A cyclic learning approach for improving pre-stack seismic processing

Abstract Current seismic processing workflows in the oil and gas industry involve several interactions between different experts to optimize the overall data quality in various tasks, such as noise attenuation, velocity analysis and horizon picking. While many machine learning-based approaches have...

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Autores principales:	Dario Augusto Borges Oliveira, Daniela Szwarcman, Rodrigo da Silva Ferreira, Semen Zaytsev, Daniil Semin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3058d7ac66554cf2a714ebf9122e8ffb
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id	oai:doaj.org-article:3058d7ac66554cf2a714ebf9122e8ffb
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spelling	oai:doaj.org-article:3058d7ac66554cf2a714ebf9122e8ffb2021-12-02T18:27:48ZA cyclic learning approach for improving pre-stack seismic processing10.1038/s41598-021-87794-82045-2322https://doaj.org/article/3058d7ac66554cf2a714ebf9122e8ffb2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87794-8https://doaj.org/toc/2045-2322Abstract Current seismic processing workflows in the oil and gas industry involve several interactions between different experts to optimize the overall data quality in various tasks, such as noise attenuation, velocity analysis and horizon picking. While many machine learning-based approaches have been proposed to support each of those steps, most of them disregard expert interactions to guide the overall optimization. This paper presents geocycles, a cyclic learning approach that mimics this iterative process, which can be applied to different pre-stack seismic processing tasks. Our method refactor these processes considering training, testing, and evaluation sub-tasks, which allow the selection of samples for greedy sequential processes targeting an overall optimum quality for very large seismic datasets. We present encouraging results showing that a cyclic structure and efficient quality metrics improved overall outcomes in up to 128% for two different seismic processing tasks in comparison to a 1-cycle machine learning approach.Dario Augusto Borges OliveiraDaniela SzwarcmanRodrigo da Silva FerreiraSemen ZaytsevDaniil SeminNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dario Augusto Borges Oliveira Daniela Szwarcman Rodrigo da Silva Ferreira Semen Zaytsev Daniil Semin A cyclic learning approach for improving pre-stack seismic processing
description	Abstract Current seismic processing workflows in the oil and gas industry involve several interactions between different experts to optimize the overall data quality in various tasks, such as noise attenuation, velocity analysis and horizon picking. While many machine learning-based approaches have been proposed to support each of those steps, most of them disregard expert interactions to guide the overall optimization. This paper presents geocycles, a cyclic learning approach that mimics this iterative process, which can be applied to different pre-stack seismic processing tasks. Our method refactor these processes considering training, testing, and evaluation sub-tasks, which allow the selection of samples for greedy sequential processes targeting an overall optimum quality for very large seismic datasets. We present encouraging results showing that a cyclic structure and efficient quality metrics improved overall outcomes in up to 128% for two different seismic processing tasks in comparison to a 1-cycle machine learning approach.
format	article
author	Dario Augusto Borges Oliveira Daniela Szwarcman Rodrigo da Silva Ferreira Semen Zaytsev Daniil Semin
author_facet	Dario Augusto Borges Oliveira Daniela Szwarcman Rodrigo da Silva Ferreira Semen Zaytsev Daniil Semin
author_sort	Dario Augusto Borges Oliveira
title	A cyclic learning approach for improving pre-stack seismic processing
title_short	A cyclic learning approach for improving pre-stack seismic processing
title_full	A cyclic learning approach for improving pre-stack seismic processing
title_fullStr	A cyclic learning approach for improving pre-stack seismic processing
title_full_unstemmed	A cyclic learning approach for improving pre-stack seismic processing
title_sort	cyclic learning approach for improving pre-stack seismic processing
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3058d7ac66554cf2a714ebf9122e8ffb
work_keys_str_mv	AT darioaugustoborgesoliveira acycliclearningapproachforimprovingprestackseismicprocessing AT danielaszwarcman acycliclearningapproachforimprovingprestackseismicprocessing AT rodrigodasilvaferreira acycliclearningapproachforimprovingprestackseismicprocessing AT semenzaytsev acycliclearningapproachforimprovingprestackseismicprocessing AT daniilsemin acycliclearningapproachforimprovingprestackseismicprocessing AT darioaugustoborgesoliveira cycliclearningapproachforimprovingprestackseismicprocessing AT danielaszwarcman cycliclearningapproachforimprovingprestackseismicprocessing AT rodrigodasilvaferreira cycliclearningapproachforimprovingprestackseismicprocessing AT semenzaytsev cycliclearningapproachforimprovingprestackseismicprocessing AT daniilsemin cycliclearningapproachforimprovingprestackseismicprocessing
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A cyclic learning approach for improving pre-stack seismic processing

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