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Contaminant Spill in a Sandbox with Non-Gaussian Conductivities: Simultaneous Identification by the Restart Normal-Score Ensemble Kalman Filter

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Contaminant Spill in a Sandbox with Non-Gaussian Conductivities: Simultaneous Identification by the Restart Normal-Score Ensemble Kalman Filter

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Chen, Z.; Xu, T.; Gómez-Hernández, JJ.; Zanini, A. (2021). Contaminant Spill in a Sandbox with Non-Gaussian Conductivities: Simultaneous Identification by the Restart Normal-Score Ensemble Kalman Filter. Mathematical Geosciences. 53(7):1587-1615. https://doi.org/10.1007/s11004-021-09928-y

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Título: Contaminant Spill in a Sandbox with Non-Gaussian Conductivities: Simultaneous Identification by the Restart Normal-Score Ensemble Kalman Filter
Autor: Chen, Zi Xu, Teng Gómez-Hernández, J. Jaime Zanini, Andrea
Entidad UPV: Universitat Politècnica de València. Instituto Universitario de Ingeniería del Agua y del Medio Ambiente - Institut Universitari d'Enginyeria de l'Aigua i Medi Ambient
Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient
Fecha difusión:
Resumen:
[EN] The joint identification of the parameters defining a contaminant source and the heterogeneous distribution of the hydraulic conductivities of the aquifer where the contamination took place is a difficult task. Previous ...[+]
Palabras clave: Inverse modeling , Forensic hydrogeology , Data assimilation , Sandbox
Derechos de uso: Reserva de todos los derechos
Fuente:
Mathematical Geosciences. (issn: 1874-8961 )
DOI: 10.1007/s11004-021-09928-y
Editorial:
Springer-Verlag
Versión del editor: https://doi.org/10.1007/s11004-021-09928-y
Código del Proyecto:
info:eu-repo/grantAgreement/Fundamental Research Funds for the Central Universities//B200201015/
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109131RB-I00/ES/APRENDIZAJE AUTOMATICO PARA HIDROGEOLOGOS FORENSES/
info:eu-repo/grantAgreement/JPDE//B19052/
info:eu-repo/grantAgreement/Gobierno de España//PRX17%2F00150/
Agradecimientos:
Financial support to carry out this work was received from the Spanish Ministry of Science and Innovation through project PID2019-109131RB-I00, and from the Spanish Ministry of Education through project PRX17/00150. Teng ...[+]
Tipo: Artículo

References

Amirabdollahian M, Datta B (2014) Identification of pollutant source characteristics under uncertainty in contaminated water resources systems using adaptive simulated anealing and fuzzy logic. Int J GEOMATE 6(1):757–762

Anderson JL (2007) An adaptive covariance inflation error correction algorithm for ensemble filters. Tellus, Ser A: Dynam Meteorol Oceanogr 59(2):210–224. https://doi.org/10.1111/j.1600-0870.2006.00216.x

Aral MM, Guan J, Maslia ML (2001) Identification of contaminant source location and release history in aquifers. J Hydrol Eng 6(3):225–234. https://doi.org/10.1061/(ASCE)1084-0699(2001)6:3(225) [+]
Amirabdollahian M, Datta B (2014) Identification of pollutant source characteristics under uncertainty in contaminated water resources systems using adaptive simulated anealing and fuzzy logic. Int J GEOMATE 6(1):757–762

Anderson JL (2007) An adaptive covariance inflation error correction algorithm for ensemble filters. Tellus, Ser A: Dynam Meteorol Oceanogr 59(2):210–224. https://doi.org/10.1111/j.1600-0870.2006.00216.x

Aral MM, Guan J, Maslia ML (2001) Identification of contaminant source location and release history in aquifers. J Hydrol Eng 6(3):225–234. https://doi.org/10.1061/(ASCE)1084-0699(2001)6:3(225)

Atmadja J, Bagtzoglou AC (2001) State of the art report on mathematical methods for groundwater pollution source identification. Environ Forens 2(3):205–214. https://doi.org/10.1006/enfo.2001.0055

Ayvaz MT (2016) A hybrid simulation-optimization approach for solving the areal groundwater pollution source identification problems. J Hydrol 538:161–176. https://doi.org/10.1016/j.jhydrol.2016.04.008

Bagtzoglou AC, Atmadja J (2005) Mathematical methods for hydrologic inversion: the case of pollution source identification. Water Pollut 5:65–96. https://doi.org/10.1007/b11442

Bagtzoglou AC, Dougherty DE, Tompson AFB (1992) Application of particle methods to reliable identification of groundwater pollution sources. Water Resour Manage 6(1):15–23. https://doi.org/10.1007/BF00872184

