The GLOBEC Kriging Software Package - EasyKrig3.0, May 1, 2004
Copyright (c) 1998, 2001, 2004. property of Dezhang Chu and Woods Hole Oceanographic Institution.
All Rights Reserved.
1. INTRODUCTION
1.1 General Information
1.1.1 About kriging
This section provides a brief theoretical background for kriging. If the user(s) is not interested in
the theoretical background, he/she can skip this section and go to section 1.1.2 directly.
Kriging is a technique that provides the Best Linear Unbiased Estimator of the unknown
fields (Journel and Huijbregts, 1978; Kitanidis, 1997). It is a local estimator that can provide
the interpolation and extrapolation of the originally sparsely sampled data that are assumed to be
reasonably characterized by the Intrinsic Statistical Model (ISM). An ISM does not require the quantity
of interest to be stationary, i.e. its mean and standard deviation are independent of position, but rather
that its covariance function depends on the separation of two data points only, i.e.
E[(z(x) - m)(z(x') - m) ] = C(h), (1)
where m is the mean of z(x) and C(h) is the covariance function with lag h, with h being the distance
between two samples x and x':
h = || x - x' ||. (2)
Another way to characterize an ISM is to use a semi-variogram,
gamma(h) = 0.5* E[ (z(x) - z(x') )^2]. (3)
The relation between the covariance function and the semi-variogram is
gamma(h) = C(0) - C(h). (4)
The kriging method is to find a local estimate of the quantity at a specified location, x(L).
This estimate is a weighted average of the N adjacent observations:
z(x(L)) = sum( lambda(i) z(x(i)), (5)
where i is from 1 to N, and x(L) are the coordinates of an arbitrary point whose value is what
we want to estimate.
The weighting coefficients lammbda(i) can be determined based on the minimum estimation variance criterion:
See Eq.(6) in Description.doc file (6)
subject to the normalization condition.
sum(lambda(i)) = 1, (7)
where i is from 1 to N. Note that we don't know the exact value at , but we are trying to find a predicted
value that provides the minimum estimation variance. The resultant kriging equation can be expressed as
See Eq.(8) in Description.doc file (8)
where mu is the Lagrangian coefficient. In addition, we have replaced the covariance function with
the normalized covariance function [normalized by C(0)]. Equivalently, by using Eq. (4), the kriging
equation can also be expressed in terms of the semi-variogram as
See Eq.(9) in Description.doc file (9)
where we have used normalized semi-variogram, i.e., semi-variogram normalized by C(0) as we did in deriving Eq. (8).
Having obtained the weighting coefficients (lambda_beta) and the Lagrangian coefficient (mu) by solving either Eq. (8) or
Eq. (9), the kriging variance, Eq. (6), can be expressed as:
See Eq.(8) in Description.doc file (10)
The above equations are the basis of the Easykrig software package.
1.1.2 Brief description of EasyKrig3.0
The EasyKrig program package uses a Graphical User Interface (GUI) to simplify the operation. It requires MATLAB 5.3 or
higher with or without optimization toolbox (see section 2.2) and consists of five components, or processing stages:
(1) data preparation, (2) variogram computation, (3) kriging, (4) visualization and (5) saving results. It allows the
user to process anisotropic data, select an appropriate model from a list of variogram models, and a choice of kriging
methods, as well as associated kriging parameters, which are also common features of the other existing software
packages. One of the major advantages of this program package is that the program minimizes the users' requirements to
"guess" the initial parameters and automatically generates the required default parameters. In addition, because it
uses a GUI, the modifications from the initial parameter settings can be easily performed. Another feature of this
program package is that it has a built-in on-line help library that allows the user to obtain the descriptions of the
use of parameters and operation options easily.
The current EasyKrig3.0 is the upgraded version of the previous version (EasyKrig2.1). In addition to having corrected
some programming errors in the previous version (mostly GUI related errors), there are many new features included in
the current version:
· Matlab Version 6.x compatible
· Capable of handling 3-D data
· Enhanced batch file processing capability
· More flexible in loading input data and saving output data
· Capable of handling the customized grid file
· More examples with detailed step by step instructions are provided to allow user(s) to master the functionality
of the software package more quickly and easily.
Although this software package lacks some abilities such as Co-kriging, it does provide a convenient tool for
geostatistical applications and should also help scientists from other fields.
For people who do not want to use GUI but only interested in function-based m-files can go to a different website
that provides a function-based m-file Kriging package (http://globec.whoi.edu/software/kriging/V3/intro_v3.html)
developed by Caroline Lafleur and Yves Gratton, INRS-Océanologie, Universit du Qubec Rimouski.
1.2 Getting Started
1.2.1 Operating systems
The software was originally developed under MATLAB 6.5 on a PC (windows 2000) and intended to be computer and/or
operating system independent. The program has been tested on various machines (PC, Macintosh, and Sun Workstation)
and operating systems (windows 2000/Xp, Linux) and performs fine.
