The analysis and management of groundwater flow problems often involve the prediction of fluid flow and/or contaminant migration patterns. These phenomena, however, are strongly influenced by the heterogeneity of the hydrogeological properties of the soil. The purpose of this project is to derive joint geophysical-hydrogeological procedures for the characterization of subsurface flow parameters. The first part of this study presents a formal stochastic approach for the integration of surface seismic data and well data into the identification of the spatial arrangement- location, geometry, and interconnectedness of lithofacies. Towards this goal, the lithology of the subsurface is represented through a random indicator function whose spatial structure is identified from seismic reflection data and well logs. Seismic interval velocities and measures of their uncertainties are computed from normal moveout corrections to the seismic reflection data. Calibration curves constructed from the well logs transform these velocity estimates into a lithology indicator prior probability field. From the well data and the prior probability field, the indicator covariance function and its associated confidence limits are computed. Neighboring lithology logs and the indicator covariance function are then combined to update the indicator probability field. To illustrate the applicability of the proposed characterization procedure, a semi-synthetic case study- based on the Fremont study area near the city of Fremont, California- is performed.

In the second part of this study, a Bayesian method is developed to estimate the spatial distribution of the permeability. In addition to sparsely sampled permeability and pressure data, the proposed approach incorporates densely sampled seismic velocity data along with semi-empirical relationships between seismic velocity, permeability and pressure. A hydrological inversion is first performed, 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 demonstrate the usefulness of this approach, synthetic case studies are performed. For further validation, the proposed methodology is applied to real data collected at Kesterson Reservoir, California. These studies demonstrate that by joining seismic data and hydrological data into a common inverse procedure, improved permeability images can be reproduced.