Bauser HH, Berg D, Klein O, Roth K (2018) Inflation method for ensemble Kalman filter in soil hydrology. Hydrol Earth Syst Sci 22(9):4921–4934. https://doi.org/10.5194/hess-22-4921-2018

Bear J (1972) Dynamics of Fluids in Porous Media. American Elsevier, Amsterdam

Butera I, Tanda MG, Zanini A (2013) Simultaneous identification of the pollutant release history and the source location in groundwater by means of a geostatistical approach. Stochast Environ Res Risk Assess 27(5):1269–1280. https://doi.org/10.1007/s00477-012-0662-1

Camporese M, Cassiani G, Deiana R, Salandin P (2011) Assessment of local hydraulic properties from electrical resistivity tomography monitoring of a three-dimensional synthetic tracer test experiment. Water Resour Res 47(12):1–15. https://doi.org/10.1029/2011WR010528

Capilla JE, Rodrigo J, Gómez-Hernández JJ (1999) Simulation of non-gaussian transmissivity fields honoring piezometric data and integrating soft and secondary information. Math Geol 31(7):907–927

Chang H, Zhang D, Lu Z (2010) History matching of facies distribution with the EnKF and level set parameterization. J Comput Phys 229(20):8011–8030. https://doi.org/10.1016/j.jcp.2010.07.005

Chen Y, Zhang D (2006) Data assimilation for transient flow in geologic formations via ensemble Kalman filter. Adv Water Res 29(8):1107–1122. https://doi.org/10.1016/j.advwatres.2005.09.007

Chen Z, Gómez-Hernández JJ, Xu T, Zanini A (2018) Joint identification of contaminant source and aquifer geometry in a sandbox experiment with the restart ensemble kalman filter. J Hydrol 564:1074–1084

Citarella D, Cupola F, Tanda MG, Zanini A (2015) Evaluation of dispersivity coefficients by means of a laboratory image analysis. J Contam Hydrol 172:10–23. https://doi.org/10.1016/j.jconhyd.2014.11.001

Crestani E, Camporese M, Baú D, Salandin P (2012) Ensemble Kalman filter versus ensemble smoother for assessing hydraulic conductivity via tracer test data assimilation. Hydrol Earth Syst Sci Discuss 9(11):13083–13115. https://doi.org/10.5194/hessd-9-13083-2012

Cupola F, Tanda MG, Zanini A (2015) Laboratory sandbox validation of pollutant source location methods. Stochast Environ Res Risk Assess 29(1):169–182. https://doi.org/10.1007/s00477-014-0869-4

Datta B, Chakrabarty D, Dhar A (2009) Simultaneous identification of unknown groundwater pollution sources and estimation of aquifer parameters. J Hydrol 376(1–2):48–57. https://doi.org/10.1016/j.jhydrol.2009.07.014

Evensen G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res 99(C5):10143. https://doi.org/10.1029/94JC00572

Feyen L, Gómez-Hernández JJ, Ribeiro P Jr, Beven KJ, De Smedt F (2003a) A Bayesian approach to stochastic capture zone delineation incorporating tracer arrival times, conductivity measurements, and hydraulic head observations. Water Resour Res 39(5):1126. https://doi.org/10.1029/2002WR001544

Feyen L, Ribeiro P Jr, Gomez-Hernandez J, Beven KJ, De Smedt F (2003b) Bayesian methodology for stochastic capture zone delineation incorporating transmissivity measurements and hydraulic head observations. J Hydrol 271(1–4):156–170

Franssen HH, Gómez-Hernández J (2002) 3d inverse modelling of groundwater flow at a fractured site using a stochastic continuum model with multiple statistical populations. Stochast Environ Res Risk Assess 16(2):155–174

Gómez-Hernández J, Wen XH (1994) Probabilistic assessment of travel times in groundwater modeling. Stochast Hydrol Hydraul 8(1):19–55

Gómez-Hernández J, Franssen HJH, Sahuquillo A (2003) Stochastic conditional inverse modeling of subsurface mass transport: a brief review and the self-calibrating method. Stochast Environ Res Risk Assess 17(5):319–328

Gómez-Hernández JJ, Wen XH (1998) To be or not to be multi-Gaussian? A reflection on stochastic hydrogeology. Adv Water Resour 21(1):47–61. https://doi.org/10.1016/S0309-1708(96)00031-0

Gorelick SM, Evans B, Remson I (1983) Identifying sources of groundwater pollution: an optimization approach. Water Resour Res 19(3):779–790. https://doi.org/10.1029/WR019i003p00779

Greybush SJ, Kalnay E, Miyoshi T, Ide K, Hunt BR (2011) Balance and ensemble kalman filter localization techniques. Mon Weather Rev 139(2):511–522

Hendricks Franssen HJ, 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 Resour Res 44(9):1–21. https://doi.org/10.1029/2007WR006505

