Pattern Recognition in a Bimodal Aquifer Using the Normal-Score Ensemble Kalman Filter
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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.
KeywordsLarge heterogeneity Parameter identification Non-multi-Gaussian Uncertainty Groundwater modeling Hard data
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- Evensen G (2007) Data assimilation: the ensemble Kalman filter. Springer, Berlin, 279 pp Google Scholar
- Fernàndez-Garcia D, Illangasekare T, Rajaram H (2005) Differences in the scale dependence of dispersivity and retardation factors estimated from forced-gradient and uniform flow tracer tests in three-dimensional physically and chemically heterogeneous porous media. Water Resour Res 41(3):W03012 CrossRefGoogle Scholar
- Harbaugh AW, Banta ER, Hill MC, McDonald MG (2000) MODFLOW-2000, the U.S. geological survey modular ground-water model—user guide to modularization concepts and the ground-water flow process. Tech rep. Open-File Report 00-92, U.S. Department of the Interior, U.S. Geological Survey. Reston, Virginia, 121 pp Google Scholar
- Naevdal G, Johnsen L, Aanonsen S, Vefring E (Mar. 2005) Reservoir monitoring and continuous model updating using ensemble Kalman filter. SPE J 10(1):66–74 Google Scholar
- Schöniger A, Nowak W, Hendricks Franssen HJ (2011) Parameter estimation by ensemble Kalman filters with transformed data: approach and application to hydraulic tomography. Water Resour Res (submitted) Google Scholar
- Stauffer D, Aharony A (1994) Introduction to percolation theory. Taylor and Francis, London. 181 pp Google Scholar
- Strébelle S 2000. Sequential simulation drawing structures from training images. PhD thesis, Stanford University. 187 pp Google Scholar
- Wen X, Chen W (2006) Real-time reservoir model updating using ensemble Kalman filter: the confirming approach. SPE J 11(4):431–442 Google Scholar
- Wen X, Chen W (2007) Some practical issues on real time reservoir updating using ensemble Kalman filter. SPE J 12(2):156–166 Google Scholar