Multimodal variational autoencoder for inverse problems in geophysics: application to a 1-D magnetotelluric problem

Estimating subsurface properties from geophysical measurements is a common inverse problem. Several Bayesian methods currently aim to find the solution to a geophysical inverse problem and quantify its uncertainty. However, most geophysical applications exhibit more than one plausible solution. Here...

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Detalhes bibliográficos
Autores: Rodriguez, O., Taylor, J.M., Pardo, D.
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Recursos:Basque Center for Applied Mathematics (BCAM)
Repositorio:BIRD. BCAM's Institutional Repository Data
OAI Identifier:oai:bird.bcamath.org:20.500.11824/1732
Acesso em linha:http://hdl.handle.net/20.500.11824/1732
Access Level:acceso abierto
Palavra-chave:Magnetotellurics
Inverse theory
Numerical modelling
Probabilistic forecasting
Statistical method
Variational autoencoder
Multimodal Models
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spelling Multimodal variational autoencoder for inverse problems in geophysics: application to a 1-D magnetotelluric problemRodriguez, O.Taylor, J.M.Pardo, D.MagnetotelluricsInverse theoryNumerical modellingProbabilistic forecastingStatistical methodVariational autoencoderMultimodal ModelsEstimating subsurface properties from geophysical measurements is a common inverse problem. Several Bayesian methods currently aim to find the solution to a geophysical inverse problem and quantify its uncertainty. However, most geophysical applications exhibit more than one plausible solution. Here, we propose a multimodal variational autoencoder model that employs a mixture of truncated Gaussian densities to provide multiple solutions, along with their probability of occurrence and a quantification of their uncertainty. This autoencoder is assembled with an encoder and a decoder, where the first one provides a mixture of truncated Gaussian densities from a neural network, and the second is the numerical solution of the forward problem given by the geophysical approach. The proposed method is illustrated with a 1-D magnetotelluric inverse problem and recovers multiple plausible solutions with different uncertainty quantification maps and probabilities that are in agreement with known physical observations.PDC2021-121093-I00 IA4TES202420242023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/20.500.11824/1732reponame:BIRD. BCAM's Institutional Repository Datainstname:Basque Center for Applied Mathematics (BCAM)Ingléshttps://academic.oup.com/gji/article/235/3/2598/7280999info:eu-repo/grantAgreement/EC/H2020/777778info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/CEX2021-001142-Sinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108111RB-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-114189RB-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-132783B-I00info:eu-repo/grantAgreement/Gobierno Vasco/ELKARTEK/info:eu-repo/grantAgreement/Gobierno Vasco/BERC/BERC.2022-2025Reconocimiento-NoComercial-CompartirIgual 3.0 Españahttp://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:bird.bcamath.org:20.500.11824/17322026-06-19T12:47:47Z
dc.title.none.fl_str_mv Multimodal variational autoencoder for inverse problems in geophysics: application to a 1-D magnetotelluric problem
title Multimodal variational autoencoder for inverse problems in geophysics: application to a 1-D magnetotelluric problem
spellingShingle Multimodal variational autoencoder for inverse problems in geophysics: application to a 1-D magnetotelluric problem
Rodriguez, O.
Magnetotellurics
Inverse theory
Numerical modelling
Probabilistic forecasting
Statistical method
Variational autoencoder
Multimodal Models
title_short Multimodal variational autoencoder for inverse problems in geophysics: application to a 1-D magnetotelluric problem
title_full Multimodal variational autoencoder for inverse problems in geophysics: application to a 1-D magnetotelluric problem
title_fullStr Multimodal variational autoencoder for inverse problems in geophysics: application to a 1-D magnetotelluric problem
title_full_unstemmed Multimodal variational autoencoder for inverse problems in geophysics: application to a 1-D magnetotelluric problem
title_sort Multimodal variational autoencoder for inverse problems in geophysics: application to a 1-D magnetotelluric problem
dc.creator.none.fl_str_mv Rodriguez, O.
Taylor, J.M.
Pardo, D.
author Rodriguez, O.
author_facet Rodriguez, O.
Taylor, J.M.
Pardo, D.
author_role author
author2 Taylor, J.M.
Pardo, D.
author2_role author
author
dc.subject.none.fl_str_mv Magnetotellurics
Inverse theory
Numerical modelling
Probabilistic forecasting
Statistical method
Variational autoencoder
Multimodal Models
topic Magnetotellurics
Inverse theory
Numerical modelling
Probabilistic forecasting
Statistical method
Variational autoencoder
Multimodal Models
description Estimating subsurface properties from geophysical measurements is a common inverse problem. Several Bayesian methods currently aim to find the solution to a geophysical inverse problem and quantify its uncertainty. However, most geophysical applications exhibit more than one plausible solution. Here, we propose a multimodal variational autoencoder model that employs a mixture of truncated Gaussian densities to provide multiple solutions, along with their probability of occurrence and a quantification of their uncertainty. This autoencoder is assembled with an encoder and a decoder, where the first one provides a mixture of truncated Gaussian densities from a neural network, and the second is the numerical solution of the forward problem given by the geophysical approach. The proposed method is illustrated with a 1-D magnetotelluric inverse problem and recovers multiple plausible solutions with different uncertainty quantification maps and probabilities that are in agreement with known physical observations.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.11824/1732
url http://hdl.handle.net/20.500.11824/1732
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://academic.oup.com/gji/article/235/3/2598/7280999
info:eu-repo/grantAgreement/EC/H2020/777778
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/CEX2021-001142-S
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108111RB-I00
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-114189RB-I00
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-132783B-I00
info:eu-repo/grantAgreement/Gobierno Vasco/ELKARTEK/
info:eu-repo/grantAgreement/Gobierno Vasco/BERC/BERC.2022-2025
dc.rights.none.fl_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:BIRD. BCAM's Institutional Repository Data
instname:Basque Center for Applied Mathematics (BCAM)
instname_str Basque Center for Applied Mathematics (BCAM)
reponame_str BIRD. BCAM's Institutional Repository Data
collection BIRD. BCAM's Institutional Repository Data
repository.name.fl_str_mv
repository.mail.fl_str_mv
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