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[Other resourceinv_m.tar

Description: Kriging算法的实现,原始实现是Fortran,现在是C语言实现,更为容易理解
Platform: | Size: 16184 | Author: 张帆 | Hits:

[Mathimatics-Numerical algorithms数值分析与算法(徐士良著)随书源程序

Description: 徐士良写的《数值分析与算法》,这是随书的源程序,有需要的朋友可以下来看看!-XU Shi-liang wrote the "Numerical Analysis and algorithm", it is with the source, a friend in need can look down!
Platform: | Size: 95232 | Author: 刘韬 | Hits:

[2D GraphicGradientLegendMod

Description: 对图形色标的编辑管理,实现了色表的添加、删除、修改等功能,类似Surfer的色表-Color graphics editor on the subject of management, realize the color table to add, delete, modify and other functions, similar to the color table Surfer
Platform: | Size: 92160 | Author: zz | Hits:

[Windows DevelopKriging

Description: 离散点资料网格化fortran程序,gslib。-Discrete points of data grid fortran program, gslib.
Platform: | Size: 353280 | Author: dawran | Hits:

[Windows DevelopKT3D

Description: 克里金插值的3D算法,fortran源代码。 -Kriging margin of 3D, fortran source code
Platform: | Size: 11264 | Author: an | Hits:

[Algorithmgslib2

Description:
Platform: | Size: 8872960 | Author: 王伟 | Hits:

[Graph programv35-06-20

Description: 克里金方法fortran编程:地球科学领域的局部不确定性的2维自动化无参数模型代码-AUTO-IK: a 2D indicator kriging program for the automated non-parametric modeling of local uncertainty in earth sciences
Platform: | Size: 433152 | Author: lxf | Hits:

[Otherik3d

Description: 斯坦福大学储层预测中心的FORTRAN3D克里金源代码,该源代码对于初学者学习理解克里金有很大的帮助!-Stanford University Center FORTRAN3D reservoir prediction Kriging source code, the source code for beginners to learn to understand a great help Kerry King!
Platform: | Size: 70656 | Author: wangwei | Hits:

[Windows DevelopGaussian-with--Anisotropy-

Description: C++与FORTRAN混编程序。考虑各向异性的克里金法及序贯高斯法。在地质,油藏建模中很有用处 Author--Jeff Boisvert and Clayton V. Deutsch-for incorporating locally varying anisotropy in kriging or sequential Gaussian simulation is based on modifying how locations in space are related. Normally, the straight line path is used however, when nonlinear features exist the appropriate path between locations follows along the features. The Dijkstra algorithm is used to determine the shortest path/distance between locations and a conventional covariance or variogram function is used. This nonlinear path is a non-Euclidean distance metric and positive definiteness of the resulting kriging system of equations is not guaranteed. Multidimensional scaling (landmark isometric mapping) is used to ensure positive definiteness.
Platform: | Size: 467968 | Author: 张开 | Hits:

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