It is known that the heterogeneity of hydraulic conductivity drives the groundwater flow and the transport of contaminants. However, in conventional characterization methods, the lack of densely sampled hydrological data does not permit us to describe the aquifer heterogeneity at an appropriate scale. In this study, we integrate ground-penetrating radar (GPR) tomographic data with hydraulic conductivity logs to estimate the hydraulic conductivity of a heterogeneous unconsolidated aquifer at a decimetric scale between two boreholes. The integration of these different data sets is achieved using a nonlinear Bayesian simulation algorithm. The prior hydraulic conductivity distribution is estimated, under Gaussian hypothesis, by simple kriging of the hydraulic well data. The likelihood of hydraulic conductivity given the relative permittivity and the electrical conductivity functions is obtained from a kernel probability density function estimator that describes the in-situ relationship between the electric and the hydraulic properties measured along boreholes. The proposed method is tested on a synthetic heterogeneous model of permeability to validate the methodology. We show that permeability realizations obtained from the proposed algorithm present a higher correlation with the synthetic model than other classical simulation methods. The method is then applied on data acquired over an unconsolidated aquifer located in Saint-Lambert-de-Lauzon, Quebec, Canada. The data set consists of measurements from (i) GPR crosshole acquisition, (ii) cone penetration testing with pressure measurement combined with soil moisture resistivity, and (iii) a borehole electromagnetic flowmeter. By using the presented Bayesian approach, we generated multiple hydraulic conductivity realizations that are in good agreement with the hydrogeological model of the area.