The SDSU Broadband Ground-Motion Generation Module BBtoolbox Version 1.5

2014 ◽  
Vol 86 (1) ◽  
pp. 81-88 ◽  
Author(s):  
K. Olsen ◽  
R. Takedatsu
Keyword(s):  
Ground Motion ◽  
Motion Generation ◽  
Broadband Ground Motion

Broadband Ground‐Motion Simulation with Interfrequency Correlations

2019 ◽  
Vol 109 (6) ◽  
pp. 2437-2446
Author(s):  
Nan Wang ◽  
Rumi Takedatsu ◽  
Kim B. Olsen ◽  
Steven M. Day
Keyword(s):  
Time Series ◽  
Ground Motion ◽  
Hazard Analysis ◽  
Low Frequency ◽  
Stochastic Methods ◽  
Motion Generation ◽  
Simulation Techniques ◽  
Motion Time ◽  
San Diego State University ◽  
Broadband Ground Motion

Abstract Ground‐motion simulations can be viable alternatives to empirical relations for seismic hazard analysis when data are sparse. Interfrequency correlation is revealed in recorded seismic data, which has implications for seismic risk (Bayless and Abrahamson, 2018a). However, in many cases, simulated ground‐motion time series, in particular those originating from stochastic methods, lack interfrequency correlation. Here, we develop a postprocessing method to rectify simulation techniques that otherwise produce synthetic time histories deficient in an interfrequency correlation structure. An empirical correlation matrix is used in our approach to generate correlated random variables that are multiplied in the frequency domain with the Fourier amplitudes of the synthetic ground‐motion time series. The method is tested using the San Diego State University broadband ground‐motion generation module, which is a broadband ground‐motion generator that combines deterministic low‐frequency and stochastic high‐frequency signals, validated for the median of the spectral acceleration. Using our method, the results for seven western U.S. earthquakes with magnitude between 5.0 and 7.2 show that empirical interfrequency correlations are well simulated for a large number of realizations without biasing the fit of the median of the spectral accelerations to data. The best fit of the interfrequency correlation to data is obtained assuming that the horizontal components are correlated with a correlation coefficient of about 0.7.

Validation of the Recipe for Broadband Ground‐Motion Simulations of Japanese Crustal Earthquakes

2016 ◽  
Vol 106 (5) ◽  
pp. 2214-2232 ◽  
Author(s):  
Asako Iwaki ◽  
Takahiro Maeda ◽  
Nobuyuki Morikawa ◽  
Hiroe Miyake ◽  
Hiroyuki Fujiwara
Keyword(s):  
Ground Motion ◽  
Crustal Earthquakes ◽  
Ground Motion Simulations ◽  
Broadband Ground Motion

scholarly journals A Multi Record Based Artificial Near Fault Ground Motion Generation Method

MethodsX ◽  
2020 ◽  
Vol 7 ◽  
pp. 100725
Author(s):  
Zhen Liu ◽  
Shibo Zhang ◽  
Zhe Zhang
Keyword(s):  
Ground Motion ◽  
Motion Generation ◽  
Near Fault ◽  
Near Fault Ground Motion

scholarly journals Hybrid broadband ground motion simulation validation of small magnitude earthquakes in Canterbury, New Zealand

2020 ◽  
Vol 36 (2) ◽  
pp. 673-699 ◽  
Author(s):  
Robin L Lee ◽  
Brendon A Bradley ◽  
Peter J Stafford ◽  
Robert W Graves ◽  
Adrian Rodriguez-Marek
Keyword(s):  
New Zealand ◽  
Ground Motion ◽  
High Frequency ◽  
Motion Simulation ◽  
Small Magnitude ◽  
Ground Motion Simulation ◽  
Simulation Methodology ◽  
Simulation Validation ◽  
Broadband Ground Motion Simulation ◽  
Broadband Ground Motion

Ground motion simulation validation is an important and necessary task toward establishing the efficacy of physics-based ground motion simulations for seismic hazard analysis and earthquake engineering applications. This article presents a comprehensive validation of the commonly used Graves and Pitarka hybrid broadband ground motion simulation methodology with a recently developed three-dimensional (3D) Canterbury Velocity Model. This is done through simulation of 148 small magnitude earthquake events in the Canterbury, New Zealand, region in order to supplement prior validation efforts directed at several larger magnitude events. Recent empirical ground motion models are also considered to benchmark the simulation predictive capability, which is examined by partitioning the prediction residuals into the various components of ground motion variability. Biases identified in source, path, and site components suggest that improvements to the predictive capabilities of the simulation methodology can be made by using a longer high-frequency path duration model, reducing empirical V s30-based low-frequency site amplification, and utilizing site-specific velocity models in the high-frequency simulations.

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