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| Titel |
Bayesian approach for three-dimensional aquifer characterization at the Hanford 300 Area |
| VerfasserIn |
H. Murakami, X. Chen, M. S. Hahn, Y. Liu, M. L. Rockhold, V. R. Vermeul, J. M. Zachara, Y. Rubin |
| 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 ; 14, no. 10 ; Nr. 14, no. 10 (2010-10-21), S.1989-2001 |
| Datensatznummer |
250012449
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| Publikation (Nr.) |
 copernicus.org/hess-14-1989-2010.pdf |
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| Zusammenfassung |
| This study presents a stochastic, three-dimensional characterization of a
heterogeneous hydraulic conductivity field within the Hanford 300 Area, Washington, USA, by assimilating large-scale, constant-rate injection test
data with small-scale, three-dimensional electromagnetic borehole flowmeter
(EBF) measurement data. We first inverted the injection test data to estimate
the transmissivity field, using zeroth-order temporal moments of pressure
buildup curves. We applied a newly developed Bayesian geostatistical
inversion framework, the method of anchored distributions (MAD), to obtain a
joint posterior distribution of geostatistical parameters and local
log-transmissivities at multiple locations. The unique aspects of MAD that
make it suitable for this purpose are its ability to integrate multi-scale,
multi-type data within a Bayesian framework and to compute a nonparametric
posterior distribution. After we combined the distribution of
transmissivities with depth-discrete relative-conductivity profile from the
EBF data, we inferred the three-dimensional geostatistical parameters of the
log-conductivity field, using the Bayesian model-based geostatistics. Such
consistent use of the Bayesian approach throughout the procedure enabled us
to systematically incorporate data uncertainty into the final posterior
distribution. The method was tested in a synthetic study and validated using
the actual data that was not part of the estimation. Results showed broader
and skewed posterior distributions of geostatistical parameters except for
the mean, which suggests the importance of inferring the entire distribution
to quantify the parameter uncertainty. |
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