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.

A subset of CyberShake ground-motion time series for response-history analysis

2021 ◽  
pp. 875529302098197
Author(s):  
Jack W Baker ◽  
Sanaz Rezaeian ◽  
Christine A Goulet ◽  
Nicolas Luco ◽  
Ganyu Teng
Keyword(s):  
Time Series ◽  
Los Angeles ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Ground Motions ◽  
Response Spectra ◽  
Motion Time ◽  
History Analysis ◽  
Response History Analysis ◽  
Simulated Ground Motions

This manuscript describes a subset of CyberShake numerically simulated ground motions that were selected and vetted for use in engineering response-history analyses. Ground motions were selected that have seismological properties and response spectra representative of conditions in the Los Angeles area, based on disaggregation of seismic hazard. Ground motions were selected from millions of available time series and were reviewed to confirm their suitability for response-history analysis. The processes used to select the time series, the characteristics of the resulting data, and the provided documentation are described in this article. The resulting data and documentation are available electronically.

Conditioned Simulation of Ground-Motion Time Series at Uninstrumented Sites Using Gaussian Process Regression

2021 ◽  
Author(s):  
Aidin Tamhidi ◽  
Nicolas Kuehn ◽  
S. Farid Ghahari ◽  
Arthur J. Rodgers ◽  
Monica D. Kohler ◽  
...  
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Ground Motion ◽  
San Francisco ◽  
Earthquake Engineering ◽  
Seismic Analysis ◽  
Ground Motions ◽  
Free Field ◽  
Gaussian Process Regression ◽  
Motion Time

ABSTRACT Ground-motion time series are essential input data in seismic analysis and performance assessment of the built environment. Because instruments to record free-field ground motions are generally sparse, methods are needed to estimate motions at locations with no available ground-motion recording instrumentation. In this study, given a set of observed motions, ground-motion time series at target sites are constructed using a Gaussian process regression (GPR) approach, which treats the real and imaginary parts of the Fourier spectrum as random Gaussian variables. Model training, verification, and applicability studies are carried out using the physics-based simulated ground motions of the 1906 Mw 7.9 San Francisco earthquake and Mw 7.0 Hayward fault scenario earthquake in northern California. The method’s performance is further evaluated using the 2019 Mw 7.1 Ridgecrest earthquake ground motions recorded by the Community Seismic Network stations located in southern California. These evaluations indicate that the trained GPR model is able to adequately estimate the ground-motion time series for frequency ranges that are pertinent for most earthquake engineering applications. The trained GPR model exhibits proper performance in predicting the long-period content of the ground motions as well as directivity pulses.

Disaggregation of seismic hazard

1999 ◽  
Vol 89 (2) ◽  
pp. 501-520 ◽  
Author(s):  
Paolo Bazzurro ◽  
C. Allin Cornell
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Hazard Analysis ◽  
Seismic Source ◽  
Source Effects ◽  
Motion Time ◽  
Time Histories ◽  
Definition Of ◽  
Rupture Mechanism ◽  
Selection Of

Abstract Probabilistic seismic hazard analysis (PSHA) integrates over all potential earthquake occurrences and ground motions to estimate the mean frequency of exceedance of any given spectral acceleration at the site. For improved communication and insights, it is becoming common practice to display the relative contributions to that hazard from the range of values of magnitude, M, distance, R, and epsilon, ɛ, the number of standard deviations from the median ground motion as predicted by an attenuation equation. The proposed disaggregation procedures, while conceptually similar, differ in several important points that are often not reported by the researchers and not appreciated by the users. We discuss here such issues, for example, definition of the probability distribution to be disaggregated, different disaggregation techniques, disaggregation of R versus ln R, and the effects of different binning strategies on the results. Misconception of these details may lead to unintended interpretations of the relative contributions to hazard. Finally, we propose to improve the disaggregation process by displaying hazard contributions in terms of not R, but latitude, longitude, as well as M and ɛ. This permits a display directly on a typical map of the faults of the surrounding area and hence enables one to identify hazard-dominating scenario events and to associate them with one or more specific faults, rather than a given distance. This information makes it possible to account for other seismic source characteristics, such as rupture mechanism and near-source effects, during selection of scenario-based ground-motion time histories for structural analysis.

Selection of ground motion time series and limits on scaling

2006 ◽  
Vol 26 (5) ◽  
pp. 477-482 ◽  
Author(s):  
Jennie Watson-Lamprey ◽  
Norman Abrahamson
Keyword(s):  
Time Series ◽  
Ground Motion ◽  
Motion Time ◽  
Selection Of

scholarly journals Conditioned Simulation of Ground Motion Time Series using Gaussian Process Regression

2020 ◽  
Author(s):  
Aidin Tamhidi ◽  
Nicolas Kuehn ◽  
S. Farid Ghahari ◽  
Ertugrul Taciroglu ◽  
Yousef Bozorgnia
Keyword(s):  
Time Series ◽  
Motion Estimation ◽  
Regression Model ◽  
Gaussian Process ◽  
Ground Motion ◽  
Ground Motions ◽  
Free Field ◽  
Gaussian Process Regression ◽  
Estimation Methods ◽  
Motion Time

Ground motion time series are critical elements of earthquake engineering for performance analysis of seismic regions’ built environment. At present, the number of available instruments to record the free-field ground motions in the US is generally sparse. Therefore, ground motion estimation methods are used to obtain input motion estimates at locations where there is no available instrumentation. In this study, the ground motion time series are constructed using a Gaussian Process regression, which models the Fourier spectrum’s real and imaginary parts as random Gaussian variables. The proposed model’s training and validation are carried out using the physics-based simulated ground motions of the 1906 San Francisco Earthquake. The evaluation of the model’s performance is also carried out using the simulated magnitude 7.0 Hayward fault earthquake and the ground motions recorded in the 2019 magnitude 7.1 Ridgecrest Earthquake sequence within the Los Angeles area. All evaluations imply that the trained Gaussian Process regression model can estimate the ground motion time series properly. It is also observed that the trained Gaussian Process regression model has decent performance on the long-period ground motion estimation due to the ground motion directivity pulses. The results also illustrate that the stations’ prediction either at the boundary edges or outside of the network might not be as accurate as other stations’ estimations.

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