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| Titel |
Transient inverse groundwater flow modelling using Random Mixing and Multiple-Point Statistics |
| VerfasserIn |
Sebastian Hörning, András Bárdossy |
| Konferenz |
EGU General Assembly 2015
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 17 (2015) |
| Datensatznummer |
250103086
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| Publikation (Nr.) |
 EGU/EGU2015-2485.pdf |
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| Zusammenfassung |
| The conditioning to measurement data by inverse modelling techniques aims to reduce the
inherent estimation uncertainty of flow and transport predictions. Besides conditioning to
hydraulic head measurements, especially in geological formations with contrasting facies of
highly different hydraulic conductivities, conditioning to concentration data (e.g. resulting
from tracer tests) may improve the estimation of spatially variable aquifer properties like
hydraulic conductivities (K).
In general the aim of inverse groundwater flow modelling is to obtain fields:
with prescribed spatial variability
with the observed values of the variable of interest at the observation locations
(maybe also at different spatial scales)
with observations (hydraulic head, concentration) coupled through the model.
Those goals are achieved using inverse modelling by random mixing. This method uses a high
dimensional geometric concept to generate conditional random fields as a weighted sum of
unconditional fields. The idea of the inverse modelling approach is to generate fields that
fulfill the first and the second conditions so that these fields form a connected domain which
has a continuous parametrization. Then the third condition can be handled by optimization
inside the above described connected domain. If no sufficient solution can be obtained the
dimensionality of the problem is increased by enlarging the continuous domain
and the optimization is continued. To include curvilinear features in the spatial
distribution of K, the methodology can be coupled with a multiple-point geostatistics
approach.
To illustrate the performance a synthetic test case example is applied. |
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