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dc.contributor.authorRay, J.
dc.contributor.authorvan Bloemen Waanders, B.
dc.contributor.authorMarzouk, Youssef M.
dc.contributor.authorMcKenna, S. A.
dc.date.accessioned2015-10-26T16:57:49Z
dc.date.available2015-10-26T16:57:49Z
dc.date.issued2012-05
dc.date.submitted2012-04
dc.identifier.issn03091708
dc.identifier.urihttp://hdl.handle.net/1721.1/99457
dc.description.abstractWe present a Bayesian technique to estimate the fine-scale properties of a binary medium from multiscale observations. The binary medium of interest consists of spatially varying proportions of low and high permeability material with an isotropic structure. Inclusions of one material within the other are far smaller than the domain sizes of interest, and thus are never explicitly resolved. We consider the problem of estimating the spatial distribution of the inclusion proportion, F(x), and a characteristic length-scale of the inclusions, δ, from sparse multiscale measurements. The observations consist of coarse-scale (of the order of the domain size) measurements of the effective permeability of the medium (i.e., static data) and tracer breakthrough times (i.e., dynamic data), which interrogate the fine scale, at a sparsely distributed set of locations. This ill-posed problem is regularized by specifying a Gaussian process model for the unknown field F(x) and expressing it as a superposition of Karhunen–Loève modes. The effect of the fine-scale structures on the coarse-scale effective permeability i.e., upscaling, is performed using a subgrid-model which includes δ as one of its parameters. A statistical inverse problem is posed to infer the weights of the Karhunen–Loève modes and δ, which is then solved using an adaptive Markov Chain Monte Carlo method. The solution yields non-parametric distributions for the objects of interest, thus providing most probable estimates and uncertainty bounds on latent structures at coarse and fine scales. The technique is tested using synthetic data. The individual contributions of the static and dynamic data to the inference are also analyzed.en_US
dc.description.sponsorshipUnited States. Dept. of Energy. National Nuclear Security Administration (Contract DE-AC04_94AL85000)en_US
dc.language.isoen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.advwatres.2012.04.009en_US
dc.rightsCreative Commons Attribution-Noncommercial-NoDerivativesen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourceOther repositoryen_US
dc.titleBayesian reconstruction of binary media with unresolved fine-scale spatial structuresen_US
dc.typeArticleen_US
dc.identifier.citationRay, J., S.A. McKenna, B. van Bloemen Waanders, and Y.M. Marzouk. “Bayesian Reconstruction of Binary Media with Unresolved Fine-Scale Spatial Structures.” Advances in Water Resources 44 (August 2012): 1–19.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronauticsen_US
dc.contributor.mitauthorMarzouk, Youssef M.en_US
dc.relation.journalAdvances in Water Resourcesen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsRay, J.; McKenna, S.A.; van Bloemen Waanders, B.; Marzouk, Y.M.en_US
dc.identifier.orcidhttps://orcid.org/0000-0001-8242-3290
mit.licensePUBLISHER_CCen_US


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