HR: 1340h
AN: GC33A-0956    [Abstracts]
TI: Data Integration for Spatiotemporal Imaging
AU: * Arogunmati, A
EM: mailyemi@gmail.com
AF: Stanford University, Department of Geophysics, Stanford University, 397 Panama Mall, Stanford, CA 94305, United States
AU: Harris, J
EM: harris@pangea.stanford.edu
AF: Stanford University, Department of Geophysics, Stanford University, 397 Panama Mall, Stanford, CA 94305, United States
AB: The primary objective of this study is the development of data evolution methods that integrate previously acquired data with newly acquired data for use in 4-D dynamic tomography. Applications include monitoring of subsurface aquifers, petroleum reservoir, and sequestered carbon dioxide migration in geologic reservoirs. In carrying out a time lapse monitoring experiment, two scenarios are possible: acquiring complete datasets at very large time steps or acquiring incomplete (sparse) datasets at closely spaced time steps. The latter scenario gives the opportunity for tracking changes in the reservoir more frequently and is therefore, preferred. However, the data acquisition process for this latter scenario must trade off spatial resolution for temporal resolution. With the standard methods used today, every dataset is inverted independently. Upon the application of a data evolution method, spatial resolution can be improved while maintaining high temporal resolution. The underlying premise for which the presented methods are developed is that throughout the lifetime of the reservoir being monitored, a few sources and receivers can be used to acquire quasi-continuous data. A complete dataset may have been acquired during site characterization and can be used as the "base" dataset. It can be as high as 10 to 20 times the size of a sparse dataset. Given that the dataset are sparse, one potential quandary is the development of an under-determined tomographic inversion problem, in which case, there is less data than the number of unknowns. The methods presented in this paper address this problem as well.
DE: 1699 General or miscellaneous
DE: 1848 Monitoring networks
DE: 1895 Instruments and techniques: monitoring
DE: 9820 Techniques applicable in three or more fields
SC: Global Environmental Change [GC]
MN: 2007 Fall Meeting