Information fusion in regularized inversion of tomographic pumping tests

Studies in Computational Intelligence
Edited by: CaiCaiJim Yeh T.-C.



In this chapter we investigate a simple approach to incorporating geophysical information into the analysis of tomographic pumping tests for characterization of the hydraulic conductivity (K) field in an aquifer. A number of authors have suggested a tomographic approach to the analysis of hydraulic tests in aquifers - essentially simultaneous analysis of multiple tests or stresses on the flow system - in order to improve the resolution of the estimated parameter fields. However, even with a large amount of hydraulic data in hand, the inverse problem is still plagued by non-uniqueness and ill-conditioning and the parameter space for the inversion needs to be constrained in some sensible fashion in order to obtain plausible estimates of aquifer properties. For seismic and radar tomography problems, the parameter space is often constrained through the application of regularization terms that impose penalties on deviations of the estimated parameters from a prior or background model, with the tradeoff between data fit and model norm explored through systematic analysis of results for different levels of weighting on the regularization terms. In this study we apply systematic regularized inversion to analysis of tomographic pumping tests in an alluvial aquifer, taking advantage of the steady-shape flow regime exhibited in these tests to expedite the inversion process. In addition, we explore the possibility of incorporating geophysical information into the inversion through a regularization term relating the estimated K distribution to ground penetrating radar velocity and attenuation distributions through a smoothing spline model. ?? 2008 Springer-Verlag Berlin Heidelberg.
Publication type Article
Publication Subtype Journal Article
Title Information fusion in regularized inversion of tomographic pumping tests
Series title Studies in Computational Intelligence
ISBN 9783540753834
DOI 10.1007/978-3-540-75384-1_6
Volume 79
Year Published 2008
Language English
Larger Work Type Article
Larger Work Subtype Journal Article
Larger Work Title Studies in Computational Intelligence
First page 137
Last page 162
Google Analytic Metrics Metrics page
Additional publication details