STATISTICAL APPROACH TO THE INVERSE PROBLEM OF AQUIFER HYDROLOGY - 3. IMPROVED SOLUTION METHOD AND ADDED PERSPECTIVE.

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Abstract

A new statistically based approach to the problem of estimating spatially varying aquifer transmissivities on the basis of steady water level and flux data. The new method is based on a variational theory developed by G. Chavent which is extended to the case of generalized nonlinear least squares. The method is implemented numerically by a finite element scheme. The inverse problem is posed in terms of log transmissivities instead of transmissivities and is solved by a Fletcher-Reeves conjugate gradient algorithm in conjunction with Newton's method for determining the step size to be taken at each iteration. Two theoretical examples are included to demonstrate the ability of the new method to deal with artificial noise of a relatively large amplitude, derived from a given stochastic model.

Original languageEnglish (US)
Pages (from-to)331-346
Number of pages16
JournalWater Resources Research
Volume16
Issue number2
StatePublished - Apr 1980
Externally publishedYes

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Hydrology
Newton-Raphson method
Stochastic models
inverse problem
Water levels
Inverse problems
Aquifers
hydrology
aquifers
transmissivity
aquifer
Fluxes
methodology
least squares
surface water level
water level
method

ASJC Scopus subject areas

  • Aquatic Science
  • Environmental Science(all)
  • Environmental Chemistry
  • Water Science and Technology

Cite this

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abstract = "A new statistically based approach to the problem of estimating spatially varying aquifer transmissivities on the basis of steady water level and flux data. The new method is based on a variational theory developed by G. Chavent which is extended to the case of generalized nonlinear least squares. The method is implemented numerically by a finite element scheme. The inverse problem is posed in terms of log transmissivities instead of transmissivities and is solved by a Fletcher-Reeves conjugate gradient algorithm in conjunction with Newton's method for determining the step size to be taken at each iteration. Two theoretical examples are included to demonstrate the ability of the new method to deal with artificial noise of a relatively large amplitude, derived from a given stochastic model.",
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