- -

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

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

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

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: ChenXuGomez-Hernandez ...
Tamaño: 3.586Mb
Formato: PDF
Descripción: Versión del Autor.
Abrir
[Cerrado]
Nombre: Chen2021_Article_ ...
Tamaño: 4.997Mb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor
dc.contributor.author Chen, Zi es_ES
dc.contributor.author Xu, Teng es_ES
dc.contributor.author Gómez-Hernández, J. Jaime es_ES
dc.contributor.author Zanini, Andrea es_ES
dc.date.accessioned 2022-09-27T18:04:00Z
dc.date.available 2022-09-27T18:04:00Z
dc.date.issued 2021-10 es_ES
dc.identifier.issn 1874-8961 es_ES
dc.identifier.uri http://hdl.handle.net/10251/186630
dc.description.abstract [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 studies have demonstrated the applicability of the restart normal-score ensemble Kalman filter (rNS-EnKF) in synthetic cases making use of sufficient hydraulic head and concentration data. This study shows an application of the same technique to a non-synthetic case under laboratory conditions and discusses the difficulties found on its application and the avenues taken to solve them. The method is first tested using a synthetic case that mimics the sandbox experiment to establish the minimum number of ensemble members and the best technique to prevent the filter collapsing. The synthetic case shows that among different techniques based on update damping and covariance inflation, the Bauser's covariance inflation method works best in preventing filter collapse. Its application to the sandbox data shows that the rNS-EnKF can benefit from Bauser's inflation to reduce the number of ensemble realizations substantially in comparison with a filter without inflation, arriving at a good joint identification of both the contaminant source and the spatial heterogeneity of the conductivities. es_ES
dc.description.sponsorship 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 Xu also acknowledges the financial support from the Fundamental Research Funds for the Central Universities (B200201015) and Jiangsu Specially-Appointed Professor Program (B19052). And the authors would like to thank University of Parma for providing the experimental equipment. Part of the work was performed during a stay of the third author at the University of Parma under the TeachinParma initiative, co-funded by Fondazione Cariparma and University of Parma. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Mathematical Geosciences es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Inverse modeling es_ES
dc.subject Forensic hydrogeology es_ES
dc.subject Data assimilation es_ES
dc.subject Sandbox es_ES
dc.subject.classification INGENIERIA HIDRAULICA es_ES
dc.title Contaminant Spill in a Sandbox with Non-Gaussian Conductivities: Simultaneous Identification by the Restart Normal-Score Ensemble Kalman Filter es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11004-021-09928-y es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Fundamental Research Funds for the Central Universities//B200201015/ es_ES
dc.relation.projectID 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/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/JPDE//B19052/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/ME//PRX17%2F00150/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Ingeniería del Agua y del Medio Ambiente - Institut Universitari d'Enginyeria de l'Aigua i Medi Ambient 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 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 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s11004-021-09928-y es_ES
dc.description.upvformatpinicio 1587 es_ES
dc.description.upvformatpfin 1615 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 53 es_ES
dc.description.issue 7 es_ES
dc.relation.pasarela S\444744 es_ES
dc.contributor.funder Ministerio de Educación es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder Jiangsu Provincial Department of Education es_ES
dc.contributor.funder Fundamental Research Funds for the Central Universities es_ES
dc.description.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 es_ES
dc.description.references 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 es_ES
dc.description.references 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) es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references Bear J (1972) Dynamics of Fluids in Porous Media. American Elsevier, Amsterdam es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references Gómez-Hernández J, Wen XH (1994) Probabilistic assessment of travel times in groundwater modeling. Stochast Hydrol Hydraul 8(1):19–55 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 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. Math Geosci 43(2):133–164. https://doi.org/10.1007/s11004-011-9316-y es_ES
dc.description.references Journel A, Isaaks E (1984) Conditional indicator simulation: application to a saskatchewan uranium deposit. J Int Assoc Math Geol 16(7):685–718 es_ES
dc.description.references Journel AG, Gomez-Hernandez JJ et al (1993) Stochastic imaging of the wilmington clastic sequence. SPE format Evaluat 8(01):33–40 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references Zheng C, Wang PP (1999) MT3DMS: A Modular Three-Dimensional Multispecies Transport Model (December):219 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES


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

Mostrar el registro sencillo del ítem