HR: 17:45h
AN: H24C-08 INVITED     [Abstracts]
TI: Steady-State Hydraulic Tomography in a Laboratory Aquifer with Deterministic Heterogeneity: Multiscale Validation of Hydraulic Conductivity Tomograms
AU: * Illman, W A
EM: walter-illman@uiowa.edu
AF: Department of Geoscience, 121 Trowbridge Hall The University of Iowa , Iowa City, IA 52242 United States
AU: * Illman, W A
EM: walter-illman@uiowa.edu
AF: Department of Civil and Environmental Engineering, The University of Iowa, Iowa City, IA 52242 United States
AU: * Illman, W A
EM: walter-illman@uiowa.edu
AF: IIHR-Hydroscience & Engineering, The University of Iowa, Iowa City, IA 52242 United States
AU: Liu, X
EM: xiaoyi-liu@uiowa.edu
AF: Department of Geoscience, 121 Trowbridge Hall The University of Iowa , Iowa City, IA 52242 United States
AU: Craig, A J
EM: ajcraig@engineering.uiowa.edu
AF: Department of Civil and Environmental Engineering, The University of Iowa, Iowa City, IA 52242 United States
AU: Craig, A J
EM: ajcraig@engineering.uiowa.edu
AF: IIHR-Hydroscience & Engineering, The University of Iowa, Iowa City, IA 52242 United States
AB: Hydraulic tomography is a technology that facilitates subsurface imaging of hydraulic heterogeneity through the inversion of multiple pumping test data. To date, a comprehensive validation of the hydraulic tomography has not been done either at the laboratory or field scales. Validation of hydraulic conductivity tomograms is possible using synthetic simulations because the forcing functions (initial and boundary conditions; source/sink terms) are fully known and errors and biases are minimal. In the field, pumping tests are more prone to variations in forcing functions and errors and biases can never be fully quantified. The main objective of this paper is to examine the accuracy of the hydraulic conductivity tomograms obtained from steady-state hydraulic tomography. We conduct hydraulic tests at multiple scales in a laboratory aquifer with deterministic heterogeneity to generate various validation data. To validate the tomograms, we compare the conditional mean and variance of hydraulic conductivity from the inverse model to the sample mean and variance of results from other measurements. Measurements include multiple hydraulic conductivity estimates from core, slug, single-hole and cross-hole tests as well as several steady-state flow-through experiments obtained within the sandbox. We also examine the influence of errors and biases on inversion results using forward and inverse simulations of synthetic and real cross-hole hydraulic tests. The role of signal-to-noise ratio, order of test data sequentially included in inverse modeling, the number of observation intervals, and conditioning with different data sets are discussed in the context of improving the quality of hydraulic conductivity tomograms.
DE: 1829 Groundwater hydrology
DE: 1846 Model calibration (3333)
DE: 1849 Numerical approximations and analysis
DE: 1873 Uncertainty assessment (3275)
DE: 3265 Stochastic processes (3235, 4468, 4475, 7857)
SC: Hydrology [H]
MN: Fall Meeting 2005