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A pattern-search-based inverse method

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A pattern-search-based inverse method

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dc.contributor.author Zhou ., Haiyan es_ES
dc.contributor.author Gómez-Hernández, J. Jaime es_ES
dc.contributor.author Li ., Liangping es_ES
dc.date.accessioned 2015-02-12T12:20:21Z
dc.date.available 2015-02-12T12:20:21Z
dc.date.issued 2012
dc.identifier.issn 0043-1397
dc.identifier.uri http://hdl.handle.net/10251/46948
dc.description.abstract Uncertainty in model predictions is caused to a large extent by the uncertainty in model parameters, while the identification of model parameters is demanding because of the inherent heterogeneity of the aquifer. A variety of inverse methods has been proposed for parameter identification. In this paper we present a novel inverse method to constrain the model parameters (hydraulic conductivities) to the observed state data (hydraulic heads). In the method proposed we build a conditioning pattern consisting of simulated model parameters and observed flow data. The unknown parameter values are simulated by pattern searching through an ensemble of realizations rather than optimizing an objective function. The model parameters do not necessarily follow a multi-Gaussian distribution, and the nonlinear relationship between the parameter and the response is captured by the multipoint pattern matching. The algorithm is evaluated in two synthetic bimodal aquifers. The proposed method is able to reproduce the main structure of the reference fields, and the performance of the updated model in predicting flow and transport is improved compared with that of the prior model. es_ES
dc.description.sponsorship The authors gratefully acknowledge the financial support from the Ministry of Science and Innovation, project CGL2011-23295. The first author also acknowledges the scholarship provided by the China Scholarship Council (CSC [2007] 3020). The authors would like to thank Gregoire Mariethoz (University of New South Wales) and Philippe Renard (University of Neuchatel) for their enthusiastic help in answering questions about the direct sampling algorithm. Gregoire Mariethoz and two anonymous reviewers are also thanked for their comments during the reviewing process, which helped improving the final paper. en_EN
dc.language Inglés es_ES
dc.publisher American Geophysical Union (AGU) es_ES
dc.relation.ispartof Water Resources Research es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Flow and transport es_ES
dc.subject Flow data es_ES
dc.subject Hydraulic heads es_ES
dc.subject Identification of model es_ES
dc.subject Inverse methods es_ES
dc.subject Main structure es_ES
dc.subject Model parameters es_ES
dc.subject Model prediction es_ES
dc.subject Multipoint es_ES
dc.subject Non-linear relationships es_ES
dc.subject Objective functions es_ES
dc.subject Reference field es_ES
dc.subject Simulated model es_ES
dc.subject.classification INGENIERIA HIDRAULICA es_ES
dc.title A pattern-search-based inverse method es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1029/2011WR011195
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//CGL2011-23295/ES/MODELACION ESTOCASTICA INVERSA FUERA DE LO NORMAL/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/CSC//[2007]3020/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient es_ES
dc.description.bibliographicCitation Zhou ., H.; Gómez-Hernández, JJ.; Li ., L. (2012). A pattern-search-based inverse method. Water Resources Research. 48(3):1-17. https://doi.org/10.1029/2011WR011195 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1029/2011WR011195 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 17 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 48 es_ES
dc.description.issue 3 es_ES
dc.relation.senia 233949
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.contributor.funder China Scholarship Council es_ES
