Three-term inversion of prestack seismic data using a weighted l2, 1 mixed norm
- Autores
- Perez, Daniel Omar; Velis, Danilo Ruben; Sacchi, Mauricio D.
- Año de publicación
- 2017
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- We present a new inversion method to estimate, from prestack seismic data, blocky P- and S-wave velocity and density images and the associated sparse reflectivity levels. The method uses the three-term Aki and Richards approximation to linearise the seismic inversion problem. To this end, we adopt a weighted mixed l2, 1-norm that promotes structured forms of sparsity, thus leading to blocky solutions in time. In addition, our algorithm incorporates a covariance or scale matrix to simultaneously constrain P- and S-wave velocities and density. This a priori information is obtained by nearby well-log data. We also include a term containing a low-frequency background model. The l2, 1 mixed norm leads to a convex objective function that can be minimised using proximal algorithms. In particular, we use the fast iterative shrinkage-thresholding algorithm. A key advantage of this algorithm is that it only requires matrix–vector multiplications and no direct matrix inversion. The latter makes our algorithm numerically stable, easy to apply, and economical in terms of computational cost. Tests on synthetic and field data show that the proposed method, contrarily to conventional l2- or l1-norm regularised solutions, is able to provide consistent blocky and/or sparse estimators of P- and S-wave velocities and density from a noisy and limited number of observations.
Fil: Perez, Daniel Omar. Universidad Nacional de la Plata. Facultad de Ciencias Astronómicas y Geofísicas. Departamento de Geofísica Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Velis, Danilo Ruben. Universidad Nacional de la Plata. Facultad de Ciencias Astronómicas y Geofísicas. Departamento de Geofísica Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Sacchi, Mauricio D.. University of Alberta; Canadá - Materia
-
Inverse Problem
Parameter Estimation
Seismic - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/56431
Ver los metadatos del registro completo
id |
CONICETDig_f7fdb9ecc94ba970421101b23fb337b2 |
---|---|
oai_identifier_str |
oai:ri.conicet.gov.ar:11336/56431 |
network_acronym_str |
CONICETDig |
repository_id_str |
3498 |
network_name_str |
CONICET Digital (CONICET) |
spelling |
Three-term inversion of prestack seismic data using a weighted l2, 1 mixed normPerez, Daniel OmarVelis, Danilo RubenSacchi, Mauricio D.Inverse ProblemParameter EstimationSeismichttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1We present a new inversion method to estimate, from prestack seismic data, blocky P- and S-wave velocity and density images and the associated sparse reflectivity levels. The method uses the three-term Aki and Richards approximation to linearise the seismic inversion problem. To this end, we adopt a weighted mixed l2, 1-norm that promotes structured forms of sparsity, thus leading to blocky solutions in time. In addition, our algorithm incorporates a covariance or scale matrix to simultaneously constrain P- and S-wave velocities and density. This a priori information is obtained by nearby well-log data. We also include a term containing a low-frequency background model. The l2, 1 mixed norm leads to a convex objective function that can be minimised using proximal algorithms. In particular, we use the fast iterative shrinkage-thresholding algorithm. A key advantage of this algorithm is that it only requires matrix–vector multiplications and no direct matrix inversion. The latter makes our algorithm numerically stable, easy to apply, and economical in terms of computational cost. Tests on synthetic and field data show that the proposed method, contrarily to conventional l2- or l1-norm regularised solutions, is able to provide consistent blocky and/or sparse estimators of P- and S-wave velocities and density from a noisy and limited number of observations.Fil: Perez, Daniel Omar. Universidad Nacional de la Plata. Facultad de Ciencias Astronómicas y Geofísicas. Departamento de Geofísica Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Velis, Danilo Ruben. Universidad Nacional de la Plata. Facultad de Ciencias Astronómicas y Geofísicas. Departamento de Geofísica Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sacchi, Mauricio D.. University of Alberta; CanadáWiley Blackwell Publishing, Inc2017-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/56431Perez, Daniel Omar; Velis, Danilo Ruben; Sacchi, Mauricio D.; Three-term inversion of prestack seismic data using a weighted l2, 1 mixed norm; Wiley Blackwell Publishing, Inc; Geophysical Prospecting; 65; 6; 11-2017; 1477-14950016-8025CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1111/1365-2478.12500info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1111/1365-2478.12500info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:15:33Zoai:ri.conicet.gov.ar:11336/56431instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:15:34.048CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Three-term inversion of prestack seismic data using a weighted l2, 1 mixed norm |
title |
Three-term inversion of prestack seismic data using a weighted l2, 1 mixed norm |
spellingShingle |
Three-term inversion of prestack seismic data using a weighted l2, 1 mixed norm Perez, Daniel Omar Inverse Problem Parameter Estimation Seismic |
