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...
Збережено в:
| Дата: | 2015 |
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| Автори: | , |
| Формат: | Стаття |
| Мова: | rus |
| Опубліковано: |
Subbotin Institute of Geophysics of the NAS of Ukraine
2015
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| Теми: | |
| Онлайн доступ: | https://journals.uran.ua/geofizicheskiy/article/view/111160 |
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| Назва журналу: | Geofizicheskiy Zhurnal |
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Geofizicheskiy Zhurnal| id |
journalsuranua-geofizicheskiy-article-111160 |
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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. 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 rus https://journals.uran.ua/geofizicheskiy/article/view/111160/106025 Copyright (c) 2020 Geofizicheskiy Zhurnal https://creativecommons.org/licenses/by/4.0 |
| institution |
Geofizicheskiy Zhurnal |
| collection |
OJS |
| language |
rus |
| topic |
neural network modelling acoustic logging wave arrival time adaptive threshold level longitudinal and transversal waves |
| 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 |
| topic_facet |
neural network modelling acoustic logging wave arrival time adaptive threshold level longitudinal and transversal waves |
| format |
Article |
| author |
Lazarenko, M. Gerasimenko, O. |
| author_facet |
Lazarenko, M. Gerasimenko, O. |
| author_sort |
Lazarenko, M. |
| title |
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_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_sort |
application of neural networks modeling for interpretation of acoustic logging traces |
| 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. |
| publisher |
Subbotin Institute of Geophysics of the NAS of Ukraine |
| publishDate |
2015 |
| url |
https://journals.uran.ua/geofizicheskiy/article/view/111160 |
| work_keys_str_mv |
AT lazarenkom applicationofneuralnetworksmodelingforinterpretationofacousticloggingtraces AT gerasimenkoo applicationofneuralnetworksmodelingforinterpretationofacousticloggingtraces |
| first_indexed |
2024-04-21T19:40:07Z |
| last_indexed |
2024-04-21T19:40:07Z |
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