dc.description.references Alcolea, A., & Renard, P. (2010). Blocking Moving Window algorithm: Conditioning multiple-point simulations to hydrogeological data. Water Resources Research, 46(8). doi:10.1029/2009wr007943 es_ES
dc.description.references Alcolea, A., Carrera, J., & Medina, A. (2006). Pilot points method incorporating prior information for solving the groundwater flow inverse problem. Advances in Water Resources, 29(11), 1678-1689. doi:10.1016/j.advwatres.2005.12.009 es_ES
dc.description.references Arpat, G. B., & Caers, J. (2007). Conditional Simulation with Patterns. Mathematical Geology, 39(2), 177-203. doi:10.1007/s11004-006-9075-3 es_ES
dc.description.references Caers , J. 2002 Geostatistical history matching under training-image based geological model constraints es_ES
dc.description.references Caers, J. (2003). Efficient gradual deformation using a streamline-based proxy method. Journal of Petroleum Science and Engineering, 39(1-2), 57-83. doi:10.1016/s0920-4105(03)00040-8 es_ES
dc.description.references Caers, J., & Hoffman, T. (2006). The Probability Perturbation Method: A New Look at Bayesian Inverse Modeling. Mathematical Geology, 38(1), 81-100. doi:10.1007/s11004-005-9005-9 es_ES
dc.description.references Capilla, J. E., & Llopis-Albert, C. (2009). Gradual conditioning of non-Gaussian transmissivity fields to flow and mass transport data: 1. Theory. Journal of Hydrology, 371(1-4), 66-74. doi:10.1016/j.jhydrol.2009.03.015 es_ES
dc.description.references Carrera, J., & Neuman, S. P. (1986). Estimation of Aquifer Parameters Under Transient and Steady State Conditions: 1. Maximum Likelihood Method Incorporating Prior Information. Water Resources Research, 22(2), 199-210. doi:10.1029/wr022i002p00199 es_ES
dc.description.references Chen, Y., & Zhang, D. (2006). Data assimilation for transient flow in geologic formations via ensemble Kalman filter. Advances in Water Resources, 29(8), 1107-1122. doi:10.1016/j.advwatres.2005.09.007 es_ES
dc.description.references Christiansen, L., Binning, P. J., Rosbjerg, D., Andersen, O. B., & Bauer-Gottwein, P. (2011). Using time-lapse gravity for groundwater model calibration: An application to alluvial aquifer storage. Water Resources Research, 47(6). doi:10.1029/2010wr009859 es_ES
dc.description.references De Marsily, G., Delay, F., Gonçalvès, J., Renard, P., Teles, V., & Violette, S. (2005). Dealing with spatial heterogeneity. Hydrogeology Journal, 13(1), 161-183. doi:10.1007/s10040-004-0432-3 es_ES
dc.description.references Deutsch, C. V., & Tran, T. T. (2002). FLUVSIM: a program for object-based stochastic modeling of fluvial depositional systems. Computers & Geosciences, 28(4), 525-535. doi:10.1016/s0098-3004(01)00075-9 es_ES
dc.description.references Dubuisson, M.-P., & Jain, A. K. (s. f.). A modified Hausdorff distance for object matching. Proceedings of 12th International Conference on Pattern Recognition. doi:10.1109/icpr.1994.576361 es_ES
dc.description.references Emsellem, Y., & De Marsily, G. (1971). An Automatic Solution for the Inverse Problem. Water Resources Research, 7(5), 1264-1283. doi:10.1029/wr007i005p01264 es_ES
dc.description.references Evensen, G. (2003). The Ensemble Kalman Filter: theoretical formulation and practical implementation. Ocean Dynamics, 53(4), 343-367. doi:10.1007/s10236-003-0036-9 es_ES
dc.description.references Falivene, O., Cabello, P., Arbués, P., Muñoz, J. A., & Cabrera, L. (2009). A geostatistical algorithm to reproduce lateral gradual facies transitions: Description and implementation. Computers & Geosciences, 35(8), 1642-1651. doi:10.1016/j.cageo.2008.12.003 es_ES
dc.description.references Fernàndez-Garcia, D., Illangasekare, T. H., & 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 Resources Research, 41(3). doi:10.1029/2004wr003125 es_ES
dc.description.references Feyen, L., & Caers, J. (2006). Quantifying geological uncertainty for flow and transport modeling in multi-modal heterogeneous formations. Advances in Water Resources, 29(6), 912-929. doi:10.1016/j.advwatres.2005.08.002 es_ES
