HR: 13:40h
AN: NS43A-01 INVITED    [Abstracts]
TI: Data Integration for Interpretation of Near-Surface Geophysical Tomograms
AU: * Day-Lewis, F D
EM: daylewis@usgs.gov
AF: U.S. Geological Survey, Office of Ground Water, Branch of Geophysics, 11 Sherman Place, Unit 5015, Storrs, CT 06269, United States
AU: Singha, K
EM: ksingha@geosc.psu.edu
AF: Dept. of Geosciences, The Pennsylvania State University, 311 Deike Building, University Park, PA 16802, United States
AB: Traditionally, interpretation of geophysical tomograms for geologic structure or engineering properties has been either qualitative, or based on petrophysical or statistical mapping to convert tomograms of the geophysical parameter (e.g., seismic velocity, radar velocity, or electrical conductivity) to some hydraulic parameter or engineering property of interest (e.g., hydraulic conductivity, porosity, or shear strength). Standard approaches to petrophysical and statistical mapping do not account for variable geophysical resolution, and thus it is difficult to obtain reliable, quantitative estimates of hydrologic properties or to characterize hydrologic processes in situ. Recent research to understand the limitations of tomograms for quantitative estimation points to the need for data integration. We divide near-surface geophysical data integration into two categories: ‘inversion-based' and ‘post- inversion' approaches. The first category includes ‘informed-inversion' strategies that integrate complementary information in the form of prior information; constraints; physically-based regularization or parameterization; or coupled inversion. Post-inversion approaches include probabilistic frameworks to map tomograms to models of engineering properties, while accounting for geophysical resolution, survey design, heterogeneity, and physical models for hydrologic processes. Here, we review recent research demonstrating the need for, and advantages of, data integration. We present examples of both inversion-based and post-inversion data integration to reduce uncertainty, improve interpretation of near-surface geophysical results, and produce more reliable predictive models.
DE: 0520 Data analysis: algorithms and implementation
DE: 0900 EXPLORATION GEOPHYSICS
DE: 1816 Estimation and forecasting
DE: 1835 Hydrogeophysics
DE: 1846 Model calibration (3333)
SC: Near-Surface Geophysics [NS]
MN: 2007 Fall Meeting