Treatment of geophysical data as a non-stationary process

Detalhes bibliográficos
Autor(a) principal: Rocha,Marcus P.C.
Data de Publicação: 2003
Outros Autores: Leite,Lourenildo W.B.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Computational & Applied Mathematics
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022003000200001
Resumo: The Kalman-Bucy method is here analized and applied to the solution of a specific filtering problem to increase the signal message/noise ratio. The method is a time domain treatment of a geophysical process classified as stochastic non-stationary. The derivation of the estimator is based on the relationship between the Kalman-Bucy and Wiener approaches for linear systems. In the present work we emphasize the criterion used, the model with apriori information, the algorithm, and the quality as related to the results. The examples are for the ideal well-log response, and the results indicate that this method can be used on a variety of geophysical data treatments, and its study clearly offers a proper insight into modeling and processing of geophysical problems.
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spelling Treatment of geophysical data as a non-stationary processstochastic processKalmann-Bucy filterdeconvolutionstate spaceThe Kalman-Bucy method is here analized and applied to the solution of a specific filtering problem to increase the signal message/noise ratio. The method is a time domain treatment of a geophysical process classified as stochastic non-stationary. The derivation of the estimator is based on the relationship between the Kalman-Bucy and Wiener approaches for linear systems. In the present work we emphasize the criterion used, the model with apriori information, the algorithm, and the quality as related to the results. The examples are for the ideal well-log response, and the results indicate that this method can be used on a variety of geophysical data treatments, and its study clearly offers a proper insight into modeling and processing of geophysical problems.Sociedade Brasileira de Matemática Aplicada e Computacional2003-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022003000200001Computational & Applied Mathematics v.22 n.2 2003reponame:Computational & Applied Mathematicsinstname:Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)instacron:SBMAC10.1590/S0101-82052003000200001info:eu-repo/semantics/openAccessRocha,Marcus P.C.Leite,Lourenildo W.B.eng2004-07-20T00:00:00Zoai:scielo:S1807-03022003000200001Revistahttps://www.scielo.br/j/cam/ONGhttps://old.scielo.br/oai/scielo-oai.php||sbmac@sbmac.org.br1807-03022238-3603opendoar:2004-07-20T00:00Computational & Applied Mathematics - Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)false
dc.title.none.fl_str_mv Treatment of geophysical data as a non-stationary process
title Treatment of geophysical data as a non-stationary process
spellingShingle Treatment of geophysical data as a non-stationary process
Rocha,Marcus P.C.
stochastic process
Kalmann-Bucy filter
deconvolution
state space
title_short Treatment of geophysical data as a non-stationary process
title_full Treatment of geophysical data as a non-stationary process
title_fullStr Treatment of geophysical data as a non-stationary process
title_full_unstemmed Treatment of geophysical data as a non-stationary process
title_sort Treatment of geophysical data as a non-stationary process
author Rocha,Marcus P.C.
author_facet Rocha,Marcus P.C.
Leite,Lourenildo W.B.
author_role author
author2 Leite,Lourenildo W.B.
author2_role author
dc.contributor.author.fl_str_mv Rocha,Marcus P.C.
Leite,Lourenildo W.B.
dc.subject.por.fl_str_mv stochastic process
Kalmann-Bucy filter
deconvolution
state space
topic stochastic process
Kalmann-Bucy filter
deconvolution
state space
description The Kalman-Bucy method is here analized and applied to the solution of a specific filtering problem to increase the signal message/noise ratio. The method is a time domain treatment of a geophysical process classified as stochastic non-stationary. The derivation of the estimator is based on the relationship between the Kalman-Bucy and Wiener approaches for linear systems. In the present work we emphasize the criterion used, the model with apriori information, the algorithm, and the quality as related to the results. The examples are for the ideal well-log response, and the results indicate that this method can be used on a variety of geophysical data treatments, and its study clearly offers a proper insight into modeling and processing of geophysical problems.
publishDate 2003
dc.date.none.fl_str_mv 2003-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022003000200001
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dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S0101-82052003000200001
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Matemática Aplicada e Computacional
publisher.none.fl_str_mv Sociedade Brasileira de Matemática Aplicada e Computacional
dc.source.none.fl_str_mv Computational & Applied Mathematics v.22 n.2 2003
reponame:Computational & Applied Mathematics
instname:Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)
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collection Computational & Applied Mathematics
repository.name.fl_str_mv Computational & Applied Mathematics - Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)
repository.mail.fl_str_mv ||sbmac@sbmac.org.br
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