dc.description.references Fu, J., & Gómez-Hernández, J. J. (2008). A Blocking Markov Chain Monte Carlo Method for Inverse Stochastic Hydrogeological Modeling. Mathematical Geosciences, 41(2), 105-128. doi:10.1007/s11004-008-9206-0 es_ES
dc.description.references Jaime Gómez-Hernánez, J., Sahuquillo, A., & Capilla, J. (1997). Stochastic simulation of transmissivity fields conditional to both transmissivity and piezometric data—I. Theory. Journal of Hydrology, 203(1-4), 162-174. doi:10.1016/s0022-1694(97)00098-x es_ES
dc.description.references Guardiano, F. B., & Srivastava, R. M. (1993). Multivariate Geostatistics: Beyond Bivariate Moments. Geostatistics Tróia ’92, 133-144. doi:10.1007/978-94-011-1739-5_12 es_ES
dc.description.references Harbaugh , A. W. E. R. Banta M. C. Hill M. G. McDonald 2000 MODFLOW-2000, the U.S. Geological Survey modular ground-water model-User guide to modularization concepts and the ground-water flow process Reston, Va. es_ES
dc.description.references Hendricks Franssen, H. J., & Kinzelbach, W. (2008). Real-time groundwater flow modeling with the Ensemble Kalman Filter: Joint estimation of states and parameters and the filter inbreeding problem. Water Resources Research, 44(9). doi:10.1029/2007wr006505 es_ES
dc.description.references Franssen, H.-J. H., Gómez-Hernández, J., & Sahuquillo, A. (2003). Coupled inverse modelling of groundwater flow and mass transport and the worth of concentration data. Journal of Hydrology, 281(4), 281-295. doi:10.1016/s0022-1694(03)00191-4 es_ES
dc.description.references Henrion, V., Caumon, G., & Cherpeau, N. (2010). ODSIM: An Object-Distance Simulation Method for Conditioning Complex Natural Structures. Mathematical Geosciences, 42(8), 911-924. doi:10.1007/s11004-010-9299-0 es_ES
dc.description.references Hoeksema, R. J., & Kitanidis, P. K. (1984). An Application of the Geostatistical Approach to the Inverse Problem in Two-Dimensional Groundwater Modeling. Water Resources Research, 20(7), 1003-1020. doi:10.1029/wr020i007p01003 es_ES
dc.description.references Hoeksema, R. J., & Kitanidis, P. K. (1985). Analysis of the Spatial Structure of Properties of Selected Aquifers. Water Resources Research, 21(4), 563-572. doi:10.1029/wr021i004p00563 es_ES
dc.description.references Hu, L. Y. (2000). Mathematical Geology, 32(1), 87-108. doi:10.1023/a:1007506918588 es_ES
dc.description.references Hu, L. Y., & Chugunova, T. (2008). Multiple-point geostatistics for modeling subsurface heterogeneity: A comprehensive review. Water Resources Research, 44(11). doi:10.1029/2008wr006993 es_ES
dc.description.references Huysmans, M., & Dassargues, A. (2009). Application of multiple-point geostatistics on modelling groundwater flow and transport in a cross-bedded aquifer (Belgium). Hydrogeology Journal, 17(8), 1901-1911. doi:10.1007/s10040-009-0495-2 es_ES
dc.description.references Jafarpour, B., & Khodabakhshi, M. (2011). A Probability Conditioning Method (PCM) for Nonlinear Flow Data Integration into Multipoint Statistical Facies Simulation. Mathematical Geosciences, 43(2), 133-164. doi:10.1007/s11004-011-9316-y es_ES
dc.description.references Journel, A., & Zhang, T. (2006). The Necessity of a Multiple-Point Prior Model. Mathematical Geology, 38(5), 591-610. doi:10.1007/s11004-006-9031-2 es_ES
dc.description.references Kerrou, J., Renard, P., Hendricks Franssen, H.-J., & Lunati, I. (2008). Issues in characterizing heterogeneity and connectivity in non-multiGaussian media. Advances in Water Resources, 31(1), 147-159. doi:10.1016/j.advwatres.2007.07.002 es_ES
dc.description.references Kwa, Franz, M. O., & Scholkopf, B. (2005). Iterative kernel principal component analysis for image modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(9), 1351-1366. doi:10.1109/tpami.2005.181 es_ES
dc.description.references Kitanidis, P. K. (2007). On stochastic inverse modeling. Geophysical Monograph Series, 19-30. doi:10.1029/171gm04 es_ES
dc.description.references Kitanidis, P. K., & Vomvoris, E. G. (1983). A geostatistical approach to the inverse problem in groundwater modeling (steady state) and one-dimensional simulations. Water Resources Research, 19(3), 677-690. doi:10.1029/wr019i003p00677 es_ES
