This paper presents a novel methodology for inverse modeling of
groundwater flow and transport problems in a Monte Carlo framework,
i.e., multiple solutions to the inverse problem are generated. The
methodology is based on the concept of random mixing of spatial random
fields. The conditional target hydraulic transmissivity field is
obtained as a linear combination of unconditional spatial random fields.
The corresponding weights of the linear combination are selected such
that the spatial variability of the hydraulic transmissivities as well
as the actual observed transmissivity values are reproduced. The
constraints related to the hydraulic head and contaminant concentration
observations are nonlinear. In order to fulfill these constraints, a
specific property of the presented approach is used. A connected domain
of fields fulfilling all linear constraints is identified. This domain
includes an infinite number of realizations, and in this domain, the
head and concentration deviations are minimized using standard
continuous optimization techniques. The methodology uses spatial copulas
to describe the spatial dependence structure. A combination with
multiple point statistics allows inversion under specific structural
constraints.
Research for this paper was supported by the German Science Foundation
(DFG) in the framework of the International Research Training Group
NUPUS under Grant No. GRK 1398.
%0 Journal Article
%1 ISI:000382094800003
%A Bardossy, Andras
%A Hoerning, Sebastian
%C 233 SPRING ST, NEW YORK, NY 10013 USA
%D 2016
%I SPRINGER
%J TRANSPORT IN POROUS MEDIA
%K Copula; Inverse Multiple mixing; modeling; point statistics} {Random
%N 2, SI
%P 241-259
%R 10.1007/s11242-015-0608-4
%T Random Mixing: An Approach to Inverse Modeling for Groundwater Flow and
Transport Problems
%V 114
%X This paper presents a novel methodology for inverse modeling of
groundwater flow and transport problems in a Monte Carlo framework,
i.e., multiple solutions to the inverse problem are generated. The
methodology is based on the concept of random mixing of spatial random
fields. The conditional target hydraulic transmissivity field is
obtained as a linear combination of unconditional spatial random fields.
The corresponding weights of the linear combination are selected such
that the spatial variability of the hydraulic transmissivities as well
as the actual observed transmissivity values are reproduced. The
constraints related to the hydraulic head and contaminant concentration
observations are nonlinear. In order to fulfill these constraints, a
specific property of the presented approach is used. A connected domain
of fields fulfilling all linear constraints is identified. This domain
includes an infinite number of realizations, and in this domain, the
head and concentration deviations are minimized using standard
continuous optimization techniques. The methodology uses spatial copulas
to describe the spatial dependence structure. A combination with
multiple point statistics allows inversion under specific structural
constraints.
@article{ISI:000382094800003,
abstract = {{This paper presents a novel methodology for inverse modeling of
groundwater flow and transport problems in a Monte Carlo framework,
i.e., multiple solutions to the inverse problem are generated. The
methodology is based on the concept of random mixing of spatial random
fields. The conditional target hydraulic transmissivity field is
obtained as a linear combination of unconditional spatial random fields.
The corresponding weights of the linear combination are selected such
that the spatial variability of the hydraulic transmissivities as well
as the actual observed transmissivity values are reproduced. The
constraints related to the hydraulic head and contaminant concentration
observations are nonlinear. In order to fulfill these constraints, a
specific property of the presented approach is used. A connected domain
of fields fulfilling all linear constraints is identified. This domain
includes an infinite number of realizations, and in this domain, the
head and concentration deviations are minimized using standard
continuous optimization techniques. The methodology uses spatial copulas
to describe the spatial dependence structure. A combination with
multiple point statistics allows inversion under specific structural
constraints.}},
added-at = {2017-05-18T11:32:12.000+0200},
address = {{233 SPRING ST, NEW YORK, NY 10013 USA}},
affiliation = {{Horning, S (Reprint Author), Univ Stuttgart, Inst Modeling Hydraul \& Environm Syst, Dept Hydrol \& Geohydrol, Stuttgart, Germany.
Bardossy, Andras; Hoerning, Sebastian, Univ Stuttgart, Inst Modeling Hydraul \& Environm Syst, Dept Hydrol \& Geohydrol, Stuttgart, Germany.}},
author = {Bardossy, Andras and Hoerning, Sebastian},
author-email = {{andras.bardossy@iws.uni-stuttgart.de
sebastian.hoerning@iws.uni-stuttgart.de}},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/21146cdb2cb2120cac1495799520c7522/hermann},
doi = {{10.1007/s11242-015-0608-4}},
eissn = {{1573-1634}},
funding-acknowledgement = {{German Science Foundation (DFG) {[}GRK 1398]}},
funding-text = {{Research for this paper was supported by the German Science Foundation
(DFG) in the framework of the International Research Training Group
NUPUS under Grant No. GRK 1398.}},
interhash = {ebc1acb5c6a1065a9ad744fc313e15ef},
intrahash = {1146cdb2cb2120cac1495799520c7522},
issn = {{0169-3913}},
journal = {{TRANSPORT IN POROUS MEDIA}},
keywords = {Copula; Inverse Multiple mixing; modeling; point statistics} {Random},
keywords-plus = {{GRADUAL DEFORMATION; CONDITIONAL SIMULATION; ITERATIVE CALIBRATION;
MEDIA}},
language = {{English}},
month = {{SEP}},
number = {{2, SI}},
number-of-cited-references = {{28}},
pages = {{241-259}},
publisher = {{SPRINGER}},
research-areas = {{Engineering}},
times-cited = {{1}},
timestamp = {2017-05-18T09:32:12.000+0200},
title = {{Random Mixing: An Approach to Inverse Modeling for Groundwater Flow and
Transport Problems}},
type = {{Article}},
volume = {{114}},
web-of-science-categories = {{Engineering, Chemical}},
year = {{2016}}
}