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Large‐scale inverse modeling with an application in hydraulic tomography
Author(s) -
Liu X.,
Kitanidis P. K.
Publication year - 2011
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2010wr009144
Subject(s) - inverse problem , tomography , inverse , electrical resistivity tomography , hydraulic conductivity , inversion (geology) , scale (ratio) , computer science , algorithm , geology , mathematical optimization , soil science , mathematics , electrical resistivity and conductivity , engineering , physics , mathematical analysis , geometry , paleontology , electrical engineering , structural basin , quantum mechanics , optics , soil water
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.