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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/98098
Ver los metadatos del registro completo
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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 |
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reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
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SEDICI (UNLP) - Universidad Nacional de La Plata |
repository.mail.fl_str_mv |
alira@sedici.unlp.edu.ar |
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