Estimation of Epicentral Intensity

  • Chongfu Huang
  • Yong Shi
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 99)


In this chapter, based on the principle of information diffusion, we suggest a new method, called self-study discrete regression, to construct a statistic relationship from a given sample. To understand this, a detail discussion develops around estimation of epicentral intensity. This chapter is organized as follows: in section 8.1 we introduce some basic concepts in seismology and earthquake engineering for studying estimation of epicentral intensity. In section 8.2, we review the linear regression and a fuzzy method for the estimation. Section 8.3 describes the method of self-study discrete regression. Section 8.4 and 8.5, respectively, give linear distribution self-study (LDSS) and normal diffusion self-study (NDSS) models to estimate epicentral intensity by magnitude. Then we conclude the chapter in section 8.6.


Information Matrix Earthquake Engineering Roman Numeral Earthquake Intensity Discrete Subset 
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  1. 1.
    Berlin, G.L. (1980), Earthquakes and the Urban Environment, Volume I, CRC Press, Boca Raton, FloridaGoogle Scholar
  2. 2.
    Dracopoulos, D.C. (1997), Evolutionary Learning Algorithms for Neural Adaptive Control, Springer-Verlag, HeidelbergMATHGoogle Scholar
  3. 3.
    Feng, D.Y., Lin, M.Z., Wu, G.Y. and Jiang, C. (1985), A study on fuzzy evaluation of earthquake intensity. Fen Deyi and Liu Xihui (eds): Fuzzy Mathematics in Earthquake Researches. Seismological Press, Beijing, pp. 149–161Google Scholar
  4. 4.
    Irie, B. and Miyake, S. (1988), Capabilities of three-layered perceptrons, Proc. of the International Conference on Neural Networks, pp. 641–648Google Scholar
  5. 5.
    Kasahara, K. (1981), Earthquake Mechanics, Cambridge University Press, Cambridge, UKGoogle Scholar
  6. 6.
    Moody, J. and Darken, C. J. (1989), Fast learning in networks of locally tuned processing units. Neural Computation, Vol. 1, pp. 281–194CrossRefGoogle Scholar
  7. 7.
    Tarbuck, E.J. and Lutgens, F.K. (1991), Earth Science ( Sixth Edition ), Macmillan Publishing Company, New YorkGoogle Scholar
  8. 8.
    Wang, F. (1983), Fuzzy recognition of relations between epicentral intensity and magnitude, Earthquake Engineering and Engineering Vibration, Vol. 3, No. 3, pp. 84–96Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Chongfu Huang
    • 1
  • Yong Shi
    • 2
  1. 1.Institute of Resources ScienceBeijing Normal UniversityBeijingChina
  2. 2.College of Information Science and TechnologyUniversity of Nebraska at OmahaOmahaUSA

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