Application of data fusion to characterization of the Fountain and Lyons Formations at a field site incorporates geologic knowledge, geophysical log data, cross-hole seismic tomography, hydraulic test data, and observations of head to reduce uncertainty associated with subsurface interpretation. These formations consist of channel and overbank deposits that have undergone variable diagenesis, resulting in more hydrofacies than would have been encountered in the original, unaltered deposits. The disparate types of available data are integrated to yield a coherent hydrofacies classification through use of discriminant analysis and soft data techniques. This data fusion improves definition of the complex hydrofacies and increases knowledge of their spatial correlation. Two hundred multiple-indicator, conditional, stochastic simulations of the site are generated, 100 with only hard data and 100 with both hard and soft data. Forward groundwater flow modeling using estimates of hydraulic conductivity from field testing yields smaller head residuals for realizations which include soft data. Inverse modeling is used to eliminate hydrofacies realizations that do not honor hydraulic data and to estimate hydrofacies hydraulic conductivity ranges for the hard and hard/soft data ensembles. Inverse parameter estimation substantially decreases head residuals for both ensembles. Standard deviations of hydraulic conductivities estimated through inverse modeling are smaller when both hard and soft data are used to generate the simulations, even though head residuals are similar within the two ensembles when these estimated hydraulic conductivities are used.