Constrained optimization framework for joint inversion of geophysical data sets
Many experimental techniques in geophysics advance the understanding of Earth processes by estimating and interpreting Earth structure (e.g. velocity and/or density structure). Different types of geophysical data can be collected and analysed separately, sometimes resulting in inconsistent models of...
- Autores:
-
Sosa Aguirre, Uram Anibal
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2013
- Institución:
- Universidad ICESI
- Repositorio:
- Repositorio ICESI
- Idioma:
- eng
- OAI Identifier:
- oai:repository.icesi.edu.co:10906/78332
- Acceso en línea:
- http://www.scopus.com/inward/record.url?eid=2-s2.0-84887566536&partnerID=tZOtx3y1
http://hdl.handle.net/10906/78332
- Palabra clave:
- Soluciones numéricas
Teoría inversa
Sismología computacional
Numerical solutions
Inverse theory
Computational seismology
Automatización y sistemas de control
Automation
Control system
- Rights
- openAccess
- License
- https://creativecommons.org/licenses/by-nc-nd/4.0/
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Sosa Aguirre, Uram Anibal2015-09-30T22:24:06Z2015-09-30T22:24:06Z2013-09-1710.1093/gji/ggt3260956-540Xhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84887566536&partnerID=tZOtx3y1http://hdl.handle.net/10906/78332instname: Universidad Icesireponame: Biblioteca Digitalrepourl: https://repository.icesi.edu.co/Many experimental techniques in geophysics advance the understanding of Earth processes by estimating and interpreting Earth structure (e.g. velocity and/or density structure). Different types of geophysical data can be collected and analysed separately, sometimes resulting in inconsistent models of the Earth depending on the data used. We present a constrained optimization approach for a joint inversion least-squares (LSQ) algorithm to characterize 1-D Earth's structure. We use two geophysical data sets sensitive to shear velocities: receiver function and surface wave dispersion velocity observations. We study the use of bound constraints on the regularized inverse problem, which are more physical than the regularization parameters required by conventional unconstrained formulations. Specifically, we develop a constrained optimization formulation that is solved with a primal-dual interior-point (PDIP) method, and validate our results with a traditional, unconstrained formulation that is solved with a truncated singular value decomposition (TSVD) for a set of numerical experiments with synthetic crustal velocity models. We conclude that the PDIP results are as accurate as those from the regularized TSVD approach, are less affected by noise, and honour the geophysical constraints. © The Authors 2013. Published by Oxford University Press on behalf of The Royal Astronomical Society.engGeophysical Journal International, Vol. 195, No. 3 - 2013EL AUTOR, expresa que la obra objeto de la presente autorización es original y la elaboró sin quebrantar ni suplantar los derechos de autor de terceros, y de tal forma, la obra es de su exclusiva autoría y tiene la titularidad sobre éste. PARÁGRAFO: en caso de queja o acción por parte de un tercero referente a los derechos de autor sobre el artículo, folleto o libro en cuestión, EL AUTOR, asumirá la responsabilidad total, y saldrá en defensa de los derechos aquí autorizados; para todos los efectos, la Universidad Icesi actúa como un tercero de buena fe. Esta autorización, permite a la Universidad Icesi, de forma indefinida, para que en los términos establecidos en la Ley 23 de 1982, la Ley 44 de 1993, leyes y jurisprudencia vigente al respecto, haga publicación de este con fines educativos. Toda persona que consulte ya sea la biblioteca o en medio electrónico podrá copiar apartes del texto citando siempre la fuentes, es decir el título del trabajo y el autor.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Soluciones numéricasTeoría inversaSismología computacionalNumerical solutionsInverse theoryComputational seismologyAutomatización y sistemas de controlAutomationControl systemConstrained optimization framework for joint inversion of geophysical data setsinfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_2df8fbb1Artículoinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a8519517451762TEXTsosa_constrained_optimization_2013.pdf.txtsosa_constrained_optimization_2013.pdf.txttext/plain70818http://repository.icesi.edu.co/biblioteca_digital/bitstream/10906/78332/2/sosa_constrained_optimization_2013.pdf.txt7f6315c3bf20eb88bbec122846bda7f3MD52ORIGINALsosa_constrained_optimization_2013.pdfsosa_constrained_optimization_2013.pdfapplication/pdf1349839http://repository.icesi.edu.co/biblioteca_digital/bitstream/10906/78332/1/sosa_constrained_optimization_2013.pdf33b8cf4152793eaa5480df1902ebfd04MD5110906/78332oai:repository.icesi.edu.co:10906/783322020-05-21 20:49:11.203Biblioteca Digital - Universidad icesicdcriollo@icesi.edu.co |
dc.title.spa.fl_str_mv |
