An Approach to Characterizing, Modeling and Analyzing Earthquake Excitation Records

  • Frank Kozin
Part of the International Centre for Mechanical Sciences book series (CISM, volume 225)


In these lectures we shall briefly describe an approach to the study of strong motion earthquake accelerograms that treats them basically as non-stationary time series. We are motivated by three distinct problems:
  1. (1)

    The statistical problem of modeling general non-stationary time series.

  2. (2)

    Characterization of earthquake acceleration records as non-stationary stochastic processes.

  3. (3)

    Predict the dynamic characteristics of local ground surface behavior from strong motion records by means of the stochastic model obtained in (2).



Strong Motion Shot Noise Conditional Moment Strong Motion Data Strong Motion Record 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    Strong motion earthquake accelerograms, Tech. Rep. Earthquake Engineering Research Lab., Cal. Inst. of Tech., Vol.I, 1970, Vol. II, 1973Google Scholar
  2. [2]
    Vanmarcke, E.H., Structural response to earthquakes, Chapter 8 in Seismic Risk and Engineering Decisions, C. Lomnitz and E. Rosenblueth, Eds., Elsevier Book Co., Amsterdam, 1976.Google Scholar
  3. [3]
    Liu, S.C., Synthesis of stochastic representations of ground motions, Bell System Technical Journal, 521, April 1970.Google Scholar
  4. [4]
    Bogdanoff, J., et al., Response of a simple structure to a random earthquake type disturbance, Bull. Seis. Soc. Amer., 51, 293, 1961.MathSciNetGoogle Scholar
  5. [5]
    Shinozuka, J. and Sato, Y., Simulation of nonstationary random processes, Proc. ASCE, 93, EM1, 11, 1967.Google Scholar
  6. [6]
    Jennings, P., et al., Simulated earthquake motions. Tech. Rep. Earth. Eng. Res. Lab., Cal.Tech. April 1968.Google Scholar
  7. [7]
    Cornell, C., Stochastic process models in structural engineering. Dept. Civil Eng., Tech. Report No.34, Stanford, 1964.Google Scholar
  8. [8]
    Lin, Y.K., On nonstationary shot noise, Jour. Acous. Soc. Amer., 36, 82, 1964.CrossRefGoogle Scholar
  9. [9]
    Amin, M. and Ang, A., Nonstationary stochastic model of earthquake motions, Proc, ASCE, 94, EM2, 559, 1968.Google Scholar
  10. [10]
    Goto, H. and Toki, K., Structural response to nonstationary random excitation, Proc. 4th WCEE, Santiago Chile, 1, A-1, 130, 1969.Google Scholar
  11. [11]
    Levy, R. and Kozin, F., Processes for earthquake simulation, Proc.ASCE, Journ. Eng. Mech. 94, EM6, 1597, Dec. 1968.Google Scholar
  12. [12]
    Levy, R. et al., Random processes for earthquake simulation, Proc. ASCE, 97, EM2, 495, 1971.Google Scholar
  13. [13]
    Bolotin, V.V., Statistical Theory of the aseismic design of structures, Proc. 2nd WCEE, Tokyo, Japan, 1365, 1960.Google Scholar
  14. [14]
    Grossmayer, R., On the application of various crossing probabilities in the structural aseismic reliability problem, Proc. IUTAM Symp. Stoch. Prob. in Dynamics Southampton, 17, July 1976.Google Scholar
  15. [15]
    Robinson, E.A., Mathematical devolopment of discrete filters for the detection of nuclear explosions, Jour. Geophys. Res., 68, 19, 5559, 1963.CrossRefMATHGoogle Scholar
  16. [16]
    Robinson, E.A., Predictive decomposition of time series with applications to seismic exploration, Geophysics, 32, 418, 1967.CrossRefGoogle Scholar
  17. [17]
    Kozin, F. and Gran, R., An approach to analysis and modeling of earthquake data in Stochastic Problems in Mechanics, Univ. of Waterloo Press, 193, 1974.Google Scholar
  18. [18]
    Gran, R. and Kozin, F., Nonlinear filtering applied to the modeling of aerthquake data, Proceedings of the Symposium on Nonlinear Estimation and Its Applications, San Diego, Calif., September 1973.Google Scholar
  19. [19]
    Bass, R. and Schwartz, L., Optimal multichannel nonlinear filtering, J.Math. Anal. and Appl., 1966.Google Scholar
  20. [20]
    Kailath, T., An innovations approach to least squares estimation, Part I, Trans. Auto. Contr., AC-13, 646, Dec. 1968.Google Scholar
  21. [21]
    Bendat, J.S. and Piersol, A.G., Random data; Analysis and Measurement Techniques, John Wiley and Sons, New York, 1971.MATHGoogle Scholar

Copyright information

© Springer-Verlag Wien 1977

Authors and Affiliations

  • Frank Kozin
    • 1
  1. 1.Polytechnic Institute of New YorkUSA

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