Graphics Programs Reference
In-Depth Information
where
srqt
is the square root of the data. Then we get the experimental
variogram
G
as half the squared differences between the observed values:
G = 0.5*(Z1 - Z2).^2;
We used the MATLAB capability to vectorize commands instead of us-
ing
for
loops in order to run faster. However, we have computed
n
2
pairs
of observations although only
n
*(
n
-1)/2 pairs are required. For large data
sets, e.g., more than 3000 data points, the software and physical memory
of the computer may become a limiting factor. For such cases, a more ef-
fi cient way of programming is described in the user manual of the software
SURFER (2002). The plot of the experimental variogram is called the
var-
iogram cloud
(Fig. 7.12). We get this after extracting the lower triangular
portions of the
D
and
G
arrays
indx = 1:length(z);
[C,R] = meshgrid(indx);
I = R>C;
9
8
7
6
5
4
3
2
1
0
0
50
100
150
200
250
300
Distance between observations
Fig. 7.12
Variogram cloud: Plot of the experimental variogram (half squared difference
between pairs of observations) versus the lag distance (separation distance of the pairs).
