Application of neural networks modeling for interpretation of acoustic logging traces

The neural networks are proposed for application as a method for automatic P- and S-waves onset time-picking on sonic logging. The neural network models of acoustic emission preceding phase onset are trained and used to discriminate noise and desired signal, the last one being packets of  longitudin...

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Datum:2015
Hauptverfasser: Lazarenko, M., Gerasimenko, O.
Format: Artikel
Sprache:Russisch
Veröffentlicht: S. Subbotin Institute of Geophysics of the NAS of Ukraine 2015
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Online Zugang:https://journals.uran.ua/geofizicheskiy/article/view/111160
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Geofizicheskiy Zhurnal
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author Lazarenko, M.
Gerasimenko, O.
author_facet Lazarenko, M.
Gerasimenko, O.
author_sort Lazarenko, M.
baseUrl_str
collection OJS
datestamp_date 2020-10-07T11:38:43Z
description The neural networks are proposed for application as a method for automatic P- and S-waves onset time-picking on sonic logging. The neural network models of acoustic emission preceding phase onset are trained and used to discriminate noise and desired signal, the last one being packets of  longitudinal  and transversal waves. The given algorithm is easily adapted to existing systems and is able to provide both processing of logging tracks in online regime and high productivity of archive materials interpretation.
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spelling journalsuranua-geofizicheskiy-article-1111602020-10-07T11:38:43Z Application of neural networks modeling for interpretation of acoustic logging traces Lazarenko, M. Gerasimenko, O. neural network modelling acoustic logging wave arrival time adaptive threshold level longitudinal and transversal waves The neural networks are proposed for application as a method for automatic P- and S-waves onset time-picking on sonic logging. The neural network models of acoustic emission preceding phase onset are trained and used to discriminate noise and desired signal, the last one being packets of  longitudinal  and transversal waves. The given algorithm is easily adapted to existing systems and is able to provide both processing of logging tracks in online regime and high productivity of archive materials interpretation. S. Subbotin Institute of Geophysics of the NAS of Ukraine 2015-10-01 Article Article application/pdf https://journals.uran.ua/geofizicheskiy/article/view/111160 10.24028/gzh.0203-3100.v37i5.2015.111160 Geofizicheskiy Zhurnal; Vol. 37 No. 5 (2015); 162-167 Геофизический журнал; Том 37 № 5 (2015); 162-167 Геофізичний журнал; Том 37 № 5 (2015); 162-167 2524-1052 0203-3100 ru https://journals.uran.ua/geofizicheskiy/article/view/111160/106025 Copyright (c) 2020 Geofizicheskiy Zhurnal https://creativecommons.org/licenses/by/4.0
spellingShingle neural network modelling
acoustic logging
wave arrival time
adaptive threshold level
longitudinal and transversal waves
Lazarenko, M.
Gerasimenko, O.
Application of neural networks modeling for interpretation of acoustic logging traces
title Application of neural networks modeling for interpretation of acoustic logging traces
title_full Application of neural networks modeling for interpretation of acoustic logging traces
title_fullStr Application of neural networks modeling for interpretation of acoustic logging traces
title_full_unstemmed Application of neural networks modeling for interpretation of acoustic logging traces
title_short Application of neural networks modeling for interpretation of acoustic logging traces
title_sort application of neural networks modeling for interpretation of acoustic logging traces
topic neural network modelling
acoustic logging
wave arrival time
adaptive threshold level
longitudinal and transversal waves
topic_facet neural network modelling
acoustic logging
wave arrival time
adaptive threshold level
longitudinal and transversal waves
url https://journals.uran.ua/geofizicheskiy/article/view/111160
work_keys_str_mv AT lazarenkom applicationofneuralnetworksmodelingforinterpretationofacousticloggingtraces
AT gerasimenkoo applicationofneuralnetworksmodelingforinterpretationofacousticloggingtraces