An inverse Q filtering method with efficient Q estimation using a BP neural network
Abstract Inverse Q filtering is a common tool in seismic processing that can be used to improve the resolution of post-stack seismic data. However, it is difficult to obtain the Q value conveniently and precisely, which limits the precision and industrial application range of inverse Q filtering. In...
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Gespeichert in:
| Autor*in: |
Wu, Jizhong [verfasserIn] Shi, Ying [verfasserIn] Wang, Kexin [verfasserIn] Yang, Qianqian [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Acta geophysica - Springer International Publishing, 2006, 72(2024), 5 vom: 07. Jan., Seite 3255-3267 |
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| Übergeordnetes Werk: |
volume:72 ; year:2024 ; number:5 ; day:07 ; month:01 ; pages:3255-3267 |
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| DOI / URN: |
10.1007/s11600-023-01242-x |
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| Katalog-ID: |
SPR056711522 |
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| 520 | |a Abstract Inverse Q filtering is a common tool in seismic processing that can be used to improve the resolution of post-stack seismic data. However, it is difficult to obtain the Q value conveniently and precisely, which limits the precision and industrial application range of inverse Q filtering. In this study, a set of technology schemes based on a BP neural network is proposed to quickly acquire the Q field so that inverse Q filtering has the ability to adapt to inhomogeneous Q values. Based on the powerful nonlinear solving ability of the BP neural network, the functional mapping relationship between the Q value and spectral information of seismic data is established, which improves the anti-noise ability of Q value prediction. The application effect of the model and actual data verifies the effectiveness of the proposed scheme. | ||
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| 650 | 4 | |a Seismic resolution |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Wang, Kexin |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Qianqian |e verfasserin |4 aut | |
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10.1007/s11600-023-01242-x doi (DE-627)SPR056711522 (SPR)s11600-023-01242-x-e DE-627 ger DE-627 rakwb eng 550 VZ 38.70 bkl Wu, Jizhong verfasserin aut An inverse Q filtering method with efficient Q estimation using a BP neural network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Inverse Q filtering is a common tool in seismic processing that can be used to improve the resolution of post-stack seismic data. However, it is difficult to obtain the Q value conveniently and precisely, which limits the precision and industrial application range of inverse Q filtering. In this study, a set of technology schemes based on a BP neural network is proposed to quickly acquire the Q field so that inverse Q filtering has the ability to adapt to inhomogeneous Q values. Based on the powerful nonlinear solving ability of the BP neural network, the functional mapping relationship between the Q value and spectral information of seismic data is established, which improves the anti-noise ability of Q value prediction. The application effect of the model and actual data verifies the effectiveness of the proposed scheme. Quality factor (dpeaa)DE-He213 BP neural network (dpeaa)DE-He213 Seismic attenuation (dpeaa)DE-He213 Seismic resolution (dpeaa)DE-He213 Shi, Ying verfasserin aut Wang, Kexin verfasserin aut Yang, Qianqian verfasserin aut Enthalten in Acta geophysica Springer International Publishing, 2006 72(2024), 5 vom: 07. Jan., Seite 3255-3267 (DE-627)51061843X (DE-600)2231673-5 1895-7455 nnns volume:72 year:2024 number:5 day:07 month:01 pages:3255-3267 https://dx.doi.org/10.1007/s11600-023-01242-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.70 VZ AR 72 2024 5 07 01 3255-3267 |
| spelling |
