HAL CCSD
Identification of groundwater flow parameters using reciprocal data from hydraulic interference tests
Marinoni, Marianna
Delay, Frederick
Ackerer, Philippe
Riva, Monica
Guadagnini, Alberto
Ecole et Observatoire des Sciences de la Terre (EOST) ; Université de Strasbourg (UNISTRA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)
International audience
ISSN: 0022-1694
Journal of Hydrology
Elsevier
insu-03707899
https://insu.hal.science/insu-03707899
https://insu.hal.science/insu-03707899
Journal of Hydrology, 2016, 539, pp.88-101. ⟨10.1016/j.jhydrol.2016.05.019⟩
BIBCODE: 2016JHyd..539...88M
DOI: 10.1016/j.jhydrol.2016.05.019
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jhydrol.2016.05.019
en
Groundwater flow
Model parameter identification
Lorentz reciprocity
Hydraulic interference
Hydraulic tomography
[SDU]Sciences of the Universe [physics]
info:eu-repo/semantics/article
Journal articles
We investigate the effect of considering reciprocal drawdown curves for the characterization of hydraulic properties of aquifer systems through inverse modeling based on interference well testing. Reciprocity implies that drawdown observed in a well B when pumping takes place from well A should strictly coincide with the drawdown observed in A when pumping in B with the same flow rate as in A. In this context, a critical point related to applications of hydraulic tomography is the assessment of the number of available independent drawdown data and their impact on the solution of the inverse problem. The issue arises when inverse modeling relies upon mathematical formulations of the classical single-continuum approach to flow in porous media grounded on Darcy's law. In these cases, introducing reciprocal drawdown curves in the database of an inverse problem is equivalent to duplicate some information, to a certain extent. We present a theoretical analysis of the way a Least-Square objective function and a Levenberg-Marquardt minimization algorithm are affected by the introduction of reciprocal information in the inverse problem. We also investigate the way these reciprocal data, eventually corrupted by measurement errors, influence model parameter identification in terms of: (a) the convergence of the inverse model, (b) the optimal values of parameter estimates, and (c) the associated estimation uncertainty. Our theoretical findings are exemplified through a suite of computational examples focused on block-heterogeneous systems with increased complexity level. We find that the introduction of noisy reciprocal information in the objective function of the inverse problem has a very limited influence on the optimal parameter estimates. Convergence of the inverse problem improves when adding diverse (nonreciprocal) drawdown series, but does not improve when reciprocal information is added to condition the flow model. The uncertainty on optimal parameter estimates is influenced by the strength of measurement errors and it is not significantly diminished or increased by adding noisy reciprocal information.
2016