- -

Groundwater flow inverse modeling in non-MultiGaussian media: Performance assessment of the normal-score Ensemble Kalman Filter

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Groundwater flow inverse modeling in non-MultiGaussian media: Performance assessment of the normal-score Ensemble Kalman Filter

Mostrar el registro completo del ítem

Li, L.; Zhou, H.; Hendricks-Franssen, HJ.; Gómez-Hernández, JJ. (2012). Groundwater flow inverse modeling in non-MultiGaussian media: Performance assessment of the normal-score Ensemble Kalman Filter. Hydrology and Earth System Sciences and Discussions. 16(2):573-590. https://doi.org/10.5194/hess-16-573-2012

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/30679

Ficheros en el ítem

Metadatos del ítem

Título: Groundwater flow inverse modeling in non-MultiGaussian media: Performance assessment of the normal-score Ensemble Kalman Filter
Autor: Li, Liangping Zhou, Haiyan Hendricks-Franssen, Hendrikus Johannes Gómez-Hernández, J. Jaime
Entidad UPV: Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient
Fecha difusión:
Resumen:
[EN] The normal-score ensemble Kalman filter (NS-EnKF) is tested on a synthetic aquifer characterized by the presence of channels with a bimodal distribution of its hydraulic conductivities. This is a clear example of an ...[+]
Palabras clave: Bimodal distribution , Connectivity pattern , Ensemble Kalman Filter , Flow configurations , Flow problems , Inverse modeling , Performance assessment , Pie- zometric Head , Random function model , Synthetic aquifers
Derechos de uso: Reconocimiento (by)
Fuente:
Hydrology and Earth System Sciences and Discussions. (issn: 1027-5606 )
DOI: 10.5194/hess-16-573-2012
Editorial:
European Geosciences Union (EGU)
Versión del editor: http://dx.doi.org/10.5194/hess-16-573-2012
Código del Proyecto:
info:eu-repo/grantAgreement/MICINN//CGL2011-23295/ES/MODELACION ESTOCASTICA INVERSA FUERA DE LO NORMAL/
Agradecimientos:
The authors gratefully acknowledge the financial support by the Spanish Ministry of Science and Innovation through project CGL2011-23295. The two anonymous reviewers are gratefully acknowledged for their comments which ...[+]
Tipo: Artículo

References

Alcolea, A. and Renard, P.: Blocking Moving Window algorithm: Conditioning multiple-point simulations to hydrogeological data, Water Resour. Res., 46, W08511, https://doi.org/10.1029/2009WR007943, 2010.

Alcolea, A., Carrera, J., and Medina, A.: Pilot points method incorporating prior information for solving the groundwater flow inverse problem, Adv. Water Resour., 29, 1678–1689, 2006.

Anderson, J.: An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903, 2001. [+]
Alcolea, A. and Renard, P.: Blocking Moving Window algorithm: Conditioning multiple-point simulations to hydrogeological data, Water Resour. Res., 46, W08511, https://doi.org/10.1029/2009WR007943, 2010.

Alcolea, A., Carrera, J., and Medina, A.: Pilot points method incorporating prior information for solving the groundwater flow inverse problem, Adv. Water Resour., 29, 1678–1689, 2006.

Anderson, J.: An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903, 2001.

Arulampalam, M., Maskell, S., Gordon, N., and Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE T. Signal Process., 50, 174–188, 2002.

Bear, J.: Dynamics of fluids in porous media, American Elsevier Pub. Co., New York, 1972.

Burgers, G., van Leeuwen, P., and Evensen, G.: Analysis scheme in the ensemble Kalman filter, Mon. Weather Rev., 126, 1719–1724, 1998.

Capilla, J. and Llopis-Albert, C.: Gradual conditioning of non-Gaussian transmissivity fields to flow and mass transport data: 1. Theory, J. Hydrol., 371, 66–74, 2009.

Capilla, J. E., Rodrigo, J., and G{ó}mez-Hern{á}ndez, J. J.: Simulation of non-Gaussian transmissivity fields honoring piezometric data and integrating soft and secondary information, Math. Geol., 31, 907–927, 1999.

Carrera, J., Alcolea, A., Medina, A., Hidalgo, J., and Slooten, L.: Inverse problem in hydrogeology, Hydrogeol. J., 13, 206–222, 2005.

Chen, Y. and Oliver, D.: Cross-covariances and localization for EnKF in multiphase flow data assimilation, Computational Geosciences, 1–23, 2010.