Hendricks Franssen HJ, Kinzelbach W (2009) Ensemble kalman filtering versus sequential self-calibration for inverse modelling of dynamic groundwater flow systems. J Hydrol 365(3–4):261–274

Houtekamer PL, Mitchell HL (2001) A sequential ensemble kalman filter for atmospheric data assimilation. Mon Weather Rev 129(1):123–137. https://doi.org/10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2

Jafarpour B, Khodabakhshi M (2011) A Probability Conditioning Method (PCM) for nonlinear flow data integration into multipoint statistical facies simulation. Math Geosci 43(2):133–164. https://doi.org/10.1007/s11004-011-9316-y

Journel A, Isaaks E (1984) Conditional indicator simulation: application to a saskatchewan uranium deposit. J Int Assoc Math Geol 16(7):685–718

Journel AG, Gomez-Hernandez JJ et al (1993) Stochastic imaging of the wilmington clastic sequence. SPE format Evaluat 8(01):33–40

Knudby C, Carrera J (2005) On the relationship between indicators of geostatistical, flow and transport connectivity. Adv Water Resour 28(4):405–421. https://doi.org/10.1016/j.advwatres.2004.09.001

Koch J, Nowak W (2016) Identification of contaminant source architectures—A statistical inversion that emulates multiphase physics in a computationally practicable manner. Water Res Res 52(2):1009–1025. https://doi.org/10.1002/2015WR017894

Kumar D, Srinivasan S (2019) Ensemble-based assimilation of nonlinearly related dynamic data in reservoir models exhibiting non-gaussian characteristics. Math Geosci 51(1):75–107. https://doi.org/10.1007/s11004-018-9762-x

Kumar D, Srinivasan S (2020) Indicator-based data assimilation with multiple-point statistics for updating an ensemble of models with non-Gaussian parameter distributions. Adv Water Resour 141:103611. https://doi.org/10.1016/j.advwatres.2020.103611

Li H, Kalnay E, Miyoshi T (2009) Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter. Q J R Meteorol Soc 135(639):523–533. https://doi.org/10.1002/qj.371

Li L, Zhou H, Gómez-Hernández JJ (2011) A comparative study of three-dimensional hydraulic conductivity upscaling at the macro-dispersion experiment (made) site, Columbus air force base, Mississippi (USA). J Hydrol 404(3–4):278–293

Li L, Zhou H, Gómez-Hernández JJ, Hendricks Franssen HJ (2012a) Jointly mapping hydraulic conductivity and porosity by assimilating concentration data via ensemble Kalman filter. J Hydrol 428–429:152–169. https://doi.org/10.1016/j.jhydrol.2012.01.037

Li L, Zhou H, Hendricks Franssen HJ, Gómez-Hernández JJ (2012b) Groundwater flow inverse modeling in non-MultiGaussian media: performance assessment of the normal-score Ensemble Kalman Filter. Hydrol Earth Syst Sci 16(2):573–590. https://doi.org/10.5194/hess-16-573-2012

Li L, Zhou H, Hendricks Franssen HJ, Gómez-Hernández JJ (2012c) Modeling transient groundwater flow by coupling ensemble kalman filtering and upscaling. Water Resour Res 48(1):W01537. https://doi.org/10.1029/2010WR010214

Liang X, Zheng X, Zhang S, Wu G, Dai Y, Li Y (2011) Maximum likelihood estimation of inflation factors on error covariance matrices for ensemble kalman filter assimilation. Q J R Meteorol Soc 138(662):263–273

Liang X, Zheng X, Zhang S, Wu G, Dai Y, Li Y (2012) Maximum likelihood estimation of inflation factors on error covariance matrices for ensemble Kalman filter assimilation. Q J R Meteorol Soc 138(662):263–273. https://doi.org/10.1002/qj.912

Mahar PS, Datta B (2000) Identification of pollution sources in transient groundwater systems. Water Resour Manage 14(3):209–227. https://doi.org/10.1023/A:1026527901213

McDonald JM, Harbaugh AW (1988) A modular three-dimensional finite-difference flow model. Techniq Water Resour Investig US Geol Surv Book 6:586. https://doi.org/10.1016/0022-1694(86)90106-X

Michalak AM, Kitanidis PK (2004) Estimation of historical groundwater contaminant distribution using the adjoint state method applied to geostatistical inverse modeling. Water Resour Res. https://doi.org/10.1029/2004WR003214

Mirghani BY, Mahinthakumar KG, Tryby ME, Ranjithan RS, Zechman EM (2009) A parallel evolutionary strategy based simulation-optimization approach for solving groundwater source identification problems. Adv Water Resour 32(9):1373–1385. https://doi.org/10.1016/j.advwatres.2009.06.001

