Journal Menu
- About this Journal
- Abstracting and Indexing
- Aims and Scope
- Annual Issues
- Article Processing Charges
- Articles in Press
- Author Guidelines
- Bibliographic Information
- Citations to this Journal
- Contact Information
- Editorial Board
- Editorial Workflow
- Free eTOC Alerts
- Publication Ethics
- Reviewers Acknowledgment
- Submit a Manuscript
- Subscription Information
- Table of Contents
Abstract and Applied Analysis
Volume 2012 (2012), Article ID 805707, 18 pages
doi:10.1155/2012/805707
Research Article
Characterizing Curvilinear Features Using the Localized Normal-Score Ensemble Kalman Filter
1Grupo de Hidrogeologia, Departamento de Ingeniería Hidráulica y Medio Ambiente, Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Spain
2Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, Austin, TX 78712, USA
Received 2 January 2012; Revised 20 March 2012; Accepted 21 March 2012
Academic Editor: Muhammad Aslam Noor
Copyright © 2012 Haiyan Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Linked References
- J. Bear, Dynamics of Fluids in Porous Media, American Elsevier, New York, NY, USA, 1972.
- P. L. Houtekamer and H. L. Mitchell, “A sequential ensemble kalman filter for atmospheric data assimilation,” Monthly Weather Review, vol. 129, no. 1, pp. 123–137, 2001. View at Scopus
- L. Bertino, G. Evensen, and H. Wackernagel, “Sequential data assimilation techniques in oceanography,” International Statistical Review, vol. 71, no. 2, pp. 223–241, 2003. View at Scopus
- G. Navdal, L. M. Johnsen, S. I. Aanonsen, and E. H. Vefring, “Reservoir monitoring and continuous model updating using ensemble kalman filter,” SPE Journal, vol. 10, no. 1, pp. 66–74, 2005. View at Scopus
- Y. Chen and D. Zhang, “Data assimilation for transient flow in geologic formations via ensemble kalman filter,” Advances in Water Resources, vol. 29, no. 8, pp. 1107–1122, 2006. View at Publisher · View at Google Scholar · View at Scopus
- L. Li, H. Zhou, H. J. Hendricks Franssen, and J. J. Gómez-Hernández, “Modeling transient groundwater flow by coupling ensemble kalman filtering and upscaling,” Water Resources Research, vol. 48, 19 pages, 2012. View at Publisher · View at Google Scholar
- H. J. Hendricks Franssen and W. Kinzelbach, “Ensemble kalman filtering versus sequential self-calibration for inverse modelling of dynamic groundwater flow systems,” Journal of Hydrology, vol. 365, no. 3-4, pp. 261–274, 2009. View at Publisher · View at Google Scholar · View at Scopus
- M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 174–188, 2002. View at Publisher · View at Google Scholar · View at Scopus
- G. Evensen and P. J. van Leeuwen, “An ensemble kalman smoother for nonlinear dynamics,” Monthly Weather Review, vol. 128, no. 6, pp. 1852–1867, 2000. View at Scopus
- H. Zhou, J. J. Gómez-Hernández, H. J. Hendricks Franssen, and L. Li, “An approach to handling non-Gaussianity of parameters and state variables in ensemble kalman filtering,” Advances in Water Resources, vol. 34, no. 7, pp. 844–864, 2011. View at Publisher · View at Google Scholar · View at Scopus
- L. Li, H. Zhou, H. J. Hendricks Franssen, and J. J. Gómez-Hernández, “Groundwater flow inverse modeling in non-MultiGaussian media: performance assessment of the normal-score ensemble kalman filter,” Hydrology and Earth System Sciences Discussions, vol. 8, no. 4, pp. 6749–6788, 2011. View at Publisher · View at Google Scholar · View at Scopus
- H. Zhou, L. Li, H. J. Hendricks Franssen, and J. J. Gómez-Hernández, “Pattern recognition in a bimodal aquifer using the normal-score ensemble kalman filter,” Mathematical Geosciences, vol. 44, no. 2, pp. 169–185, 2012.
- G. Evensen, “Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics,” Journal of Geophysical Research, vol. 99, no. 5, pp. 10143–10162, 1994. View at Scopus
- G. Burgers, P. J. van Leeuwen, and G. Evensen, “Analysis scheme in the ensemble kalman filter,” Monthly Weather Review, vol. 126, no. 6, pp. 1719–1724, 1998. View at Scopus
- G. Evensen, Data Assimilation: The Ensemble Kalman Filter, Springer, Berlin, Germany, 2009. View at Publisher · View at Google Scholar
- P. Goovaerts, Geostatistics for natural resources evaluation, Oxford University Press, New York, NY, USA, 1997.
- Y. Chen and D. S. Oliver, “Cross-covariances and localization for EnKF in multiphase flow data assimilation,” Computational Geosciences, vol. 14, no. 4, pp. 579–601, 2010. View at Publisher · View at Google Scholar · View at Scopus
- G. Gaspari and S. E. Cohn, “Construction of correlation functions in two and three dimensions,” Quarterly Journal of the Royal Meteorological Society, vol. 125, no. 554, pp. 723–757, 1999. View at Scopus
- T. M. Hamill, J. S. Whitaker, and C. Snyder, “Distance-dependent filtering of background error covariance estimates in an ensemble kalman filter,” Monthly Weather Review, vol. 129, no. 11, pp. 2776–2790, 2001. View at Scopus
- S. Strebelle, Sequential simulation drawing structures from training images, Ph.D. thesis, Stanford University, 2000.
- J. J. Gómez-Hernández and A. G. Journel, “Joint sequential simulation of Multi-Gaussian fields,” in Geostatistics Tr'oia '92, vol. 1, pp. 85–94, Kluwer Academic, Dordrecht, The Netherlands, 1993.
- A. W. Harbaugh, Banta E. R., M. C. Hill, and M. G. McDonald, “MODFLOW-2000, the U.S. geological survey modular ground-water model—user guide to modularization concepts and the ground-water flow process,” Technical Report Open-File Report 00-92, U.S. Department of the Interior, U.S. Geological Survey, Reston, Va, USA, 2000.
- J. Carrera and S. P. Neuman, “Estimation of aquifer parameters under transient and steady state conditions: 2. Uniqueness, stability, and solution algorithms,” Water Resources Research, vol. 22, no. 2, pp. 211–227, 1986. View at Scopus
- J. P. Delhomme, “Spatial variability and uncertainty in groundwater flow parameters: a geostatistical approach.,” Water Resources Research, vol. 15, no. 2, pp. 269–280, 1979. View at Scopus