Large-scale inverse modeling with an application in hydraulic tomography

Inverse modeling has been widely used in subsurface problems, where direct measurements of parameters are expensive and sometimes impossible. Subsurface media are inherently heterogeneous in complex ways, which implies that the number of unknowns is usually large. Furthermore, technologies such as h...

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Bibliographic Details
Published in:Water resources research 2011-02, Vol.47 (2), p.n/a
Main Authors: Liu, X., Kitanidis, P. K.
Format: Article
Language:English
Subjects:
Algorithms
Fourier transforms
Geophysics
geostatistics
Groundwater
High performance computing
hydraulic tomography
Hydraulics
Hydrology
inverse modeling
Iterative methods
large-scale
least square
Mathematics
Methods
Sparsity
Tomography
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Summary:Inverse modeling has been widely used in subsurface problems, where direct measurements of parameters are expensive and sometimes impossible. Subsurface media are inherently heterogeneous in complex ways, which implies that the number of unknowns is usually large. Furthermore, technologies such as hydraulic tomography and electric resistivity tomography allow the collection of more indirect measurements, and at the same time, there is an increased appreciation of the value of detailed characterization of the subsurface media in, for example, remediation projects. Hence, we need efficient inverse methods that can assimilate a large volume of measurements to estimate even larger numbers of parameters, i.e., large‐scale inverse modeling. In this paper, we present a Bayesian method that employs a sparse formulation, and we applied this method to a laboratory hydraulic tomography problem, where we successfully estimated half a million unknowns that represent the hydraulic conductivity field of the sandbox at a fine scale. The inversion took about 2 h with a single core.
ISSN:0043-1397
1944-7973
DOI:10.1029/2010WR009144