Chen, Y. and Zhang, D.: Data assimilation for transient flow in geologic formations via ensemble Kalman filter, Adv. Water Resour., 29, 1107–1122, 2006.

Deutsch, C. V. and Journel, A. G.: GSLIB, Geostatistical Software Library and User's Guide, 2nd Edn., Oxford University Press, New York, 1998.

Devegowda, D., Arroyo-Negrete, E., and Datta-Gupta, A.: Flow relevant covariance localization during dynamic data assimilation using EnKF, Adv. Water Resour., 33, 129–145, 2010.

Evensen, G.: The ensemble Kalman filter: Theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, 2003.

Fern{à}ndez-Garcia, D., Trinchero, P., and Sanchez-Vila, X.: Conditional stochastic mapping of transport connectivity, Water Resour. Res., 46, W10515, https://doi.org/10.1029/2009WR008533, 2010.

Feyen, L. and Caers, J.: Multiple-point geostatistics: a powerful tool to improve groundwater flow and transport predictions in multi-modal formations, Geostatistics for Environmental Applications, Springer, Berlin, Heidelberg, New York, 197–208, 2005.

Fu, J. and G{ó}mez-Hern{á}ndez, J. J.: A Blocking Markov Chain Monte Carlo Method for Inverse Stochastic Hydrogeological Modeling, Math. Geosci., 41, 105–128, 2009.

Gaspari, G. and Cohn, S.: Construction of correlation functions in two and three dimensions, Q. J. Roy. Meteorol. Soc., 125, 723–757, 1999.

Gómez-Hernández, J. J. and Journel, A. G.: Joint sequential simulation of multi-Gaussian fields, Geostat. Troia, 92, 85–94, 1993.

Gómez-Hernández, J. J. and Srivastava, R. M.: ISIM3D: An ANSI-C Three Dimensional Multiple Indicator Conditional Simulation Program, Comput. Geosci., 16, 395–440, 1990.

G{ó}mez-Hern{á}ndez, J. and Wen, X.: To be or not to be multi-Gaussian? A reflection on stochastic hydrogeology, Adv. Water Resour., 21, 47–61, 1998.

Gómez-Hernández, J. J., Sahuquillo, A., and Capilla, J. E.: Stochastic Simulation of Transmissivity Fields Conditional to Both Transmissivity and Piezometric Data, 1, {T}heory, J. Hydrol., 203, 162–174, 1997.

Goovaerts, P.: Geostatistics for natural resources evaluation, Oxford University Press, USA, 1997.

Hamill, T., Whitaker, J., and Snyder, C.: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter, Mon. Weather Rev., 129, 2776–2790, 2001.

Harbaugh, A. W., Banta, E. R., Hill, M. C., and McDonald, M. G.: MODFLOW-2000, the U.S. Geological Survey modular ground-water model, U.S. Geological Survey, Branch of Information Services, Reston, VA, Denver, CO, 2000.

Hendricks Franssen, H. and Kinzelbach, W.: Real-time groundwater flow modeling with the Ensemble Kalman Filter: Joint estimation of states and parameters and the filter inbreeding problem, Water Resour. Res., 44, W09408, https://doi.org/10.1029/2007WR006505, 2008.

Hendricks Franssen, H. and Kinzelbach, W.: Ensemble Kalman filtering versus sequential self-calibration for inverse modelling of dynamic groundwater flow systems, J. Hydrol., 365, 261–274, 2009.

Hendricks Franssen, H., G{ó}mez-Hern{á}ndez, J., and Sahuquillo, A.: Coupled inverse modelling of groundwater flow and mass transport and the worth of concentration data, J. Hydrol., 281, 281–295, 2003.

Hendricks Franssen, H., Alcolea, A., Riva, M., Bakr, M., van der Wiel, N., Stauffer, F., and Guadagnini, A.: A comparison of seven methods for the inverse modelling of groundwater flow. Application to the characterisation of well catchments, Adv. Water Resour., 32, 851–872, 2009.

Hendricks Franssen, H. H. P., Kaiser, U. K., Bauser, G., Stauffer, F., Mueller, R., and Kinzelbach, W.: Operational real-time modeling with EnKF of variably saturated subsurface flow including stream-aquifer interaction and parameter updating, Water Resour. Res., 47, W02532, https://doi.org/10.1029/2010WR009480, 2011.