1.2.2 Down-load the program
The user needs to download the compressed file from GLOBEC web site first, the URL is
ftp://globec.whoi.edu/pub/software/kriging/easy_krig/V3.0/
and the compressed file is
Windows 95/98/NT/2000/XP and Linux: easy_krig30.zip
Unix: easy_krig30.tar.Z
Macintosh: easy_krig30mac.zip
After having downloaded the file, the user needs to uncompress the file. A directory of easy_krig3.0 will be
created and you are ready to run the program.
1.2.3 Quick start
Start MATLAB and go to the designated easy_krig3.0 home directory. Just type "startkrig" in the MATLAB
command window, a window will pop up. This window is the base window, called the Navigator window. The Menubar in this
window contains many options you can choose. Now you are ready to move on.
Note: You can add the kriging home directory to the Matlab search path and run the program from other directories.
However, you have to make sure that there are no functions of your own having the same names as those used for
easy_krig3.0. Since the program will allow you to load and save files using a file browser, it is recommended that
you run the program under the easy_krig3.0 home directory.
The program provides a full on-line help function that provides the descriptions the use of almost all of the
selectable functions, options, and parameters. It is quite self-explanatory and easy to use.
2. DATA PROCESSING STAGES
There are several data processing stages (tasks) that are selectable from the Menubar on the top of the Navigator
window, as well as other task windows. By selecting or clicking on any of the tasks, a window corresponding to the
selected task will pop up. On each task window including the Navigator window, the descriptions and explanations
of every option and selection in each task window can b
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克里金插值的matlab实现
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克里金插值法的计算机实现多种多样,这里是克里金插值的matlab实现。克里金法(Kriging)是依据协方差函数对随机过程/随机场进行空间建模和预测(插值)的回归算法。在特定的随机过程,例如固有平稳过程中,克里金法能够给出最优线性无偏估计(Best Linear Unbiased Prediction, BLUP),因此在地统计学中也被称为空间最优无偏估计器(spatial BLUP)。
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克里金插值的matlab实现 (115个子文件)
Temperature3d.dat 158KB
globec_grid.dat 133KB
salinity.dat 18KB
layer14.dat 4KB
layer13.dat 4KB
layer12.dat 4KB
layer10.dat 4KB
layer11.dat 4KB
color_info.dat 3KB
zooplankton.dat 900B
pressure_batch_file.dat 135B
Description.doc 119KB
general_message.fig 9KB
variogram3dfig.m 30KB
kriging3dfig.m 29KB
dispkrig3dfig.m 28KB
dataprep3dfig.m 22KB
radio_action.m 17KB
dispkrig3d.m 16KB
file_browser3d.m 12KB
variogram_proc.m 9KB
variogram_help.m 9KB
dataprep_help.m 8KB
visualization_help.m 8KB
krig.m 8KB
dataprep3d.m 8KB
kriging_help.m 7KB
plotvariogram2d3dfig.m 7KB
krig3d.m 7KB
krig3dmanager.m 7KB
plotvariogram2d3d.m 7KB
cross_validation.m 7KB
krig2d.m 6KB
loaddatfile.m 6KB
variogram3d.m 6KB
set3dkrigpara.m 6KB
set3dvariopara.m 6KB
general_message.m 5KB
datachk.m 5KB
main_menu3d.m 4KB
batch_krig.m 4KB
initialization6x.m 4KB
validationfig.m 4KB
popupmenu_action.m 4KB
modelstring.m 4KB
validation_proc.m 4KB
vario_opt_button.m 3KB
advanced_dispkrig3d.m 3KB
getdefault3dvariopara.m 3KB
variogrammodel3d.m 3KB
check_unitsfig.m 3KB
plotvariogram1d.m 3KB
mapax.m 3KB
load_para_file.m 2KB
program_info.m 2KB
display_globec_grid.m 2KB
degmins.m 2KB
save_data_format_info.m 2KB
kriging_info.m 2KB
slider_action.m 2KB
radio_action_visual.m 2KB
datareduction.m 2KB
radio_action_vario2d3d.m 2KB
get_set_gridfile_para.m 2KB
load_data_format_info.m 2KB
message.m 2KB
coordtransform3d.m 2KB
startkrig.m 2KB
getdefault3dkrigpara.m 2KB
display_3dkrig_results.m 2KB
lsqfit.m 2KB
variogram_theo.m 2KB
test_message.m 2KB
blk2blk3d.m 2KB
get3dvariopara_theo.m 1KB
xy2ll.m 1KB
pdf_func.m 1KB
navigator_help.m 1KB
sta2blk3d.m 1KB
get_data_format_info.m 1KB
datatransform.m 1KB
sta2blk2d.m 1KB
help_message.m 1KB
median_nan.m 1KB
sort_mex.m 1KB
close_window.m 1KB
getXvisual.m 1KB
mean_nan.m 1KB
std_nan.m 1KB
sum_nan.m 965B
get_ninc.m 884B
remove_nan.m 874B
cost_function.m 859B
ll2xy.m 845B
auto_contour.m 828B
disp_images.m 740B
filenametruncation.m 657B
test_remove_nan.m 654B
save_window_pos.m 560B
save_para_file.m 558B
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