Simulated-annealing-based conditional simulations provide a flexible
means of quantitatively integrating diverse types of subsurface data.
Although such techniques are being increasingly used in hydrocarbon
reservoir characterization studies, their potential in environmental,
engineering and hydrological investigations is still largely unexploited.
Here, we introduce a novel simulated annealing (SA) algorithm geared
towards the integration of high-resolution geophysical and hydrological
data which, compared to more conventional approaches, provides significant
advancements in the way that large-scale structural information in
the geophysical data is accounted for. Model perturbations in the
annealing procedure are made by drawing from a probability distribution
for the target parameter conditioned to the geophysical data. This
is the only place where geophysical information is utilized in our
algorithm, which is in marked contrast to other approaches where
model perturbations are made through the swapping of values in the
simulation grid and agreement with soft data is enforced through
a correlation coefficient constraint. Another major feature of our
algorithm is the way in which available geostatistical information
is utilized. Instead of constraining realizations to match a parametric
target covariance model over a wide range of spatial lags, we constrain
the realizations only at smaller lags where the available geophysical
data cannot provide enough information. Thus we allow the larger-scale
subsurface features resolved by the geophysical data to have much
more due control on the output realizations. Further, since the only
component of the SA objective function required in our approach is
a covariance constraint at small lags, our method has improved convergence
and computational efficiency over more traditional methods. Here,
we present the results of applying our algorithm to the integration
of porosity log and tomographic crosshole georadar data to generate
stochastic realizations of the local-scale porosity structure. Our
procedure is first tested on a synthetic data set, and then applied
to data collected at the Boise Hydrogeophysical Research Site.