1986
DOI: 10.1029/wr022i002p00199
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Estimation of Aquifer Parameters Under Transient and Steady State Conditions: 1. Maximum Likelihood Method Incorporating Prior Information

Abstract: In this series of three papers a method is presented to estimate the parameters of groundwater flow models under steady and nonsteady state conditions. The parameters include values and directions of principal hydraulic conductivities (or transmissivities) in anisotropic media, specific storage (or storativity), interior and boundary recharge or leakage rates, coefficients of head-dependent interior and boundary sources, and boundary heads. In transient situations, the initial head distribution can also be est… Show more

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Cited by 784 publications
(518 citation statements)
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References 52 publications
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“…''Regularization'' is a mathematical strategy that helps to stabilize ill-posed problems by the enabling the inclusion of additional information [e.g., Tikhonov and Arsenin, 1977;Lawson and Hanson, 1995;Weiss and Smith, 1998;Doherty and Skahill, 2006;Linden et al, 2005;Tonkin and Doherty, 2005;Isaaks and Srivastava, 1989]. By using prior information (either direct or indirect) related to the parameters, regularization is able to ''better condition'' the objective function response surface, either via some kind of penalty function [Carrera and Neuman, 1986;Doherty and Skahill, 2006] or by imposing constraints that reduce the dimensionality of the parameter search space [see Pokhrel et al, 2008Pokhrel et al, , 2009]. …”
Section: Introductionmentioning
confidence: 99%
“…''Regularization'' is a mathematical strategy that helps to stabilize ill-posed problems by the enabling the inclusion of additional information [e.g., Tikhonov and Arsenin, 1977;Lawson and Hanson, 1995;Weiss and Smith, 1998;Doherty and Skahill, 2006;Linden et al, 2005;Tonkin and Doherty, 2005;Isaaks and Srivastava, 1989]. By using prior information (either direct or indirect) related to the parameters, regularization is able to ''better condition'' the objective function response surface, either via some kind of penalty function [Carrera and Neuman, 1986;Doherty and Skahill, 2006] or by imposing constraints that reduce the dimensionality of the parameter search space [see Pokhrel et al, 2008Pokhrel et al, , 2009]. …”
Section: Introductionmentioning
confidence: 99%
“…Historically, the field of groundwater monitoring has made considerable inroads into optimal sampling design. Carrera and Neuman (1986) suggested the reduction of parameter variances (A-optimality criterion) be used to determine the optimal locations to make measurements. Knopman and Voss (1989) optimized the accuracy to which the parameters are determined, cost of sampling and even the type of model used.…”
Section: What Degree Of Confidence Is Associated With the Results?mentioning
confidence: 99%
“…The groundwater inverse analysis is inherently ill posed, and how to overcome or avoid this problem is the essential issue [Cooley, 1982;Yeh, 1986;Carrera and Neuman, 1986a, b, and c; Carrera, 1987;Sun, 1994;McLaughlin and Townley, 1996. The fundamental solution is to obtain sufficient observation data; nevertheless, this solution is not always feasible owing to the restricted budget and other reasons.…”
Section: Matching Of the Subjective And The Objective Informationmentioning
confidence: 99%