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| Titel |
The effect of training image and secondary data integration with multiple-point geostatistics in groundwater modelling |
| VerfasserIn |
X. L. He, T. O. Sonnenborg, F. Jørgensen, K. H. Jensen |
| 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 (2014-08-07), S.2943-2954 |
| Datensatznummer |
250120430
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| Publikation (Nr.) |
 copernicus.org/hess-18-2943-2014.pdf |
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| Zusammenfassung |
| Multiple-point geostatistical simulation (MPS) has recently become popular
in stochastic hydrogeology, primarily because of its capability to derive
multivariate distributions from a training image (TI). However, its
application in three-dimensional (3-D) simulations has been constrained by the
difficulty of constructing a 3-D TI. The object-based unconditional
simulation program TiGenerator may be a useful tool in this regard; yet the
applicability of such parametric training images has not been documented in
detail. Another issue in MPS is the integration of multiple geophysical
data. The proper way to retrieve and incorporate information from high-resolution geophysical data is still under discussion. In this study, MPS
simulation was applied to different scenarios regarding the TI and soft
conditioning. By comparing their output from simulations of groundwater flow
and probabilistic capture zone, TI from both sources (directly converted
from high-resolution geophysical data and generated by TiGenerator) yields
comparable results, even for the probabilistic capture zones, which are
highly sensitive to the geological architecture. This study also suggests
that soft conditioning in MPS is a convenient and efficient way of
integrating secondary data such as 3-D airborne electromagnetic data
(SkyTEM), but over-conditioning has to be avoided. |
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