Constrained optimization framework for joint inversion of geophysical data sets |
title |
Constrained optimization framework for joint inversion of geophysical data sets |
spellingShingle |
Constrained optimization framework for joint inversion of geophysical data sets Soluciones numéricas Teoría inversa Sismología computacional Numerical solutions Inverse theory Computational seismology Automatización y sistemas de control Automation Control system |
title_short |
Constrained optimization framework for joint inversion of geophysical data sets |
title_full |
Constrained optimization framework for joint inversion of geophysical data sets |
title_fullStr |
Constrained optimization framework for joint inversion of geophysical data sets |
title_full_unstemmed |
Constrained optimization framework for joint inversion of geophysical data sets |
title_sort |
Constrained optimization framework for joint inversion of geophysical data sets |
dc.creator.fl_str_mv |
Sosa Aguirre, Uram Anibal |
dc.contributor.author.spa.fl_str_mv |
Sosa Aguirre, Uram Anibal |
dc.subject.spa.fl_str_mv |
Soluciones numéricas Teoría inversa Sismología computacional Numerical solutions Inverse theory Computational seismology Automatización y sistemas de control Automation Control system |
topic |
Soluciones numéricas Teoría inversa Sismología computacional Numerical solutions Inverse theory Computational seismology Automatización y sistemas de control Automation Control system |
description |
Many experimental techniques in geophysics advance the understanding of Earth processes by estimating and interpreting Earth structure (e.g. velocity and/or density structure). Different types of geophysical data can be collected and analysed separately, sometimes resulting in inconsistent models of the Earth depending on the data used. We present a constrained optimization approach for a joint inversion least-squares (LSQ) algorithm to characterize 1-D Earth's structure. We use two geophysical data sets sensitive to shear velocities: receiver function and surface wave dispersion velocity observations. We study the use of bound constraints on the regularized inverse problem, which are more physical than the regularization parameters required by conventional unconstrained formulations. Specifically, we develop a constrained optimization formulation that is solved with a primal-dual interior-point (PDIP) method, and validate our results with a traditional, unconstrained formulation that is solved with a truncated singular value decomposition (TSVD) for a set of numerical experiments with synthetic crustal velocity models. We conclude that the PDIP results are as accurate as those from the regularized TSVD approach, are less affected by noise, and honour the geophysical constraints. © The Authors 2013. Published by Oxford University Press on behalf of The Royal Astronomical Society. |
publishDate |
2013 |
dc.date.issued.none.fl_str_mv |
2013-09-17 |
dc.date.accessioned.none.fl_str_mv |
2015-09-30T22:24:06Z |
dc.date.available.none.fl_str_mv |
2015-09-30T22:24:06Z |
dc.type.eng.fl_str_mv |
info:eu-repo/semantics/article |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.local.spa.fl_str_mv |
Artículo |
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info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
status_str |
publishedVersion |
dc.identifier.spa.fl_str_mv |
10.1093/gji/ggt326 |
dc.identifier.issn.none.fl_str_mv |
0956-540X |
dc.identifier.other.spa.fl_str_mv |
http://www.scopus.com/inward/record.url?eid=2-s2.0-84887566536&partnerID=tZOtx3y1 |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10906/78332 |
dc.identifier.instname.none.fl_str_mv |
instname: Universidad Icesi |
dc.identifier.reponame.none.fl_str_mv |
reponame: Biblioteca Digital |
dc.identifier.repourl.none.fl_str_mv |
repourl: https://repository.icesi.edu.co/ |
identifier_str_mv |
10.1093/gji/ggt326 0956-540X instname: Universidad Icesi reponame: Biblioteca Digital repourl: https://repository.icesi.edu.co/ |
url |
http://www.scopus.com/inward/record.url?eid=2-s2.0-84887566536&partnerID=tZOtx3y1 http://hdl.handle.net/10906/78332 |
dc.language.iso.eng.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.none.fl_str_mv |
Geophysical Journal International, Vol. 195, No. 3 - 2013 |
dc.rights.uri.none.fl_str_mv |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
dc.rights.license.none.fl_str_mv |
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) |
dc.rights.coar.none.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-nd/4.0/ Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
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Universidad ICESI |
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http://repository.icesi.edu.co/biblioteca_digital/bitstream/10906/78332/2/sosa_constrained_optimization_2013.pdf.txt http://repository.icesi.edu.co/biblioteca_digital/bitstream/10906/78332/1/sosa_constrained_optimization_2013.pdf |
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