The integration of geophysical data into the subsurface characterization
problem has been shown in many cases to significantly improve hydrological
knowledge by providing information at spatial scales and locations
that is unattainable using conventional hydrological measurement
techniques. The investigation of exactly how much benefit can be
brought by geophysical data in terms of its effect on hydrological
predictions, however, has received considerably less attention in
the literature. Here, we examine the potential hydrological benefits
brought by a recently introduced simulated annealing (SA) conditional
stochastic simulation method designed for the assimilation of diverse
hydrogeophysical data sets. We consider the specific case of integrating
crosshole ground-penetrating radar (GPR) and borehole porosity log
data to characterize the porosity distribution in saturated heterogeneous
aquifers. In many cases, porosity is linked to hydraulic conductivity
and thus to flow and transport behavior. To perform our evaluation,
we first generate a number of synthetic porosity fields exhibiting
varying degrees of spatial continuity and structural complexity.
Next, we simulate the collection of crosshole GPR data between several
boreholes in these fields, and the collection of porosity log data
at the borehole locations. The inverted GPR data, together with the
porosity logs, are then used to reconstruct the porosity field using
the SA-based method, along with a number of other more elementary
approaches. Assuming that the grid-cell-scale relationship between
porosity and hydraulic conductivity is unique and known, the porosity
realizations are then used in groundwater flow and contaminant transport
simulations to assess the benefits and limitations of the different
approaches.