Table 2.
Line
2
Variogram of Line 1 and Line 2 FIDLER Date (1 Foot Aboveground)
(Continued)
Number
of Pairs
Lag Number
1
2
3
360
34
22
10
161
Distance
(Inches)
5
10
15
1800
Variogram
(x 10 3
58.37
56.78
55.91
124.1
It does not seem possible though, to use these results from Line 1 to
model the short distance behavior of the variogram over all the area
covered by the 100-foot grid, since the counting error depends on the
level of activity.
At very low levels along Line 2, we get pure noise
(Table 2), signifying no correlation structure.
A question at this point is:
analyses consistent?"
“Are the l-foot and the 100-foot scale
The answer is yes.
Seen from a 100-foot grid, the
details of the l-foot scale structures merely appear as noise.
The value
Cy = 0.0127, which may be called the “apparent nugget effect,” is the
variance of that noise. Since we are working at a 100-foot scale, we do
not need a precise modeling of microstructures and can instead use a
simplified macroscopic model with a discontinuity of magnitude Co
The T(x) Field
We do not have direct access to T(x), the true correction term, and what
we analyze are the estimated residuals from the regression:
T(x,) = P(x,) - aF(x,) ~ B= TOx,) + fe(x,) -anG@I.
AD
Figure 7 shows the raw variograms of these residuals computed separately
for FIDLER > 5,000 and FIDLER < 5,000.
The variograms of the differences
{log Am(x) - a,F(x) - B,} on Lines 1 and 2 data are also plotted for
comparison (a, and §, regression coefficients were calculated using
pooled Lines 4 and 2 data).
Since the Pu to Am ratio is approximately
constant in the log scale, these differences should be comparable to
values of T(x).
Each of these variograms stabilizes at a certain level, called the
"sill," indicating a stationary behavior of T(x).
This was to be expected
since the drift effect is already accounted for by the guess field
aF(x) + 8.
The value at the sill is equal to the overall variance of the
382