Stochastic simulation of regionalized ground motions using wavelet packets and cokriging analysis

Performance-based earthquake engineering often requires ground-motion time-history analyses to be performed, but very often, ground motions are not recorded at the location being analyzed. The present study is among the first attempt to stochastically simulate spatially distributed ground motions over a region using wavelet packets and cokriging analysis. First, we characterize the time and frequency properties of ground motions using the wavelet packet analysis. The spatial cross-correlations of wavelet packet parameters are determined through geostatistical analysis of regionalized ground-motion data from the Northridge and Chi-Chi earthquakes. It is observed that the spatial cross-correlations of wavelet packet parameters are closely related to regional site conditions. Furthermore, using the developed spatial cross-correlation model and the cokriging technique, wavelet packet parameters at unmeasured locations can be best estimated, and regionalized ground-motion time histories can be synthesized. Case studies and blind tests using data from the Northridge and Chi-Chi earthquakes demonstrate that the simulated ground motions generally agree well with the actual recorded data. The proposed method can be used to stochastically simulate regionalized ground motions for time-history analyses of distributed infrastructure and has important applications in regional-scale hazard analysis and loss estimation. Copyright © 2014 John Wiley & Sons, Ltd.

[1]  Thomas D. O'Rourke,et al.  Northridge Earthquake Effects on Pipelines and Residential Buildings , 2005 .

[2]  Aspasia Zerva,et al.  Effects of spatially variable ground motions on the seismic response of a skewed, multi-span, RC highway bridge , 2005 .

[3]  Y. Yeh,et al.  Spatial variation and stochastic modelling of seismic differential ground movement , 1988 .

[4]  Gang Wang,et al.  Region‐Specific Spatial Cross‐Correlation Model for Stochastic Simulation of Regionalized Ground‐Motion Time Histories , 2015 .

[5]  Armen Der Kiureghian,et al.  Simulation of spatially varying ground motions including incoherence, wave‐passage and differential site‐response effects , 2012 .

[6]  Simona Esposito,et al.  PGA and PGV Spatial Correlation Models Based on European Multievent Datasets , 2011 .

[7]  G. Fenton,et al.  Conditional Simulation of Spatially Correlated Earthquake Ground Motion , 1993 .

[8]  George Deodatis,et al.  Non-stationary stochastic vector processes: seismic ground motion applications , 1996 .

[9]  Jack W. Baker,et al.  A spatial cross‐correlation model of spectral accelerations at multiple periods , 2013 .

[10]  Gang Wang,et al.  Intra‐Event Spatial Correlations for Cumulative Absolute Velocity, Arias Intensity, and Spectral Accelerations Based on Regional Site Conditions , 2013 .

[11]  Gang Wang,et al.  A ground motion selection and modification method capturing response spectrum characteristics and variability of scenario earthquakes , 2011 .

[12]  Abbie B. Liel,et al.  Incorporation of Spatial Correlations between Building Response Parameters in Regional Seismic Loss Assessment , 2014 .

[13]  A. Zerva,et al.  Spatial variation of seismic ground motions: An overview , 2002 .

[14]  Han Ping Hong,et al.  Simulation of Multiple-Station Ground Motions Using Stochastic Point-Source Method with Spatial Coherency and Correlation Characteristics , 2013 .

[15]  Gang Wang,et al.  A simple ground‐motion prediction model for cumulative absolute velocity and model validation , 2013 .

[16]  Friedemann Wenzel,et al.  Influence of spatial correlation of strong ground motion on uncertainty in earthquake loss estimation , 2011 .

[17]  J. Penzien,et al.  Multiple-station ground motion processing and simulation based on smart-1 array data , 1989 .

[18]  N. Abrahamson,et al.  Engineering Characterization of Earthquake Ground Motion Coherency and Amplitude Variability , 2011 .

[19]  D. Dreger,et al.  Coherency analysis of accelerograms recorded by the UPSAR array during the 2004 Parkfield earthquake , 2014 .

[20]  John F. Schneider,et al.  Empirical Spatial Coherency Functions for Application to Soil-Structure Interaction Analyses , 1991 .

[21]  M. Goulard,et al.  Linear coregionalization model: Tools for estimation and choice of cross-variogram matrix , 1992 .

[22]  Roxane Foulser-Piggott,et al.  A predictive model for Arias intensity at multiple sites and consideration of spatial correlations , 2012 .

[23]  Gang Wang,et al.  Fully probabilistic seismic displacement analysis of spatially distributed slopes using spatially correlated vector intensity measures , 2014 .

[24]  G. Atkinson,et al.  Ground-Motion Prediction Equations for the Average Horizontal Component of PGA, PGV, and 5%-Damped PSA at Spectral Periods between 0.01 s and 10.0 s , 2008 .

[25]  J. Baker,et al.  Correlation model for spatially distributed ground‐motion intensities , 2009 .

[26]  R. Olea.  Geostatistics for Natural Resources Evaluation By Pierre Goovaerts, Oxford University Press, Applied Geostatistics Series, 1997, 483 p., hardcover, $65 (U.S.), ISBN 0-19-511538-4 , 1999 .

[27]  Tzay-Chyn Shin,et al.  An Overview of the 1999 Chi-Chi, Taiwan, Earthquake , 2004 .

[28]  Gang Wang,et al.  Design Ground Motion Library: An Interactive Tool for Selecting Earthquake Ground Motions , 2015 .

[29]  H. Hong,et al.  Spatial correlation of peak ground motions and response spectra , 2008 .

[30]  Jack W. Baker,et al.  Stochastic Model for Earthquake Ground Motion Using Wavelet Packets , 2013 .

[31]  Yuehua Zeng,et al.  Fault Rupture Process of the 20 September 1999 Chi-Chi, Taiwan, Earthquake , 2004 .

[32]  Gang Wang,et al.  Spatial Cross‐Correlation Models for Vector Intensity Measures (PGA, I a , PGV, and SAs) Considering Regional Site Conditions , 2013 .