Statistical Segmentation of Geophysical Log Data

Autores
Velis, Danilo Rubén
Año de publicación
2007
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Stationary segments in well log sequences can be automatically detected by searching for change points in the data. These change points, which correspond to abrupt changes in the statistical nature of the underlying process, can be identified by analysing the probability density functions of two adjacent sub-samples as they move along the data sequence. A statistical test is used to set a significance level of the probability that the two distributions are the same, thus providing a means to decide how many segments comprise the data by keeping those change points that yield low probabilities. Data from the Ocean Drilling Program were analysed, where a high correlation between the available core-log lithology interpretation and the statistical segmentation was observed. Results show that the proposed algorithm can be used as an auxiliary tool in the analysis and interpretation of geophysical log data for the identification of lithology units and sequences.
Facultad de Ciencias Astronómicas y Geofísicas
Materia
Astronomía
Data mining
Segmentation
Zonation
Change point
Probability density function
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/139406

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spelling Statistical Segmentation of Geophysical Log DataVelis, Danilo RubénAstronomíaData miningSegmentationZonationChange pointProbability density functionStationary segments in well log sequences can be automatically detected by searching for change points in the data. These change points, which correspond to abrupt changes in the statistical nature of the underlying process, can be identified by analysing the probability density functions of two adjacent sub-samples as they move along the data sequence. A statistical test is used to set a significance level of the probability that the two distributions are the same, thus providing a means to decide how many segments comprise the data by keeping those change points that yield low probabilities. Data from the Ocean Drilling Program were analysed, where a high correlation between the available core-log lithology interpretation and the statistical segmentation was observed. Results show that the proposed algorithm can be used as an auxiliary tool in the analysis and interpretation of geophysical log data for the identification of lithology units and sequences.Facultad de Ciencias Astronómicas y Geofísicas2007-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf409-417http://sedici.unlp.edu.ar/handle/10915/139406enginfo:eu-repo/semantics/altIdentifier/issn/0882-8121info:eu-repo/semantics/altIdentifier/issn/1573-8868info:eu-repo/semantics/altIdentifier/doi/10.1007/s11004-007-9103-yinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-17T10:14:46Zoai:sedici.unlp.edu.ar:10915/139406Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-17 10:14:46.961SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Statistical Segmentation of Geophysical Log Data
title Statistical Segmentation of Geophysical Log Data
spellingShingle Statistical Segmentation of Geophysical Log Data
Velis, Danilo Rubén
Astronomía
Data mining
Segmentation
Zonation
Change point
Probability density function
title_short Statistical Segmentation of Geophysical Log Data
title_full Statistical Segmentation of Geophysical Log Data
title_fullStr Statistical Segmentation of Geophysical Log Data
title_full_unstemmed Statistical Segmentation of Geophysical Log Data
title_sort Statistical Segmentation of Geophysical Log Data
dc.creator.none.fl_str_mv Velis, Danilo Rubén
author Velis, Danilo Rubén
author_facet Velis, Danilo Rubén
author_role author
dc.subject.none.fl_str_mv Astronomía
Data mining
Segmentation
Zonation
Change point
Probability density function
topic Astronomía
Data mining
Segmentation
Zonation
Change point
Probability density function
dc.description.none.fl_txt_mv Stationary segments in well log sequences can be automatically detected by searching for change points in the data. These change points, which correspond to abrupt changes in the statistical nature of the underlying process, can be identified by analysing the probability density functions of two adjacent sub-samples as they move along the data sequence. A statistical test is used to set a significance level of the probability that the two distributions are the same, thus providing a means to decide how many segments comprise the data by keeping those change points that yield low probabilities. Data from the Ocean Drilling Program were analysed, where a high correlation between the available core-log lithology interpretation and the statistical segmentation was observed. Results show that the proposed algorithm can be used as an auxiliary tool in the analysis and interpretation of geophysical log data for the identification of lithology units and sequences.
Facultad de Ciencias Astronómicas y Geofísicas
description Stationary segments in well log sequences can be automatically detected by searching for change points in the data. These change points, which correspond to abrupt changes in the statistical nature of the underlying process, can be identified by analysing the probability density functions of two adjacent sub-samples as they move along the data sequence. A statistical test is used to set a significance level of the probability that the two distributions are the same, thus providing a means to decide how many segments comprise the data by keeping those change points that yield low probabilities. Data from the Ocean Drilling Program were analysed, where a high correlation between the available core-log lithology interpretation and the statistical segmentation was observed. Results show that the proposed algorithm can be used as an auxiliary tool in the analysis and interpretation of geophysical log data for the identification of lithology units and sequences.
publishDate 2007
dc.date.none.fl_str_mv 2007-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/139406
url http://sedici.unlp.edu.ar/handle/10915/139406
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/0882-8121
info:eu-repo/semantics/altIdentifier/issn/1573-8868
info:eu-repo/semantics/altIdentifier/doi/10.1007/s11004-007-9103-y
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.format.none.fl_str_mv application/pdf
409-417
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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