HR: 0800h
AN: H11D-0328    [Abstracts]
TI: Adaptive Fusion of Stochastic Information for Imaging Fractured Vadose Zones
AU: * Daniels, J
EM: jeff@geology.ohio-state.edu
AF: Ohio State University, Dept. Geological Sciences, Columbus, OH 43210
AU: Yeh, J
AF: University of Arizona, Dept. Hydrology and Water Resources, Tucson, AZ 85721
AU: Illman, W
AF: University of Iowa, Department of Geoscience, Iowa City, IA 52242
AU: Harri, S
AF: University of Arizona, Dept. Hydrology and Water Resources, Tucson, AZ 85721
AU: Kruger, A
AF: University of Iowa, Department of Geoscience, Iowa City, IA 52242
AU: Parashar, M
AF: Rutgers University, Department of Electrical and Computer Engineering, New Brunswick, NJ 08854
AB: A stochastic information fusion methodology is developed to assimilate electrical resistivity tomography, high-frequency ground penetrating radar, mid-range-frequency radar, pneumatic/gas tracer tomography, and hydraulic/tracer tomography to image fractures, characterize hydrogeophysical properties, and monitor natural processes in the vadose zone. The information technology research will develop: 1) mechanisms and algorithms for fusion of large data volumes ; 2) parallel adaptive computational engines supporting parallel adaptive algorithms and multi-physics/multi-model computations; 3) adaptive runtime mechanisms for proactive and reactive runtime adaptation and optimization of geophysical and hydrological models of the subsurface; and 4) technologies and infrastructure for remote (pervasive) and collaborative access to computational capabilities for monitoring subsurface processes through interactive visualization tools. The combination of the stochastic fusion approach and information technology can lead to a new level of capability for both hydrologists and geophysicists enabling them to "see" into the earth at greater depths and resolutions than is possible today. Furthermore, the new computing strategies will make high resolution and large-scale hydrological and geophysical modeling feasible for the private sector, scientists, and engineers who are unable to access supercomputers, i.e., an effective paradigm for technology transfer.
DE: 1719 Hydrology
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting