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A pattern-search-based inverse method

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A pattern-search-based inverse method

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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

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Metadatos del ítem

Título: A pattern-search-based inverse method
Autor: Zhou ., Haiyan Gómez-Hernández, J. Jaime Li ., Liangping
Entidad UPV: Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient
Fecha difusión:
Resumen:
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 ...[+]
Palabras clave: Flow and transport , Flow data , Hydraulic heads , Identification of model , Inverse methods , Main structure , Model parameters , Model prediction , Multipoint , Non-linear relationships , Objective functions , Reference field , Simulated model
Derechos de uso: Reserva de todos los derechos
Fuente:
Water Resources Research. (issn: 0043-1397 )
DOI: 10.1029/2011WR011195
Editorial:
American Geophysical Union (AGU)
Versión del editor: http://dx.doi.org/10.1029/2011WR011195
Código del Proyecto:
info:eu-repo/grantAgreement/MICINN//CGL2011-23295/ES/MODELACION ESTOCASTICA INVERSA FUERA DE LO NORMAL/
info:eu-repo/grantAgreement/CSC//[2007]3020/
Agradecimientos:
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 ...[+]
Tipo: Artículo

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

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

Arpat, G. B., & Caers, J. (2007). Conditional Simulation with Patterns. Mathematical Geology, 39(2), 177-203. doi:10.1007/s11004-006-9075-3 [+]
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

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

Arpat, G. B., & Caers, J. (2007). Conditional Simulation with Patterns. Mathematical Geology, 39(2), 177-203. doi:10.1007/s11004-006-9075-3

Caers , J. 2002 Geostatistical history matching under training-image based geological model constraints

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

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

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

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

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

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

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

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

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

Emsellem, Y., & De Marsily, G. (1971). An Automatic Solution for the Inverse Problem. Water Resources Research, 7(5), 1264-1283. doi:10.1029/wr007i005p01264

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

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

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

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

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

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

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

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.

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

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

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

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

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

Hu, L. Y. (2000). Mathematical Geology, 32(1), 87-108. doi:10.1023/a:1007506918588

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

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

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

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

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

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

Kitanidis, P. K. (2007). On stochastic inverse modeling. Geophysical Monograph Series, 19-30. doi:10.1029/171gm04

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

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

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

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

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

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

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

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

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

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

Strebelle, S. (2002). Mathematical Geology, 34(1), 1-21. doi:10.1023/a:1014009426274

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

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

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

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

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

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