HR: 17:30h
AN: H44C-07    [Abstracts]
TI: Non-Bayesian Information Fusion for Integrating Hydrologic and Multiple Sets of Geophysical Data
AU: * Ozbek, M M
EM: ozbek@cems.uvm.edu
AF: University of Vermont, Department of Civil and Environmental Engineering, Burlington, VT 05401 United States
AU: Pinder, G F
EM: pinder@cems.uvm.edu
AF: University of Vermont, Department of Civil and Environmental Engineering, Burlington, VT 05401 United States
AB: Combination of geological, geophysical and geohydrological data derived from disparate sources is a cost-effective and scientifically challenging approach to maximizing information on the subsurface. Existing studies have limitations in that no universal methods are available for converting geophysical attributes to geohydrological ones due to the inconsistency in the methods of geophysical data acquisition and interpretation and the fact that the complementary nature of the geophysical methods are not exploited. Indeed, there is no single geophysical method effective in most environmental and subsurface conditions, and all are strongly scenario-dependent. Thus it becomes essential to characterize the information that each individual geophysical method provides in combination. Our approach explicitly quantifies and integrates into the characterization process the insight of a geophysicist on i) the individual capabilities that geophysical methods have and ii) what the meaning of the data is that they produce when interpreted collectively. A model based upon the mathematics of fuzzy set theory based approximate reasoning and of belief theory is used address the following problems: 1) the use of geological and hydrogeological knowledge that relates geological conditions to hydrogeological attributes for the creation of site specific a priori conductivity field in the presence of a limited amount of borehole data 2) the use of geophysical knowledge in the solution of the `geophysical data interpretation' problem defined as the synthesis of data generated by several geophysical methods to infer the true conditions of the soil and 3) the use of the inferred soil information to condition the a priori conductivity field. The approach is demonstrated through an application using real site data.
DE: 1800 HYDROLOGY
DE: 1829 Groundwater hydrology
DE: 1831 Groundwater quality
DE: 5100 PHYSICAL PROPERTIES OF ROCKS
DE: 5114 Permeability and porosity
SC: Hydrology [H]
MN: Fall Meeting 2005