Title :
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
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;
Conference_Titel :
Computational Intelligence and Industrial Applications, 2009. PACIIA 2009. Asia-Pacific Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4606-3
DOI :
10.1109/PACIIA.2009.5406412
