A structure-guided and edge-preserving algorithm for smoothing 3D seismic data

Autores
Gómez, Julián Luis; Velis, Danilo Rubén; Sabbione, Juan Ignacio
Año de publicación
2017
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
We present structure-oriented and edge-preserving algorithms for denoising seismic data volumes in the frequencyspace (fxy) domain. After transforming the 3D data to the fxy domain, we apply for each frequency slice, 1D edgepreserving smoothing filters which are guided spatially by the so-called gradient structure tensor. The synthesis of the denoised data shows that incoherent as well as footprint noise can be significantly reduced with no loss of important structural information. The algorithm can be efficiently implemented using linear convolutions to compute the structure tensor and simple 1D edge-preserving operators. Contrarily to other 2D and 3D edge preserving algorithms, the proposed filtering is not limited to impedance data volumes.
Facultad de Ciencias Astronómicas y Geofísicas
Materia
Geofísica
Algorithms
Tensor
Filtering
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/146750
Acceder
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network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling A structure-guided and edge-preserving algorithm for smoothing 3D seismic dataGómez, Julián LuisVelis, Danilo RubénSabbione, Juan IgnacioGeofísicaAlgorithmsTensorFilteringWe present structure-oriented and edge-preserving algorithms for denoising seismic data volumes in the frequencyspace (fxy) domain. After transforming the 3D data to the fxy domain, we apply for each frequency slice, 1D edgepreserving smoothing filters which are guided spatially by the so-called gradient structure tensor. The synthesis of the denoised data shows that incoherent as well as footprint noise can be significantly reduced with no loss of important structural information. The algorithm can be efficiently implemented using linear convolutions to compute the structure tensor and simple 1D edge-preserving operators. Contrarily to other 2D and 3D edge preserving algorithms, the proposed filtering is not limited to impedance data volumes.Facultad de Ciencias Astronómicas y Geofísicas2017info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/146750enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T17:18:21Zoai:sedici.unlp.edu.ar:10915/146750Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 17:18:22.063SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A structure-guided and edge-preserving algorithm for smoothing 3D seismic data
title A structure-guided and edge-preserving algorithm for smoothing 3D seismic data
spellingShingle A structure-guided and edge-preserving algorithm for smoothing 3D seismic data
Gómez, Julián Luis
Geofísica
Algorithms
Tensor
Filtering
title_short A structure-guided and edge-preserving algorithm for smoothing 3D seismic data
title_full A structure-guided and edge-preserving algorithm for smoothing 3D seismic data
title_fullStr A structure-guided and edge-preserving algorithm for smoothing 3D seismic data
title_full_unstemmed A structure-guided and edge-preserving algorithm for smoothing 3D seismic data
title_sort A structure-guided and edge-preserving algorithm for smoothing 3D seismic data
dc.creator.none.fl_str_mv Gómez, Julián Luis
Velis, Danilo Rubén
Sabbione, Juan Ignacio
author Gómez, Julián Luis
author_facet Gómez, Julián Luis
Velis, Danilo Rubén
Sabbione, Juan Ignacio
author_role author
author2 Velis, Danilo Rubén
Sabbione, Juan Ignacio
author2_role author
author
dc.subject.none.fl_str_mv Geofísica
Algorithms
Tensor
Filtering
topic Geofísica
Algorithms
Tensor
Filtering
dc.description.none.fl_txt_mv We present structure-oriented and edge-preserving algorithms for denoising seismic data volumes in the frequencyspace (fxy) domain. After transforming the 3D data to the fxy domain, we apply for each frequency slice, 1D edgepreserving smoothing filters which are guided spatially by the so-called gradient structure tensor. The synthesis of the denoised data shows that incoherent as well as footprint noise can be significantly reduced with no loss of important structural information. The algorithm can be efficiently implemented using linear convolutions to compute the structure tensor and simple 1D edge-preserving operators. Contrarily to other 2D and 3D edge preserving algorithms, the proposed filtering is not limited to impedance data volumes.
Facultad de Ciencias Astronómicas y Geofísicas
description We present structure-oriented and edge-preserving algorithms for denoising seismic data volumes in the frequencyspace (fxy) domain. After transforming the 3D data to the fxy domain, we apply for each frequency slice, 1D edgepreserving smoothing filters which are guided spatially by the so-called gradient structure tensor. The synthesis of the denoised data shows that incoherent as well as footprint noise can be significantly reduced with no loss of important structural information. The algorithm can be efficiently implemented using linear convolutions to compute the structure tensor and simple 1D edge-preserving operators. Contrarily to other 2D and 3D edge preserving algorithms, the proposed filtering is not limited to impedance data volumes.
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/146750
url http://sedici.unlp.edu.ar/handle/10915/146750
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
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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