Geology Reference
In-Depth Information
The applicability and usefulness of kriging as a tool for network design
has been recognized. The success of kriging is primarily based upon taking
into account the structural aspects, such as correlations of geological
formations. One of the most interesting features of kriging, as pointed out
by Matheron (1963), is that the variance of estimation, which measures the
uncertainty of the estimate, can be computed before the actual measurements
are available. This feature suggests its application to the design of
measurement networks, namely to locate measurement points in such a way
that the estimation variance is minimized. Hughes and Lettenmaier (1981)
pointed out that kriging should be more useful for network design than for
estimation. Frequently, measurement points are located by random search
procedures. The estimation variance is computed for each set of measurement
points and the set that gives the smallest estimation variance is accepted
(Journel and Huijbregts, 1978).
ESTIMATION VARIANCE REDUCTION APPROACH
IN OPTIMAL NETWORK DESIGN
Geostatistical estimation variance reduction, cross validation techniques etc.
are a few procedures that could study adequacy of a given monitoring network
and could evolve an optimal monitoring network with some given constraints.
The advantage of the geostatistical estimation technique is that the variance
of the estimation error could be calculated at any point without having the
actual measurement on that point say a well. Thus the benefits to be accrued
from an additional measurement could be studied prior to its measurements.
A few new procedures have been developed using geostatistical technique so
that the number of monitoring wells was reduced without loosing the scientific/
monitoring benefits. It is difficult to define or generalize the necessary and/
or sufficient data for a particular study but availability of adequate
measurements to capture the variability of the parameter is the key for a
successful scientific study. Large amount of measurements will make the
study easy but the project extremely ill-favoured or uneconomic but less
number of data will make the study gloomy. It is difficult but important to
determine the optimal requirement of data for any study. Often it depends on
the scientific objective of the study also. The main objectives of the
geostatistical optimization of the monitoring network have been that the
monitored parameter should:
Represent the true variability of the parameter under study and,
Provide its estimate on unmeasured locations with a desired accuracy in
the form of the variance of the estimation error.
Thus the entire area is usually divided into reasonably finer grid and the
variance of the estimation error are calculated through a suitable kriging
technique and the same are compared with the pre-decided or desired limit
of the variance of the estimation error. Thus depending on the outcome of
