Interpolation algorithm ranking using cross-validation and the role of smoothing effect. A coal zone example

For a property measured at several locations, interpolation algorithms provide a unique and smooth function yielding a locally realistic estimation at any point within the sampled region. Previous studies searching for optimal interpolation strategies by measuring cross-validation error have not fou...

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Detalles Bibliográficos
Autores: Falivene Aldea, Oriol, Cabrera, Lluís, Tolosana-Delgado, R., Sáez, Alberto
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2010
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/101823
Acceso en línea:https://hdl.handle.net/2445/101823
Access Level:acceso abierto
Palabra clave:Fàcies (Geologia)
Models matemàtics
Facies (Geology)
Mathematical models
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spelling Interpolation algorithm ranking using cross-validation and the role of smoothing effect. A coal zone exampleFalivene Aldea, OriolCabrera, LluísTolosana-Delgado, R.Sáez, AlbertoFàcies (Geologia)Models matemàticsFacies (Geology)Mathematical modelsFor a property measured at several locations, interpolation algorithms provide a unique and smooth function yielding a locally realistic estimation at any point within the sampled region. Previous studies searching for optimal interpolation strategies by measuring cross-validation error have not found consistent rankings; this fact was traditionally explained by differences in the distribution, spatial variability and sampling patterns of the datasets. This article demonstrates that ranking differences are also related to interpolation smoothing, an important factor controlling cross-validation errors that was not considered previously. Indeed, smoothing in average-based interpolation algorithms depends on the number of neighbouring data points used to obtain each interpolated value, among other algorithm parameters. A 3D dataset of calorific value measurements from a coal zone is used to demonstrate that different algorithm rankings can be obtained solely by varying the number of neighbouring points considered (i.e. whilst maintaining the distribution, spatial variability and sampling pattern of the dataset). These results suggest that cross-validation error cannot be used as a unique criterion to compare the performance of interpolation algorithms, as has been done in the past, and indicate that smoothing should be also 26 coupled to search for optimum and geologically realistic interpolation algorithms.Elsevier Ltd2010info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://hdl.handle.net/2445/101823Articles publicats en revistes (Dinàmica de la Terra i l'Oceà)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésVersió postprint del document publicat a: http://dx.doi.org/10.1016/j.cageo.2009.09.015Computers & Geosciences, 2010, vol. 36, p. 512-519http://dx.doi.org/10.1016/j.cageo.2009.09.015(c) Elsevier Ltd, 2010info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1018232026-05-27T06:46:51Z
dc.title.none.fl_str_mv Interpolation algorithm ranking using cross-validation and the role of smoothing effect. A coal zone example
title Interpolation algorithm ranking using cross-validation and the role of smoothing effect. A coal zone example
spellingShingle Interpolation algorithm ranking using cross-validation and the role of smoothing effect. A coal zone example
Falivene Aldea, Oriol
Fàcies (Geologia)
Models matemàtics
Facies (Geology)
Mathematical models
title_short Interpolation algorithm ranking using cross-validation and the role of smoothing effect. A coal zone example
title_full Interpolation algorithm ranking using cross-validation and the role of smoothing effect. A coal zone example
title_fullStr Interpolation algorithm ranking using cross-validation and the role of smoothing effect. A coal zone example
title_full_unstemmed Interpolation algorithm ranking using cross-validation and the role of smoothing effect. A coal zone example
title_sort Interpolation algorithm ranking using cross-validation and the role of smoothing effect. A coal zone example
dc.creator.none.fl_str_mv Falivene Aldea, Oriol
Cabrera, Lluís
Tolosana-Delgado, R.
Sáez, Alberto
author Falivene Aldea, Oriol
author_facet Falivene Aldea, Oriol
Cabrera, Lluís
Tolosana-Delgado, R.
Sáez, Alberto
author_role author
author2 Cabrera, Lluís
Tolosana-Delgado, R.
Sáez, Alberto
author2_role author
author
author
dc.subject.none.fl_str_mv Fàcies (Geologia)
Models matemàtics
Facies (Geology)
Mathematical models
topic Fàcies (Geologia)
Models matemàtics
Facies (Geology)
Mathematical models
description For a property measured at several locations, interpolation algorithms provide a unique and smooth function yielding a locally realistic estimation at any point within the sampled region. Previous studies searching for optimal interpolation strategies by measuring cross-validation error have not found consistent rankings; this fact was traditionally explained by differences in the distribution, spatial variability and sampling patterns of the datasets. This article demonstrates that ranking differences are also related to interpolation smoothing, an important factor controlling cross-validation errors that was not considered previously. Indeed, smoothing in average-based interpolation algorithms depends on the number of neighbouring data points used to obtain each interpolated value, among other algorithm parameters. A 3D dataset of calorific value measurements from a coal zone is used to demonstrate that different algorithm rankings can be obtained solely by varying the number of neighbouring points considered (i.e. whilst maintaining the distribution, spatial variability and sampling pattern of the dataset). These results suggest that cross-validation error cannot be used as a unique criterion to compare the performance of interpolation algorithms, as has been done in the past, and indicate that smoothing should be also 26 coupled to search for optimum and geologically realistic interpolation algorithms.
publishDate 2010
dc.date.none.fl_str_mv 2010
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/101823
url https://hdl.handle.net/2445/101823
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Versió postprint del document publicat a: http://dx.doi.org/10.1016/j.cageo.2009.09.015
Computers & Geosciences, 2010, vol. 36, p. 512-519
http://dx.doi.org/10.1016/j.cageo.2009.09.015
dc.rights.none.fl_str_mv (c) Elsevier Ltd, 2010
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) Elsevier Ltd, 2010
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier Ltd
publisher.none.fl_str_mv Elsevier Ltd
dc.source.none.fl_str_mv Articles publicats en revistes (Dinàmica de la Terra i l'Oceà)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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