Graphics Programs Reference
In-Depth Information
of the lag matrix
D
zeros with
NaN
·s, otherwise the minimum distance will
be zero:
D2 = D.*(diag(x*NaN)+1);
lag = mean(min(D2))
lag =
8.0107
While the estimated variogram values tend to become more erratic with
increasing distances, it is important to defi ne a maximum distance which
limits the calculation. As a rule of thumb, the half maximum distance is
suitable range for variogram analysis. We obtain the half maximum distance
and the maximum number of lags by:
hmd = max(D(:))/2
hmd =
130.1901
max_lags = floor(hmd/lag)
max_lags =
16
Then the separation distances are classifi ed and the classical variogram es-
timator is calculated:
LAGS = ceil(D/lag);
for i = 1:max_lags
SEL = (LAGS == i);
DE(i) = mean(mean(D(SEL)));
PN(i) = sum(sum(SEL == 1))/2;
GE(i) = mean(mean(G(SEL)));
end
where
SEL
is the selection matrix defi ned by the lag classes in
LAG
,
DE
is
the mean lag,
PN
is the number of pairs, and
GE
is the variogram estimator.
Now we can plot the classical variogram estimator (variogram versus mean
separation distance) together with the population variance:
plot(DE,GE,'.' )
var_z = var(z)
b = [0 max(DE)];
c = [var_z var_z];
hold on
plot(b,c, '--r')
