HR: 0800h
AN: H11D-0326 [Abstracts]
TI: Artificially Induced and Naturally Occurring Hydraulic Tomography for Characterizing Groundwater
Basins
AU: * Zhu, J
EM: junfeng@email.arizona.edu
AF: University of Arizona, 1133 E. North Campus Dr., Tucson, AZ 85721
United States
AU: Yeh, T J
EM: yeh@hwr.arizona.edu
AF: University of Arizona, 1133 E. North Campus Dr., Tucson, AZ 85721
United States
AB:
A hydraulic tomographic survey is an innovative approach to characterize the heterogeneity of hydraulic parameters in the
subsurface. During hydraulic tomography, spatial and temporal pressure responses from a series of aquifer excitations at
different locations and times are collected. Such a data set provides additional independent constraints and makes
groundwater inverse problems better posed. Based on the sequential successive linear estimator approach (Yeh and Liu,
2000), we developed an iterative sequential successive linear estimator to analyze the data sets for estimating hydraulic
conductivity and specific storage of three-dimensional groundwater basins. To account for the temporal correlation of
transient heads, we treated head responses at different time steps in one pumping test as one dataset, whereas head responses
from different pumping tests are incorporated into the inverse approach sequentially. After the sequential inclusion of
the data sets from different excitations, we iterate the process to improve our estimates. Our study shows that the head is
highly correlated with specific storage at early time during a pumping test and with hydraulic conductivity at late time.
Consequently, to obtain good estimates of both hydraulic conductivity and specific storage, the head responses at both early
and late time are needed. Our new inverse approach has been implemented on a parallel computing platform and applications of
our new method for large-scale, naturally occurring hydraulic tomographic surveys are discussed using changes in external
loadings on aquifers (such as river stages, barometric pressures, precipitations, surface water reservoirs, trains, etc.).
DE: 3260 Inverse theory
DE: 1829 Groundwater hydrology
DE: 1869 Stochastic processes
DE: 1878 Water/energy interactions
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting