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@PHDTHESIS{Keller:841017,
author = {Keller, Johannes Joachim},
othercontributors = {Hendricks Franssen, Harrie-Jan and Clauser, Christoph and
Kowalski, Julia and Nowak, Wolfgang},
title = {{E}nsemble {K}alman filtering for parameter estimation in
groundwater flow modeling : implementation and robust
comparison of filter variants within the {SHEMAT}-{S}uite
stochastic mode},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2022-01402},
pages = {1 Online-Ressource : Illustrationen},
year = {2020},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2022; Dissertation, Rheinisch-Westfälische
Technische Hochschule Aachen, 2020},
abstract = {Modeling groundwater flow is important for many scientific
and commercial applications related to the geosciences. Two
such applications are the simulation of geothermal systems
where a combination of flow and heat is simulated, and
monitoring contaminant transport where a combination of flow
and species transport is simulated. All such applications
have in common that uncertainty quantification is essential,
in particular the correct modeling of permeabilities of the
porous medium through which the fluid, often water, flows.
This thesis presents a framework for applying the ensemble
Kalman filter (EnKF) for permeability estimation. The EnKF
is a method for data assimilation and parameter estimation
adapted to large-scale, non-linear models. In the first part
of the thesis, the focus is on the numerical code
SHEMAT-Suite. SHEMAT-Suite is described, in particular the
workflow for ensuring error-free and reproducible
implementation that was implemented during this thesis. This
concerns two parts of the software: the groundwater flow
simulation and the EnKF update. A special focus is laid on
the stochastic mode of SHEMAT-Suite. During this thesis, the
existing software that allowed the usage of a potentially
damped EnKF was completely refactored. This included among
other tasks: a module implementation of the methods, a
remodeling of the input file of the stochastic mode to match
the main input file of the software, and, finally, including
numerous error messages. Additionally, the suite of EnKF
methods that is used in the remainder of this thesis was
implemented in the same modular way. In the future, the
modular implementation facilitates adding EnKF methods and
robustly comparing the existing EnKF methods. In the
remainder of this thesis, I use this framework for
investigating possible inaccuracies related to the EnKF and
groundwater flow simulation. First, the influence of the
sampling error stemming from random seed initialization is
analyzed in a comparison of EnKF variants. Secondly, the
pilot point EnKF is introduced. The pilot point EnKF is a
novel EnKF method that is good at suppressing unwanted
spurious correlations that may occur when using EnKF methods
for parameter estimation. The results of this thesis provide
insights for the robust and efficient usage of growing
computer resources for permeability estimation and
groundwater flow estimation. In the majority of scientific
disciplines, quantifying uncertainties is of high importance
and, due to improving computer performance, uncertainty
quantification becomes feasible for previously too
computationally intensive simulations. For permeability
estimation and groundwater flow simulation, quantifying
uncertainty is particularly essential for a number of
reasons. (1) There are usually many uncertain influences in
subsurface models used for groundwater flow, (2) subsurface
models are typically large-scale, and, (3) as a consequence,
small ensemble sizes have to suffice for ensemble-based
uncertainty quantification methods. In this work, a
framework for using ensemble Kalman filters (EnKF) for
parameter estimation is presented. In this framework, the
EnKF methods are implemented as part of the scientific
software SHEMAT-Suite in such a way that the implementation
supports scientific principles, such as reproducibility and
falsifiability. By comparing performances of EnKF methods,
it is shown that the random seed has a non-negligible
influence on these performance comparisons. Finally, the
PP-EnKF is introduced, an EnKF variant tailored towards
suppressing spurious correlations that result from
undersampling in the EnKF.},
cin = {532820 / 530000 / 080052},
ddc = {550},
cid = {$I:(DE-82)532820_20140620$ / $I:(DE-82)530000_20140620$ /
$I:(DE-82)080052_20160101$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2022-01402},
url = {https://publications.rwth-aachen.de/record/841017},
}
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