Estimation of Epicentral Intensity
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.
KeywordsInformation Matrix Earthquake Engineering Roman Numeral Earthquake Intensity Discrete Subset
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- 1.Berlin, G.L. (1980), Earthquakes and the Urban Environment, Volume I, CRC Press, Boca Raton, FloridaGoogle Scholar
- 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.Irie, B. and Miyake, S. (1988), Capabilities of three-layered perceptrons, Proc. of the International Conference on Neural Networks, pp. 641–648Google Scholar
- 5.Kasahara, K. (1981), Earthquake Mechanics, Cambridge University Press, Cambridge, UKGoogle Scholar
- 7.Tarbuck, E.J. and Lutgens, F.K. (1991), Earth Science ( Sixth Edition ), Macmillan Publishing Company, New YorkGoogle Scholar
- 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