jonathan P. Stewart ◽  
Youssef M. A. Hashash ◽  
Ellen M. Rathje ◽  
Kenneth W. Campbell ◽  
...  
Keyword(s):  
North America ◽  
Ground Motion ◽  
Site Response ◽  
Site Amplification ◽  
Eastern North America ◽  
Resonance Effects ◽  
Bayesian Techniques ◽  
Motion Data ◽  
Motion Models ◽  
Scaling Models

We present empirical linear site amplification models conditioned on time-averaged shear wave velocity in the upper 30 m ( VS30) for central and eastern North America. The models are derived from ground motion data and site condition information from the NGA-East project and are intended for use with reference rock ground motion models. Site amplification is found to scale with VS30 for intermediate to stiff site conditions ( VS30 > 300 m/s) in a weaker manner than for active tectonic regions such as the western United States. For stiff sites ( >800 m/s), we find differences in site amplification for previously glaciated and nonglaciated regions, with nonglaciated sites having lower amplification. The models were developed using a combination of least-squares, mixed effects, and Bayesian techniques; the latter show that accounting for predictor uncertainty does not appreciably affect the median model but decreases model dispersion. Our VS30-scaling models are modular and additive to simulation-based models for the nonlinear components of site response. A limitation of the present models is that they do not account for site-specific resonance effects.

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