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| Hauptseite > Publikationsdatenbank > Pattern Recognition in a Bimodal Aquifer Using the Normal-Score Ensemble Kalman Filter > print |
| Hauptseite > Publikationsdatenbank > Pattern Recognition in a Bimodal Aquifer Using the Normal-Score Ensemble Kalman Filter > print |
| 001 | 21353 | ||
| 005 | 20200702121620.0 | ||
| 024 | 7 | _ | |2 DOI |a 10.1007/s11004-011-9372-3 |
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| 037 | _ | _ | |a PreJuSER-21353 |
| 041 | _ | _ | |a eng |
| 082 | _ | _ | |a 550 |
| 084 | _ | _ | |2 WoS |a Geosciences, Multidisciplinary |
| 084 | _ | _ | |2 WoS |a Mathematics, Interdisciplinary Applications |
| 100 | 1 | _ | |0 P:(DE-HGF)0 |a Zhou, H.Y. |b 0 |
| 245 | _ | _ | |a Pattern Recognition in a Bimodal Aquifer Using the Normal-Score Ensemble Kalman Filter |
| 260 | _ | _ | |a Heidelberg [u.a.] |b Springer |c 2012 |
| 300 | _ | _ | |a 169 - 185 |
| 336 | 7 | _ | |a Journal Article |0 PUB:(DE-HGF)16 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a article |2 DRIVER |
| 440 | _ | 0 | |0 25983 |a MATHEMATICAL GEOSCIENCES |v 44 |y 2 SI |
| 500 | _ | _ | |3 POF3_Assignment on 2016-02-29 |
| 500 | _ | _ | |a The authors gratefully acknowledge the financial support by the Spanish Ministry of Science and Innovation through project CGL2011-23295. The first author appreciates the financial aid from China Scholarship Council (CSC No. [2007]3020). |
| 520 | _ | _ | |a The ensemble Kalman filter (EnKF) is now widely used in diverse disciplines to estimate model parameters and update model states by integrating observed data. The EnKF is known to perform optimally only for multi-Gaussian distributed states and parameters. A new approach, the normal-score EnKF (NS-EnKF), has been recently proposed to handle complex aquifers with non-Gaussian distributed parameters. In this work, we aim at investigating the capacity of the NS-EnKF to identify patterns in the spatial distribution of the model parameters (hydraulic conductivities) by assimilating dynamic observations in the absence of direct measurements of the parameters themselves. In some situations, hydraulic conductivity measurements (hard data) may not be available, which requires the estimation of conductivities from indirect observations, such as piezometric heads. We show how the NS-EnKF is capable of retrieving the bimodal nature of a synthetic aquifer solely from piezometric head data. By comparison with a more standard implementation of the EnKF, the NS-EnKF gives better results with regard to histogram preservation, uncertainty assessment, and transport predictions. |
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| 653 | 2 | 0 | |2 Author |a Large heterogeneity |
| 653 | 2 | 0 | |2 Author |a Parameter identification |
| 653 | 2 | 0 | |2 Author |a Non-multi-Gaussian |
| 653 | 2 | 0 | |2 Author |a Uncertainty |
| 653 | 2 | 0 | |2 Author |a Groundwater modeling |
| 653 | 2 | 0 | |2 Author |a Hard data |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Li, L.P. |b 1 |
| 700 | 1 | _ | |0 P:(DE-Juel1)138662 |a Hendricks-Franssen, H.J. |b 2 |u FZJ |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Gomez-Hernandez, J.J. |b 3 |
| 773 | _ | _ | |0 PERI:(DE-600)2414234-7 |a 10.1007/s11004-011-9372-3 |g Vol. 44, p. 169 - 185 |p 169 - 185 |q 44<169 - 185 |t Mathematical geosciences |v 44 |x 1874-8961 |y 2012 |
| 856 | 7 | _ | |u http://dx.doi.org/10.1007/s11004-011-9372-3 |
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