Non-monotonic Gaussian transformation for multivariate regionalized data.
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| Title: | Non-monotonic Gaussian transformation for multivariate regionalized data. |
|---|---|
| Authors: | Khorram, Farzaneh1,2,3 (AUTHOR) farzaneh.khorram@unab.cl, Emery, Xavier1,2 (AUTHOR) xemery@ing.uchile.cl, Cáceres, Alejandro1,2 (AUTHOR) alecacer@uchile.cl |
| Source: | Stochastic Environmental Research & Risk Assessment. Jul2025, Vol. 39 Issue 7, p3019-3043. 25p. |
| Subjects: | Marginal distributions, Nickel ores, Ore deposits, Random fields, Geological statistics |
| Abstract: | A novel approach is presented to transform a set of coregionalized variables measured on quantitative scales into Gaussian variables. The innovation is twofold. On the one hand, unlike joint anamorphoses approaches, there is a one-to-one association between each original variable and a Gaussian variable, which makes the transformation applicable even in case of heterotopic sampling designs. On the other hand, the transformation of each variable is non-monotonic, which provides greater flexibility in comparison with the traditional normal scores transform. The inference of the transformation functions relies on the fitting of indicator direct and cross-covariances, for which an iterative procedure is proposed, and of the marginal distribution of each variable. The covariances of the Gaussian random fields can subsequently be fitted so as to reproduce the spatial correlation of (a transform of) the original variables. Once the model parameters are determined, conditional simulation can be performed by means of a mix of sequential Monte Carlo and classical multigaussian simulation techniques. An application case study is presented, pertaining to the evaluation of a lateritic nickel ore deposit, where the performances of our proposal in terms of prediction, uncertainty quantification, and reproduction of statistical and spatial dependencies between variables, are compared to that of the traditional multigaussian approach and the projection pursuit multivariate transform. [ABSTRACT FROM AUTHOR] |
| Copyright of Stochastic Environmental Research & Risk Assessment is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 186468948 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Non-monotonic Gaussian transformation for multivariate regionalized data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Khorram%2C+Farzaneh%22">Khorram, Farzaneh</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> farzaneh.khorram@unab.cl</i><br /><searchLink fieldCode="AR" term="%22Emery%2C+Xavier%22">Emery, Xavier</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> xemery@ing.uchile.cl</i><br /><searchLink fieldCode="AR" term="%22Cáceres%2C+Alejandro%22">Cáceres, Alejandro</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> alecacer@uchile.cl</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Stochastic+Environmental+Research+%26+Risk+Assessment%22">Stochastic Environmental Research & Risk Assessment</searchLink>. Jul2025, Vol. 39 Issue 7, p3019-3043. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Marginal+distributions%22">Marginal distributions</searchLink><br /><searchLink fieldCode="DE" term="%22Nickel+ores%22">Nickel ores</searchLink><br /><searchLink fieldCode="DE" term="%22Ore+deposits%22">Ore deposits</searchLink><br /><searchLink fieldCode="DE" term="%22Random+fields%22">Random fields</searchLink><br /><searchLink fieldCode="DE" term="%22Geological+statistics%22">Geological statistics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A novel approach is presented to transform a set of coregionalized variables measured on quantitative scales into Gaussian variables. The innovation is twofold. On the one hand, unlike joint anamorphoses approaches, there is a one-to-one association between each original variable and a Gaussian variable, which makes the transformation applicable even in case of heterotopic sampling designs. On the other hand, the transformation of each variable is non-monotonic, which provides greater flexibility in comparison with the traditional normal scores transform. The inference of the transformation functions relies on the fitting of indicator direct and cross-covariances, for which an iterative procedure is proposed, and of the marginal distribution of each variable. The covariances of the Gaussian random fields can subsequently be fitted so as to reproduce the spatial correlation of (a transform of) the original variables. Once the model parameters are determined, conditional simulation can be performed by means of a mix of sequential Monte Carlo and classical multigaussian simulation techniques. An application case study is presented, pertaining to the evaluation of a lateritic nickel ore deposit, where the performances of our proposal in terms of prediction, uncertainty quantification, and reproduction of statistical and spatial dependencies between variables, are compared to that of the traditional multigaussian approach and the projection pursuit multivariate transform. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Stochastic Environmental Research & Risk Assessment is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00477-025-03005-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 3019 Subjects: – SubjectFull: Marginal distributions Type: general – SubjectFull: Nickel ores Type: general – SubjectFull: Ore deposits Type: general – SubjectFull: Random fields Type: general – SubjectFull: Geological statistics Type: general Titles: – TitleFull: Non-monotonic Gaussian transformation for multivariate regionalized data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Khorram, Farzaneh – PersonEntity: Name: NameFull: Emery, Xavier – PersonEntity: Name: NameFull: Cáceres, Alejandro IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 14363240 Numbering: – Type: volume Value: 39 – Type: issue Value: 7 Titles: – TitleFull: Stochastic Environmental Research & Risk Assessment Type: main |
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