A new Bayesian method is developed to identify the spatial distribution of permeabilities. In addition to sparsely sampled permeability and pressure data, this approach incorporates densely sampled seismic velocity data along with semiempirical relationships between seismic and hydraulic soil properties. The procedure consists first of performing a hydrological inversion based solely on the permeability and pressure data. In light of the available seismic data, the velocity--permeability--pressure relationships are then used to update, in a Bayesian sense, the image of the permeability field. To investigate the usefulness of this approach, synthetic case studies are performed. These studies demonstrate that, even when the seismic data are corrupted by a significant level of error, a joint geophysical-hydrological inversion can produce improved images of permeability. Moreover, this paper derives rigorously the bounds on the error that can be tolerated in seismic velocities such that they are still useful for hydrological purposes. ¿ American Geophysical Union 1993