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| Titel |
Estimation of heterogeneous aquifer parameters using centralized and decentralized fusion of hydraulic tomography data |
| VerfasserIn |
A. H. Alzraiee, D. Baù, A. Elhaddad |
| 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 (2014-08-27), S.3207-3223 |
| Datensatznummer |
250120447
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| Publikation (Nr.) |
 copernicus.org/hess-18-3207-2014.pdf |
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| Zusammenfassung |
| Characterization of spatial variability of hydraulic properties of
groundwater systems at high resolution is essential to simulate flow and
transport phenomena. This paper investigates two schemes to invert transient
hydraulic head data resulting from multiple pumping tests for the purpose of
estimating the spatial distributions of the hydraulic conductivity, K, and
the specific storage, Ss, of an aquifer. The two methods are
centralized fusion and decentralized fusion. The centralized fusion of
transient data is achieved when data from all pumping tests are processed
concurrently using a central inversion processor, whereas the decentralized
fusion inverts data from each pumping test separately to obtain optimal local
estimates of hydraulic parameters, which are consequently fused using the
generalized Millman formula, an algorithm for merging multiple correlated or
uncorrelated local estimates. For both data fusion schemes, the basic
inversion processor employed is the ensemble Kalman filter, which is employed
to assimilate the temporal moments of impulse response functions obtained
from the transient hydraulic head measurements resulting from multiple
pumping tests. Assimilating the temporal moments instead of the hydraulic
head transient data themselves is shown to provide a significant improvement
in computational efficiency. Additionally, different assimilation strategies
to improve the estimation of Ss are investigated. Results show
that estimation of the K and Ss distributions using temporal
moment analysis is fairly good, and the centralized inversion scheme
consistently outperforms the decentralized inversion scheme. |
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