Hu, L. Y.: Gradual Deformation and Iterative Calibration of Gaussian-Related Stochastic Models, Math. Geol., 32, 87–108, 2000.

Huang, C., Hu, B. X., Li, X., and Ye, M.: Using data assimilation method to calibrate a heterogeneous conductivity field and improve solute transport prediction with an unknown contamination source, Stoch. Environ. Res. Risk Assess., 23, 1155–1167, 2008.

Kerrou, J., Renard, P., Hendricks Franssen, H., and Lunati, I.: Issues in characterizing heterogeneity and connectivity in non-multiGaussian media, Adv. Water Resour., 31, 147–159, 2008.

Knudby, C. and Carrera, J.: On the relationship between indicators of geostatistical, flow and transport connectivity, Adv. Water Resour., 28, 405–421, 2005.

LaVenue, A. M., Ramarao, B. S., {d}e Marsily, G., and Marietta, M. G.: Pilot point methodology for automated calibration of an ensemble of conditionally simulated transmissivity fields, 2, {A}pplication, Water Resour. Res., 31, 495–516, 1995.

Lee, S., Carle, S., and Fogg, G.: Geologic heterogeneity and a comparison of two geostatistical models: Sequential Gaussian and transition probability-based geostatistical simulation, Adv. Water Resour., 30, 1914–1932, 2007.

Li, L., Zhou, H., and Gómez-Hernández, J. J.: Steady-state groundwater flow modeling with full tensor conductivities using finite differences, Comput. Geosci., 36, 1211–1223, https://doi.org/10.1016/j.cageo.2010.04.002, 2010.

Li, L., Zhou, H., and Gómez-Hernández, J. J.: A comparative study of three-dimensional hydrualic conductivity upscaling at the Macrodispersion Experiment (MADE) site, Columbus Air Force Base, Mississippi (USA), J. Hydrol., 404, 278–293, https://doi.org/10.1016/j.jhydrol.2011.05.001, 2011a.

Li, L., Zhou, H., and Gómez-Hernández, J. J.: Transport Upscaling Using Multi-rate Mass Transfer in Three-dimensional Highly Heterogeneous Porous Media, Adv. Water Resour., 34, 478–489, https://doi.org/10.1016/j.advwatres.2011.01.001, 2011b.

Li, L., Zhou, H., Gómez-Hernández, J. J., and Hendricks Franssen, H.: Jointly Mapping Hydraulic Conductivity and Porosity by Assimilating Concentration Data via Ensemble Kalman Filter, J. Hydrol., https://doi.org/10.1016/j.jhydrol.2012.01.037, in press, 2012a.

Li, L., Zhou, H., Hendricks Franssen, H., and Gómez-Hernández, J. J.: Modeling transient flow by coupling Ensemble Kalman Filtering and upscaling, Water Resour. Res., 48, W01537, https://doi.org/10.1029/2010WR010214, 2012b.

Mariethoz, G., Renard, P., and Caers, J.: Bayesian inverse problem and optimization with iterative spatial resampling, Water Resour. Res., 46, W11530, https://doi.org/10.1029/2010WR009274, 2010a.

Mariethoz, G., Renard, P., and Straubhaar, J.: The Direct Sampling method to perform multiple-point geostatistical simulations, Water Resour. Res., 46, W11536, https://doi.org/10.1029/2008WR007621, 2010b.

McLaughlin, D. and Townley, L.: A reassessment of the groundwater inverse problem, Water Resour. Res., 32, 1131–1161, 1996.

Nan, T. and Wu, J.: Groundwater parameter estimation using the ensemble Kalman filter with localization, Hydrogeol. J., 19, 1–15, 2010.

Nowak, W.: Best unbiased ensemble linearization and the quasi-linear Kalman ensemble generator, Water Resour. Res., 45, W04431, https://doi.org/10.1029/2008WR007328, 2009.

Oliver, D., Cunha, L., and Reynolds, A.: Markov chain Monte Carlo methods for conditioning a permeability field to pressure data, Math. Geol., 29, 61–91, 1997.

Pardo-Ig{ú}zquiza, E. and Dowd, P.: CONNEC3D: a computer program for connectivity analysis of 3D random set models, Comput. Geosci., 29, 775–785, 2003.

