A large number of geologic environments are composed of different layers of contrasting grain sizes. An accurate description of the locations of these stratigraphic units is necessary for defining flow-field boundaries. In groundwater contamination problems, preferential pathways are controlled by the hydraulic properties of these units. This work presents and applies a Bayesian Kriging technique to a subsurface characterization problem. Expertise guesses with given uncertainties are included in the estimation procedure. This technique assures that observation data (hard data) prevail in areas close to observation points, whereas in areas without observations the guesses (soft data) have greater influence. Maps of the estimates and the associated uncertainties are shown to be key tools in reflecting the quantity and quality of the available data. The Simple and Universal Kriging become subsets of this procedure. Key words : Kriging, subsurface characterization, conditional simulation, geostatistics.
[1]
Graham E. Fogg,et al.
Groundwater Flow and Sand Body Interconnectedness in a Thick, Multiple-Aquifer System
,
1986
.
[2]
William H. Press,et al.
Numerical recipes
,
1990
.
[3]
Valerii V. Fedorov,et al.
Kriging and other Estimators of Spatial Field Characteristics (With Special Reference to Environmental Studies)
,
1989
.
[4]
P. L. Finkelstein.
The Spatial Analysis of Acid Precipitation Data.
,
1984
.
[5]
L. Smith.
Spatial variability of flow parameters in a stratified sand
,
1981
.
[6]
H. Omre.
Bayesian kriging—Merging observations and qualified guesses in kriging
,
1987
.
[7]
Henning Omre,et al.
The Bayesian bridge between simple and universal kriging
,
1989
.
[8]
E. Sudicky.
A natural gradient experiment on solute transport in a sand aquifer: Spatial variability of hydraulic conductivity and its role in the dispersion process
,
1986
.