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dc.contributor.authorMara, Thierry A.
dc.contributor.authorFajraoui, Noura
dc.contributor.authorGuadagnini, Alberto
dc.contributor.authorYounes, Anis
dc.date.accessioned2018-09-04T17:30:33Z
dc.date.available2018-09-04T17:30:33Z
dc.date.issued2017-11
dc.identifier.citationMara, T.A., Fajraoui, N., Guadagnini, A. et al. Stoch Environ Res Risk Assess (2017) 31: 2313. https://doi.org/10.1007/s00477-016-1344-1en_US
dc.identifier.issn1436-3240
dc.identifier.issn1436-3259
dc.identifier.doi10.1007/s00477-016-1344-1
dc.identifier.urihttp://hdl.handle.net/10150/628640
dc.description.abstractWe focus on the Bayesian estimation of strongly heterogeneous transmissivity fields conditional on data sampled at a set of locations in an aquifer. Log-transmissivity, Y, is modeled as a stochastic Gaussian process, parameterized through a truncated Karhunen-LoSve (KL) expansion. We consider Y fields characterized by a short correlation scale as compared to the size of the observed domain. These systems are associated with a KL decomposition which still requires a high number of parameters, thus hampering the efficiency of the Bayesian estimation of the underlying stochastic field. The distinctive aim of this work is to present an efficient approach for the stochastic inverse modeling of fully saturated groundwater flow in these types of strongly heterogeneous domains. The methodology is grounded on the construction of an optimal sparse KL decomposition which is achieved by retaining only a limited set of modes in the expansion. Mode selection is driven by model selection criteria and is conditional on available data of hydraulic heads and (optionally) Y. Bayesian inversion of the optimal sparse KLE is then inferred using Markov Chain Monte Carlo (MCMC) samplers. As a test bed, we illustrate our approach by way of a suite of computational examples where noisy head and Y values are sampled from a given randomly generated system. Our findings suggest that the proposed methodology yields a globally satisfactory inversion of the stochastic head and Y fields. Comparison of reference values against the corresponding MCMC predictive distributions suggests that observed values are well reproduced in a probabilistic sense. In a few cases, reference values at some unsampled locations (typically far from measurements) are not captured by the posterior probability distributions. In these cases, the quality of the estimation could be improved, e.g., by increasing the number of measurements and/or the threshold for the selection of KL modes.en_US
dc.description.sponsorshipFrench National Research Agency [ANR-12-BS06-0010-02]; European Union's Horizon Research and Innovation programmeen_US
dc.language.isoenen_US
dc.publisherSPRINGERen_US
dc.relation.urlhttp://link.springer.com/10.1007/s00477-016-1344-1en_US
dc.rights© Springer-Verlag Berlin Heidelberg 2016.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectHeterogeneous porous mediaen_US
dc.subjectStochastic inverse modelingen_US
dc.subjectKarhunen-Loeve expansionen_US
dc.subjectMarkov Chain Monte Carloen_US
dc.titleDimensionality reduction for efficient Bayesian estimation of groundwater flow in strongly heterogeneous aquifersen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Dept Hydrol & Atmospher Scien_US
dc.identifier.journalSTOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENTen_US
dc.description.note12 month embargo; published online: 02 November 2016en_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleStochastic Environmental Research and Risk Assessment
dc.source.volume31
dc.source.issue9
dc.source.beginpage2313
dc.source.endpage2326
refterms.dateFOA2017-11-02T00:00:00Z


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