Quantifying a soil pore distribution from 3D images: Multifractal spectrum through wavelet approach RID B-1239-2010

dc.contributor.authorPiñuela Izquierdo, Juan Antonio
dc.contributor.authorÁlvarez, A.spa
dc.contributor.authorAndina, Diegospa
dc.contributor.authorHeck, R. J.spa
dc.contributor.authorTarquis, Ana Maríaspa
dc.date.accessioned2013-11-27T17:26:20Z
dc.date.available2013-11-27T17:26:20Z
dc.date.issued2010spa
dc.description.abstractKnowledge on soil pore geometry is important for understanding soil processes as it controls the movement and storage of fluids on various scales. With the advent of modern non-destructive tomography techniques there have been many attempts made to analyze pore space features mainly concentrating on the visualization Of Soil Structure. Multifractal formalism or the wavelet transform has been revealed as a useful tool in these cases where highly complex and heterogeneous media are studied. The field of 3D pore space analysis opens a challenging opportunity to develop techniques for quantifying and describing pore space properties. One of these quantifications can be the maximum depth pore network (MD), analogous as the quantification of the preferential flow paths. In this paper, a variation of the wavelet transform modulo maxima (WTMM) method used to compute multifractal behavior is presented. As a wavelet transform analysis (WTA), it allows us to focus on every scale which can be useful to select the range of scales where multifractal analysis (MFA) can be applied, revealing the MD global scaling patterns. In addition, the proposed method does not make any global estimate, so it can also be used to focus on local distribution of singularities. So, in the context of multiscaling structure analysis, the proposed wavelet-based method can complement box-counting analysis in order to statistically describe preferential flow path geometry and flow processes. The methodology is applied to determine the multifiractal behaviour of 3D images of soil samples with 45.1 mu m resolution (256 x 256 x 256 voxels) with closer porosities (ranging from 12% to 14%) and different spatial arrangements. (C) 2009 Elsevier B.V. All rights reserved.spa
dc.description.filiationUEMspa
dc.description.impact2.178 JCR (2010) Q1, 5/32 Soil sciencespa
dc.identifier.citationPinuela, J., Álvarez, A., Andina, D., Heck, R. J., & Tarquis, A. M. (2010). Quantifying a soil pore distribution from 3D images: multifractal spectrum through wavelet approach RID B-1239-2010. Geoderma, 155(3-4), 203-210.spa
dc.identifier.doi10.1016/j.geoderma.2009.07.007spa
dc.identifier.issn00167061spa
dc.identifier.urihttp://hdl.handle.net/11268/643
dc.language.isoengspa
dc.peerreviewedSispa
dc.rights.accessRightsrestricted accessen
dc.subject.otherMultifractal Analysisspa
dc.subject.otherLipschitz Exponentsspa
dc.subject.otherScalingspa
dc.subject.otherC. Tomographyspa
dc.subject.otherRay Computed-Tomographyspa
dc.subject.otherFractal Dimensionspa
dc.subject.otherSingularity Detectionspa
dc.subject.otherBulk-Densityspa
dc.subject.otherMacroporosityspa
dc.subject.otherQuantificationspa
dc.subject.otherAgriculturespa
dc.subject.unescoSuelospa
dc.subject.unescoIngeniería de la construcciónspa
dc.subject.unescoReconocimiento de formasspa
dc.titleQuantifying a soil pore distribution from 3D images: Multifractal spectrum through wavelet approach RID B-1239-2010spa
dc.typejournal articlespa
dspace.entity.typePublication
relation.isAuthorOfPublication79ab54d1-c7b5-472a-8b7a-c7fc1cfbca93
relation.isAuthorOfPublication.latestForDiscovery79ab54d1-c7b5-472a-8b7a-c7fc1cfbca93

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