Ramarao, B. S., LaVenue, A. M., {d}e Marsily, G., and Marietta, M. G.: Pilot point methodology for automated calibration of an ensemble of conditionally simulated transmissivity fields, 1, {T}heory and computational experiments, Water Resour. Res., 31, 475–493, 1995.

Reichle, R., Walker, J., Koster, R., and Houser, P.: Extended versus ensemble Kalman filtering for land data assimilation, J. Hydrometeorol., 3, 728–740, 2002.

Sahuquillo, A., Capilla, J. E., Gómez-Hernández, J. J., and Andreu, J.: Conditional simulation of transmissivity fields honouring piezometric head data, in: Hydraulic Engineering Software IV, Fluid Flow Modeling, edited by: Blair, W. R. and Cabrera, E., vol. II, Elsevier Applied Science, London, UK, 201–214, 1992.

Strebelle, S.: Conditional simulation of complex geological structures using multiple-point statistics, Math. Geol., 34, 1–21, 2002.

Sun, A. Y., Morris, A. P., and Mohanty, S.: Sequential updating of multimodal hydrogeologic parameter fields using localization and clustering techniques, Water Resour. Res., 45, W07424, https://doi.org/10.1029/2008WR007443, 2009.

Wen, X. and G{ó}mez-Hern{á}ndez, J.: Numerical modeling of macrodispersion in heterogeneous media: a comparison of multi-Gaussian and non-multi-Gaussian models, J. Contam. Hydrol., 30, 129–156, 1998.

Wen, X., Deutsch, C., and Cullick, A.: Construction of geostatistical aquifer models integrating dynamic flow and tracer data using inverse technique, J. Hydrol., 255, 151–168, 2002.

Wen, X. H. and Chen, W.: Real-time reservoir model updating using ensemble Kalman filter, in: SPE reservoir simulation symposium, 2005.

Western, A., Bloschl, G., and Grayson, R.: Toward capturing hydrologically significant connectivity in spatial patterns, Water Resour. Res., 37, 83–97, 2001.

Wu, J., Boucher, A., and Zhang, T.: A SGeMS code for pattern simulation of continuous and categorical variables: FILTERSIM, Comput. Geosci., 34, 1863–1876, 2008.

Yeh, W.: Review of parameter identification procedures in groundwater hydrology: The inverse problem, Water Resour. Res., 22, 95–108, 1986.

Zhou, H., Li, L., and Gómez-Hernández, J. J.: Three-dimensional hydraulic conductivity upscaling in groundwater modelling, Comput. Geosci., 36, 1224–1235, https://doi.org/10.1016/j.cageo.2010.03.008, 2010.

Zhou, H., Gómez-Hernández, J. J., Hendricks Franssen, H., and Li, L.: An approach to handling non-Gaussianity of parameters and state variables in Ensemble Kalman Filtering, Adv. Water Resour., 34, 844–864, https://doi.org/10.1016/j.advwatres.2011.04.014, 2011.

Zhou, H., Gómez-Hernández, J. J., and Li, L.: A pattern search based inverse method, Water Resour. Res., https://doi.org/10.1029/2011WR011195, in press, 2012a.

Zhou, H., Gómez-Hernández, J. J., and Li, L.: Inverse methods in hydrogeology: trajectory and future trends, J. Hydrol., in review, 2012b.

Zhou, H., Li, L., Gómez-Hernández, J. J., and Hendricks Franssen, H.: Pattern identification in bimodal aquifer by normal score Ensemble Kalman Filter, Math. Geosci., 44, 169–185, https://doi.org/10.1007/s11004-011-9372-3, 2012c.

Zimmerman, D., De Marsily, G., Gotway, C., Marietta, M., Axness, C., Beauheim, R., Bras, R., Carrera, J., Dagan, G., Davies, P., Gallegos, P., Galli, A., Gomez-Hernandez, J., Grindrod, P., Gutjahr, A., Kitanidis, P., Lavenue, A., McLaughlin, D., Neuman, S., RamaRao, B., Ravenne, C., and Rubin, Y.: A comparison of seven geostatistically based inverse approaches to estimate transmissivities for modeling advective transport by groundwater flow, Water Resour. Res., 34, 1373–1413, 1998.

Zinn, B. and Harvey, C. F.: When good statistical models of aquifer heterogeneity go bad: A comparison of flow, dispersion, and mass transfer in connected and multivariate Gaussian hydraulic conductivity fields, Water Resour. Res., 39, 1051, https://doi.org/10.1029/2001WR001146, 2003.

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem