In order to predict the fate and transport of subsurface contaminants we require an accurate model of the subsurface hydrogeologic properties. The focus of our research is the use of ground penetrating radar data for the construction of facies-based hydrogeologic models. The approach we have adopted builds on the concept of radar facies analysis where the radar data are divided into regions that are similar in character or appearance. Our working hypothesis is that the spatial distribution of radar facies can be related to that of lithofacies; and, through the relationship between lithofacies and hydrofacies at a given site, to hydrofacies. The goal of our research is to determine the optimal way to obtain and use a subsurface model of radar facies to develop a subsurface model of hydrofacies.
Our first objective is to develop a method for the classification and identification of radar-facies. The method will be based on the use of neural networks and will provide an efficient way of converting a large, complex radar data set to a simplified model of the facies-scale structure of the subsurface. Our second objective is to determine the relationship between radar facies and the lithofacies and hydrofacies at one well-characterized site. We have elected to work at the Borden groundwater research site in Ontario, Canada where collaboration with Richelle Allen-King and co-workers from Washington State University provides us with a unique and exciting opportunity to investigate the link between radar images and subsurface properties. The successful completion of this research will contribute to our fundamental understanding of the link between what is seen in a radar image, and the facies-scale sedimentary structure of the subsurface. The results of this research will be of value to all researchers currently addressing the issue of how best to use radar data as a basis for the development of hydrogeological models.
