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dc.contributor.authorMenafoglio, A.
dc.contributor.authorGuadagnini, A.
dc.contributor.authorSecchi, P.
dc.date.accessioned2017-01-17T23:01:05Z
dc.date.available2017-01-17T23:01:05Z
dc.date.issued2016-08
dc.identifier.citationStochastic simulation of soil particle-size curves in heterogeneous aquifer systems through a Bayes space approach 2016, 52 (8):5708 Water Resources Researchen
dc.identifier.issn00431397
dc.identifier.doi10.1002/2015WR018369
dc.identifier.urihttp://hdl.handle.net/10150/621995
dc.description.abstractWe address the problem of stochastic simulation of soil particle-size curves (PSCs) in heterogeneous aquifer systems. Unlike traditional approaches that focus solely on a few selected features of PSCs (e.g., selected quantiles), our approach considers the entire particle-size curves and can optionally include conditioning on available data. We rely on our prior work to model PSCs as cumulative distribution functions and interpret their density functions as functional compositions. We thus approximate the latter through an expansion over an appropriate basis of functions. This enables us to (a) effectively deal with the data dimensionality and constraints and (b) to develop a simulation method for PSCs based upon a suitable and well defined projection procedure. The new theoretical framework allows representing and reproducing the complete information content embedded in PSC data. As a first field application, we demonstrate the quality of unconditional and conditional simulations obtained with our methodology by considering a set of particle-size curves collected within a shallow alluvial aquifer in the Neckar river valley, Germany.
dc.description.sponsorshipEuropean Union [640979]en
dc.language.isoenen
dc.publisherAMER GEOPHYSICAL UNIONen
dc.relation.urlhttp://doi.wiley.com/10.1002/2015WR018369en
dc.rights© 2016. American Geophysical Union. All Rights Reserved.en
dc.subjectgeostatisticsen
dc.subjectfunctional compositionsen
dc.subjectparticle-size distributionen
dc.subjectgroundwateren
dc.subjecthydrogeologyen
dc.subjectBayes spacesen
dc.titleStochastic simulation of soil particle-size curves in heterogeneous aquifer systems through a Bayes space approachen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Dept Hydrol & Atmospher Scien
dc.identifier.journalWater Resources Researchen
dc.description.noteFirst Published: 2 August 2016; 6 Month Embargo.en
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
dc.eprint.versionFinal published versionen
dc.contributor.institutionMOX, Department of Mathematics; Politecnico di Milano; Milano Italy
dc.contributor.institutionDepartment of Civil and Environmental Engineering; Politecnico di Milano; Milano Italy
dc.contributor.institutionMOX, Department of Mathematics; Politecnico di Milano; Milano Italy
refterms.dateFOA2017-02-05T00:00:00Z
html.description.abstractWe address the problem of stochastic simulation of soil particle-size curves (PSCs) in heterogeneous aquifer systems. Unlike traditional approaches that focus solely on a few selected features of PSCs (e.g., selected quantiles), our approach considers the entire particle-size curves and can optionally include conditioning on available data. We rely on our prior work to model PSCs as cumulative distribution functions and interpret their density functions as functional compositions. We thus approximate the latter through an expansion over an appropriate basis of functions. This enables us to (a) effectively deal with the data dimensionality and constraints and (b) to develop a simulation method for PSCs based upon a suitable and well defined projection procedure. The new theoretical framework allows representing and reproducing the complete information content embedded in PSC data. As a first field application, we demonstrate the quality of unconditional and conditional simulations obtained with our methodology by considering a set of particle-size curves collected within a shallow alluvial aquifer in the Neckar river valley, Germany.


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