Neupauer RM, Wilson JL (1999) Adjoint method for obtaining backward-in-time location and travel time probabilities of a conservative groundwater contaminant. Water Resour Res 35(11):3389–3398. https://doi.org/10.1029/1999WR900190

Sun AY, Painter SL, Wittmeyer GW (2006) A constrained robust least squares approach for contaminant release history identification. Water Res Res 42(4):1–13. https://doi.org/10.1029/2005WR004312

Sun AY, Morris AP, Mohanty S (2009) Sequential updating of multimodal hydrogeologic parameter fields using localization and clustering techniques. Water Resour Res 45(7):1–15. https://doi.org/10.1029/2008WR007443

Wagner BJ (1992) Simultaneous parameter estimation and contaminant source characterization for coupled groundwater flow and contaminant transport modelling. J Hydrol 135(1–4):275–303. https://doi.org/10.1016/0022-1694(92)90092-A

Wang X, Bishop CH (2003) A comparison of breeding and ensemble transform kalman filter ensemble forecast schemes. J Atmos Sci 60(9):1140–1158. https://doi.org/10.1175/1520-0469(2003)060<1140:ACOBAE>2.0.CO;2

Wen XH, Chen WH (2005) Some practical issues on real-time reservoir model updating using ensemble Kalman filter. Paper presented at the International Petroleum Technology Conference, Doha, Qatar, November 2005. Paper Number: IPTC-11024-MS. https://doi.org/10.2523/IPTC-11024-MS

Wen XH, Chen WH (2006) Real-time reservoir model updating using ensemble Kalman filter with confirming option. SPE J 11(4):431–442. https://doi.org/10.2118/92991-PA

Wen XH, Jaime Gómez-Hernandez J, Capilla JE, Sahuquillo A (1996) Significance of conditioning to piezometric head data for predictions of mass transport in groundwater modeling. Math Geol 28(7):951–968. https://doi.org/10.1007/BF02066011

Wen XH, Capilla JE, Deutsch C, Gómez-Hernández J, Cullick A (1999) A program to create permeability fields that honor single-phase flow rate and pressure data. Comp Geosci 25(3):217–230

Woodbury AD, Ulrych TJ (1996) Minimum relative entropy inversion: theory and application to recovering the release history of a groundwater contaminant. Water Resour Res 32(9):2671–2681

Xu T, Gómez-Hernández JJ (2016) Joint identification of contaminant source location, initial release time, and initial solute concentration in an aquifer via ensemble Kalman filtering. Water Resour Res. https://doi.org/10.1002/2014WR016618.Received

Xu T (2017) Gómez-Hernández JJ (2018) Simultaneous identification of a contaminant source and hydraulic conductivity via the restart normal-score ensemble Kalman filter. Adv Water Resour 112:106–123. https://doi.org/10.1016/j.advwatres.2017.12.011

Xu T, Gómez-Hernández JJ, Zhou H, Li L (2013) The power of transient piezometric head data in inverse modeling: an application of the localized normal-score EnKF with covariance inflation in a heterogenous bimodal hydraulic conductivity field. Adv Water Res 54:100–118. https://doi.org/10.1016/j.advwatres.2013.01.006

Yeh HD, Chang TH, Lin YC (2007) Groundwater contaminant source identification by a hybrid heuristic approach. Water Resour Res 43(9):1–16. https://doi.org/10.1029/2005WR004731

Zheng C, Wang PP (1999) MT3DMS: A Modular Three-Dimensional Multispecies Transport Model (December):219

Zheng X (2009) An adaptive estimation of forecast error covariance parameters for Kalman filtering data assimilation. Adv Atmos Sci 26(1):154–160. https://doi.org/10.1007/s00376-009-0154-5

Zhou H, Gómez-Hernández JJ, Hendricks Franssen HJ, Li L (2011) An approach to handling non-Gaussianity of parameters and state variables in ensemble Kalman filtering. Adv Water Resour 34(7):844–864. https://doi.org/10.1016/j.advwatres.2011.04.014

Zhou H, Gómez-Hernández JJ, Li L (2012a) A pattern-search-based inverse method. Water Resour Res 48(3):W03505. https://doi.org/10.1029/2011WR011195

Zhou H, Li L, Franssen HJH, Gómez-Hernández JJ (2012b) Pattern recognition in a bimodal aquifer using the normal-score ensemble kalman filter. Math Geosci 44(2):169–185

Zhou H, Gómez-Hernández JJ, Li L (2014) Inverse methods in hydrogeology: evolution and recent trends. Adv Water Resour 63:22–37. https://doi.org/10.1016/j.advwatres.2013.10.014

Zinn B, Harvey CF (2003) 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(3):137–147. https://doi.org/10.1029/2001WR001146

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