title_short |
Three-term inversion of prestack seismic data using a weighted l2, 1 mixed norm |
title_full |
Three-term inversion of prestack seismic data using a weighted l2, 1 mixed norm |
title_fullStr |
Three-term inversion of prestack seismic data using a weighted l2, 1 mixed norm |
title_full_unstemmed |
Three-term inversion of prestack seismic data using a weighted l2, 1 mixed norm |
title_sort |
Three-term inversion of prestack seismic data using a weighted l2, 1 mixed norm |
dc.creator.none.fl_str_mv |
Perez, Daniel Omar Velis, Danilo Ruben Sacchi, Mauricio D. |
author |
Perez, Daniel Omar |
author_facet |
Perez, Daniel Omar Velis, Danilo Ruben Sacchi, Mauricio D. |
author_role |
author |
author2 |
Velis, Danilo Ruben Sacchi, Mauricio D. |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Inverse Problem Parameter Estimation Seismic |
topic |
Inverse Problem Parameter Estimation Seismic |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.5 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
We present a new inversion method to estimate, from prestack seismic data, blocky P- and S-wave velocity and density images and the associated sparse reflectivity levels. The method uses the three-term Aki and Richards approximation to linearise the seismic inversion problem. To this end, we adopt a weighted mixed l2, 1-norm that promotes structured forms of sparsity, thus leading to blocky solutions in time. In addition, our algorithm incorporates a covariance or scale matrix to simultaneously constrain P- and S-wave velocities and density. This a priori information is obtained by nearby well-log data. We also include a term containing a low-frequency background model. The l2, 1 mixed norm leads to a convex objective function that can be minimised using proximal algorithms. In particular, we use the fast iterative shrinkage-thresholding algorithm. A key advantage of this algorithm is that it only requires matrix–vector multiplications and no direct matrix inversion. The latter makes our algorithm numerically stable, easy to apply, and economical in terms of computational cost. Tests on synthetic and field data show that the proposed method, contrarily to conventional l2- or l1-norm regularised solutions, is able to provide consistent blocky and/or sparse estimators of P- and S-wave velocities and density from a noisy and limited number of observations. Fil: Perez, Daniel Omar. Universidad Nacional de la Plata. Facultad de Ciencias Astronómicas y Geofísicas. Departamento de Geofísica Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Velis, Danilo Ruben. Universidad Nacional de la Plata. Facultad de Ciencias Astronómicas y Geofísicas. Departamento de Geofísica Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Sacchi, Mauricio D.. University of Alberta; Canadá |
description |
We present a new inversion method to estimate, from prestack seismic data, blocky P- and S-wave velocity and density images and the associated sparse reflectivity levels. The method uses the three-term Aki and Richards approximation to linearise the seismic inversion problem. To this end, we adopt a weighted mixed l2, 1-norm that promotes structured forms of sparsity, thus leading to blocky solutions in time. In addition, our algorithm incorporates a covariance or scale matrix to simultaneously constrain P- and S-wave velocities and density. This a priori information is obtained by nearby well-log data. We also include a term containing a low-frequency background model. The l2, 1 mixed norm leads to a convex objective function that can be minimised using proximal algorithms. In particular, we use the fast iterative shrinkage-thresholding algorithm. A key advantage of this algorithm is that it only requires matrix–vector multiplications and no direct matrix inversion. The latter makes our algorithm numerically stable, easy to apply, and economical in terms of computational cost. Tests on synthetic and field data show that the proposed method, contrarily to conventional l2- or l1-norm regularised solutions, is able to provide consistent blocky and/or sparse estimators of P- and S-wave velocities and density from a noisy and limited number of observations. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-11 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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://hdl.handle.net/11336/56431 Perez, Daniel Omar; Velis, Danilo Ruben; Sacchi, Mauricio D.; Three-term inversion of prestack seismic data using a weighted l2, 1 mixed norm; Wiley Blackwell Publishing, Inc; Geophysical Prospecting; 65; 6; 11-2017; 1477-1495 0016-8025 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/56431 |
identifier_str_mv |
Perez, Daniel Omar; Velis, Danilo Ruben; Sacchi, Mauricio D.; Three-term inversion of prestack seismic data using a weighted l2, 1 mixed norm; Wiley Blackwell Publishing, Inc; Geophysical Prospecting; 65; 6; 11-2017; 1477-1495 0016-8025 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/doi/10.1111/1365-2478.12500 info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1111/1365-2478.12500 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Wiley Blackwell Publishing, Inc |
publisher.none.fl_str_mv |
Wiley Blackwell Publishing, Inc |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
_version_ |
1844614093054410752 |
score |
13.070432 |