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| Titel |
Challenges in conditioning a stochastic geological model of a heterogeneous glacial aquifer to a comprehensive soft data set |
| VerfasserIn |
J. Koch, X. He, K. H. Jensen, J. C. Refsgaard |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 18, no. 8 ; Nr. 18, no. 8 (2014-08-06), S.2907-2923 |
| Datensatznummer |
250120428
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| Publikation (Nr.) |
 copernicus.org/hess-18-2907-2014.pdf |
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| Zusammenfassung |
| In traditional hydrogeological investigations, one geological model is often
used based on subjective interpretations and sparse data availability. This
deterministic approach usually does not account for any uncertainties.
Stochastic simulation methods address this problem and can capture the
geological structure uncertainty. In this study the geostatistical software
TProGS is utilized to simulate an ensemble of realizations for a binary
(sand/clay) hydrofacies model in the Norsminde catchment, Denmark. TProGS
can incorporate soft data, which represent the associated level of
uncertainty. High-density (20 m × 20 m × 2 m) airborne geophysical data
(SkyTEM) and categorized borehole data are utilized to define the model of
spatial variability in horizontal and vertical direction, respectively, and
both are used for soft conditioning of the TProGS simulations. The category
probabilities for the SkyTEM data set are derived from a histogram
probability matching method, where resistivity is paired with the
corresponding lithology from the categorized borehole data. This study
integrates two distinct data sources into the stochastic modeling process
that represent two extremes of the conditioning density spectrum: sparse
borehole data and abundant SkyTEM data. In the latter the data have a strong
spatial correlation caused by its high data density, which triggers the
problem of overconditioning. This problem is addressed by a work-around
utilizing a sampling/decimation of the data set, with the aim to reduce the
spatial correlation of the conditioning data set. In the case of abundant
conditioning data, it is shown that TProGS is capable of reproducing
non-stationary trends. The stochastic realizations are validated by five
performance criteria: (1) sand proportion, (2) mean length, (3) geobody
connectivity, (4) facies probability distribution and (5) facies probability–resistivity
bias. In conclusion, a stochastically generated set of
realizations soft-conditioned to 200 m moving sampling of geophysical data
performs most satisfactorily when balancing the five performance criteria. The
ensemble can be used in subsequent hydrogeological flow modeling to address
the predictive uncertainty originating from the geological structure
uncertainty. |
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