dc.description.references Li, L., Zhou, H., Hendricks Franssen, H. J., & Gómez-Hernández, J. J. (2011). Groundwater flow inverse modeling in non-MultiGaussian media: performance assessment of the normal-score Ensemble Kalman Filter. Hydrology and Earth System Sciences Discussions, 8(4), 6749-6788. doi:10.5194/hessd-8-6749-2011 es_ES
dc.description.references Li, L., Zhou, H., & Gómez-Hernández, J. J. (2011). Transport upscaling using multi-rate mass transfer in three-dimensional highly heterogeneous porous media. Advances in Water Resources, 34(4), 478-489. doi:10.1016/j.advwatres.2011.01.001 es_ES
dc.description.references Li, L., Zhou, H., & Gómez-Hernández, J. J. (2011). A comparative study of three-dimensional hydraulic conductivity upscaling at the macro-dispersion experiment (MADE) site, Columbus Air Force Base, Mississippi (USA). Journal of Hydrology, 404(3-4), 278-293. doi:10.1016/j.jhydrol.2011.05.001 es_ES
dc.description.references Mariethoz, G., Renard, P., & Straubhaar, J. (2010). The Direct Sampling method to perform multiple-point geostatistical simulations. Water Resources Research, 46(11). doi:10.1029/2008wr007621 es_ES
dc.description.references Mariethoz, G., Renard, P., & Caers, J. (2010). Bayesian inverse problem and optimization with iterative spatial resampling. Water Resources Research, 46(11). doi:10.1029/2010wr009274 es_ES
dc.description.references Neuman, S. P. (1973). Calibration of distributed parameter groundwater flow models viewed as a multiple-objective decision process under uncertainty. Water Resources Research, 9(4), 1006-1021. doi:10.1029/wr009i004p01006 es_ES
dc.description.references Oliver, D. S., Cunha, L. B., & Reynolds, A. C. (1997). Markov chain Monte Carlo methods for conditioning a permeability field to pressure data. Mathematical Geology, 29(1), 61-91. doi:10.1007/bf02769620 es_ES
dc.description.references RamaRao, B. S., LaVenue, A. M., De Marsily, G., & Marietta, M. G. (1995). Pilot Point Methodology for Automated Calibration of an Ensemble of conditionally Simulated Transmissivity Fields: 1. Theory and Computational Experiments. Water Resources Research, 31(3), 475-493. doi:10.1029/94wr02258 es_ES
dc.description.references Rubin, Y., Chen, X., Murakami, H., & Hahn, M. (2010). A Bayesian approach for inverse modeling, data assimilation, and conditional simulation of spatial random fields. Water Resources Research, 46(10). doi:10.1029/2009wr008799 es_ES
dc.description.references Strebelle, S. (2002). Mathematical Geology, 34(1), 1-21. doi:10.1023/a:1014009426274 es_ES
dc.description.references Suzuki, S., & Caers, J. (2008). A Distance-based Prior Model Parameterization for Constraining Solutions of Spatial Inverse Problems. Mathematical Geosciences, 40(4), 445-469. doi:10.1007/s11004-008-9154-8 es_ES
dc.description.references Wen, X. H., Capilla, J. E., Deutsch, C. V., Gómez-Hernández, J. J., & Cullick, A. S. (1999). A program to create permeability fields that honor single-phase flow rate and pressure data. Computers & Geosciences, 25(3), 217-230. doi:10.1016/s0098-3004(98)00126-5 es_ES
dc.description.references Zhang, T., Switzer, P., & Journel, A. (2006). Filter-Based Classification of Training Image Patterns for Spatial Simulation. Mathematical Geology, 38(1), 63-80. doi:10.1007/s11004-005-9004-x es_ES
dc.description.references Zhou, H., Gómez-Hernández, J. J., Hendricks Franssen, H.-J., & Li, L. (2011). An approach to handling non-Gaussianity of parameters and state variables in ensemble Kalman filtering. Advances in Water Resources, 34(7), 844-864. doi:10.1016/j.advwatres.2011.04.014 es_ES
dc.description.references Zhou, H., Li, L., Hendricks Franssen, H.-J., & Gómez-Hernández, J. J. (2011). Pattern Recognition in a Bimodal Aquifer Using the Normal-Score Ensemble Kalman Filter. Mathematical Geosciences, 44(2), 169-185. doi:10.1007/s11004-011-9372-3 es_ES


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