Simple and fast gradient-based impedance inversion using total variation regularization

Autores
Pérez, Daniel Omar; Velis, Danilo Rubén
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We present an algorithm to estimate blocky images of the subsurface acoustic impedance (AI) from poststack seismic data. We regularize the resulting inverse problem, which is inherently ill-posed and non-unique, by means of the total variation semi-norm (TV). This allows us promote stable and blocky solutions which are, by virtue of the capability of TV to handle edges properly, adequate to model layered earth models with sharp contrasts. The use of the TV leads to a convex objective function easily minimized using a gradient-based algorithm that requires, in contrast to other AI inversion methods based on TV regularization, simple matrix-vector multiplications and no direct matrix inversion. The latter makes the algorithm numerically stable, easy to apply, and economic in terms of computational cost. Tests on synthetic and field data show that the proposed method, contrarily to conventional l2- or l1-norm regularized solutions, is able to provide blocky AI images that preserve the subsurface layered structure with good lateral continuity from noisy observations.
Facultad de Ciencias Astronómicas y Geofísicas
Materia
Ciencias Naturales
Total variation
Acoustic impedance
Inversion
Seismic
Poststack
Blocky
FISTA
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/98098

id SEDICI_aa1e316ede1f851ef7161cfb6df4d8e1
oai_identifier_str oai:sedici.unlp.edu.ar:10915/98098
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Simple and fast gradient-based impedance inversion using total variation regularizationPérez, Daniel OmarVelis, Danilo RubénCiencias NaturalesTotal variationAcoustic impedanceInversionSeismicPoststackBlockyFISTAWe present an algorithm to estimate blocky images of the subsurface acoustic impedance (AI) from poststack seismic data. We regularize the resulting inverse problem, which is inherently ill-posed and non-unique, by means of the total variation semi-norm (TV). This allows us promote stable and blocky solutions which are, by virtue of the capability of TV to handle edges properly, adequate to model layered earth models with sharp contrasts. The use of the TV leads to a convex objective function easily minimized using a gradient-based algorithm that requires, in contrast to other AI inversion methods based on TV regularization, simple matrix-vector multiplications and no direct matrix inversion. The latter makes the algorithm numerically stable, easy to apply, and economic in terms of computational cost. Tests on synthetic and field data show that the proposed method, contrarily to conventional l<sub>2</sub>- or l<sub>1</sub>-norm regularized solutions, is able to provide blocky AI images that preserve the subsurface layered structure with good lateral continuity from noisy observations.Facultad de Ciencias Astronómicas y Geofísicas2018info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf473-486http://sedici.unlp.edu.ar/handle/10915/98098enginfo:eu-repo/semantics/altIdentifier/url/https://ri.conicet.gov.ar/11336/84630info:eu-repo/semantics/altIdentifier/url/http://www.geophysical-press.com/online/VOL27-5-Art4.pdfinfo:eu-repo/semantics/altIdentifier/issn/0963-0651info:eu-repo/semantics/altIdentifier/hdl/11336/84630info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:20:30Zoai:sedici.unlp.edu.ar:10915/98098Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:20:31.151SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Simple and fast gradient-based impedance inversion using total variation regularization
title Simple and fast gradient-based impedance inversion using total variation regularization
spellingShingle Simple and fast gradient-based impedance inversion using total variation regularization
Pérez, Daniel Omar
Ciencias Naturales
Total variation
Acoustic impedance
Inversion
Seismic
Poststack
Blocky
FISTA
title_short Simple and fast gradient-based impedance inversion using total variation regularization
title_full Simple and fast gradient-based impedance inversion using total variation regularization
title_fullStr Simple and fast gradient-based impedance inversion using total variation regularization
title_full_unstemmed Simple and fast gradient-based impedance inversion using total variation regularization
title_sort Simple and fast gradient-based impedance inversion using total variation regularization
dc.creator.none.fl_str_mv Pérez, Daniel Omar
Velis, Danilo Rubén
author Pérez, Daniel Omar
author_facet Pérez, Daniel Omar
Velis, Danilo Rubén
author_role author
author2 Velis, Danilo Rubén
author2_role author
dc.subject.none.fl_str_mv Ciencias Naturales
Total variation
Acoustic impedance
Inversion
Seismic
Poststack
Blocky
FISTA
topic Ciencias Naturales
Total variation
Acoustic impedance
Inversion
Seismic
Poststack
Blocky
FISTA
dc.description.none.fl_txt_mv We present an algorithm to estimate blocky images of the subsurface acoustic impedance (AI) from poststack seismic data. We regularize the resulting inverse problem, which is inherently ill-posed and non-unique, by means of the total variation semi-norm (TV). This allows us promote stable and blocky solutions which are, by virtue of the capability of TV to handle edges properly, adequate to model layered earth models with sharp contrasts. The use of the TV leads to a convex objective function easily minimized using a gradient-based algorithm that requires, in contrast to other AI inversion methods based on TV regularization, simple matrix-vector multiplications and no direct matrix inversion. The latter makes the algorithm numerically stable, easy to apply, and economic in terms of computational cost. Tests on synthetic and field data show that the proposed method, contrarily to conventional l<sub>2</sub>- or l<sub>1</sub>-norm regularized solutions, is able to provide blocky AI images that preserve the subsurface layered structure with good lateral continuity from noisy observations.
Facultad de Ciencias Astronómicas y Geofísicas
description We present an algorithm to estimate blocky images of the subsurface acoustic impedance (AI) from poststack seismic data. We regularize the resulting inverse problem, which is inherently ill-posed and non-unique, by means of the total variation semi-norm (TV). This allows us promote stable and blocky solutions which are, by virtue of the capability of TV to handle edges properly, adequate to model layered earth models with sharp contrasts. The use of the TV leads to a convex objective function easily minimized using a gradient-based algorithm that requires, in contrast to other AI inversion methods based on TV regularization, simple matrix-vector multiplications and no direct matrix inversion. The latter makes the algorithm numerically stable, easy to apply, and economic in terms of computational cost. Tests on synthetic and field data show that the proposed method, contrarily to conventional l<sub>2</sub>- or l<sub>1</sub>-norm regularized solutions, is able to provide blocky AI images that preserve the subsurface layered structure with good lateral continuity from noisy observations.
publishDate 2018
dc.date.none.fl_str_mv 2018
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/10915/98098
url http://sedici.unlp.edu.ar/handle/10915/98098
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://ri.conicet.gov.ar/11336/84630
info:eu-repo/semantics/altIdentifier/url/http://www.geophysical-press.com/online/VOL27-5-Art4.pdf
info:eu-repo/semantics/altIdentifier/issn/0963-0651
info:eu-repo/semantics/altIdentifier/hdl/11336/84630
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
473-486
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
_version_ 1844616079690694656
score 13.070432