Kriging space surface fitting and application based on genetic algorithm

Author

Liu, Zhifeng ; Wei, Zhenhua ; Ju, Xia

Author_Institution

China Univ. of Geosci., Wuhan, China

Volume

1

fYear

2009

fDate

28-29 Nov. 2009

Firstpage

38

Lastpage

41

Abstract

Semi-variant function as an important mathematical model of Kriging spatial analysis can effectively describe the features of the variants (such as ore grade, thickness of ore body) in some districts of ore deposit. This paper introduced the method that how to use genetic algorithm (GA) to estimate the semi-variant function parameters for Kriging spatial analysis and this method is applied to establish the three-dimensional (3D) overburden model of the prospecting area for a hydropower project successfully.

Keywords

genetic algorithms; geophysical techniques; statistical analysis; 3D overburden model; genetic algorithm; kriging space surface fitting; kriging spatial analysis; ore deposit; semivariant function parameters; Aerospace industry; Algorithm design and analysis; Computational intelligence; Computer industry; Equations; Genetic algorithms; Geology; Interpolation; Parameter estimation; Surface fitting; 3D model; Genetic Algorithm; Kriging; Semi-variant function;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Industrial Applications, 2009. PACIIA 2009. Asia-Pacific Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4606-3

Type

conf

DOI

10.1109/PACIIA.2009.5406412

Filename

5406412