10.1007/s11600-023-01242-x doi (DE-627)SPR056711522 (SPR)s11600-023-01242-x-e DE-627 ger DE-627 rakwb eng 550 VZ 38.70 bkl Wu, Jizhong verfasserin aut An inverse Q filtering method with efficient Q estimation using a BP neural network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Inverse Q filtering is a common tool in seismic processing that can be used to improve the resolution of post-stack seismic data. However, it is difficult to obtain the Q value conveniently and precisely, which limits the precision and industrial application range of inverse Q filtering. In this study, a set of technology schemes based on a BP neural network is proposed to quickly acquire the Q field so that inverse Q filtering has the ability to adapt to inhomogeneous Q values. Based on the powerful nonlinear solving ability of the BP neural network, the functional mapping relationship between the Q value and spectral information of seismic data is established, which improves the anti-noise ability of Q value prediction. The application effect of the model and actual data verifies the effectiveness of the proposed scheme. Quality factor (dpeaa)DE-He213 BP neural network (dpeaa)DE-He213 Seismic attenuation (dpeaa)DE-He213 Seismic resolution (dpeaa)DE-He213 Shi, Ying verfasserin aut Wang, Kexin verfasserin aut Yang, Qianqian verfasserin aut Enthalten in Acta geophysica Springer International Publishing, 2006 72(2024), 5 vom: 07. Jan., Seite 3255-3267 (DE-627)51061843X (DE-600)2231673-5 1895-7455 nnns volume:72 year:2024 number:5 day:07 month:01 pages:3255-3267 https://dx.doi.org/10.1007/s11600-023-01242-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.70 VZ AR 72 2024 5 07 01 3255-3267 |
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10.1007/s11600-023-01242-x doi (DE-627)SPR056711522 (SPR)s11600-023-01242-x-e DE-627 ger DE-627 rakwb eng 550 VZ 38.70 bkl Wu, Jizhong verfasserin aut An inverse Q filtering method with efficient Q estimation using a BP neural network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Inverse Q filtering is a common tool in seismic processing that can be used to improve the resolution of post-stack seismic data. However, it is difficult to obtain the Q value conveniently and precisely, which limits the precision and industrial application range of inverse Q filtering. In this study, a set of technology schemes based on a BP neural network is proposed to quickly acquire the Q field so that inverse Q filtering has the ability to adapt to inhomogeneous Q values. Based on the powerful nonlinear solving ability of the BP neural network, the functional mapping relationship between the Q value and spectral information of seismic data is established, which improves the anti-noise ability of Q value prediction. The application effect of the model and actual data verifies the effectiveness of the proposed scheme. Quality factor (dpeaa)DE-He213 BP neural network (dpeaa)DE-He213 Seismic attenuation (dpeaa)DE-He213 Seismic resolution (dpeaa)DE-He213 Shi, Ying verfasserin aut Wang, Kexin verfasserin aut Yang, Qianqian verfasserin aut Enthalten in Acta geophysica Springer International Publishing, 2006 72(2024), 5 vom: 07. Jan., Seite 3255-3267 (DE-627)51061843X (DE-600)2231673-5 1895-7455 nnns volume:72 year:2024 number:5 day:07 month:01 pages:3255-3267 https://dx.doi.org/10.1007/s11600-023-01242-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.70 VZ AR 72 2024 5 07 01 3255-3267 |
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10.1007/s11600-023-01242-x doi (DE-627)SPR056711522 (SPR)s11600-023-01242-x-e DE-627 ger DE-627 rakwb eng 550 VZ 38.70 bkl Wu, Jizhong verfasserin aut An inverse Q filtering method with efficient Q estimation using a BP neural network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Inverse Q filtering is a common tool in seismic processing that can be used to improve the resolution of post-stack seismic data. However, it is difficult to obtain the Q value conveniently and precisely, which limits the precision and industrial application range of inverse Q filtering. In this study, a set of technology schemes based on a BP neural network is proposed to quickly acquire the Q field so that inverse Q filtering has the ability to adapt to inhomogeneous Q values. Based on the powerful nonlinear solving ability of the BP neural network, the functional mapping relationship between the Q value and spectral information of seismic data is established, which improves the anti-noise ability of Q value prediction. The application effect of the model and actual data verifies the effectiveness of the proposed scheme. Quality factor (dpeaa)DE-He213 BP neural network (dpeaa)DE-He213 Seismic attenuation (dpeaa)DE-He213 Seismic resolution (dpeaa)DE-He213 Shi, Ying verfasserin aut Wang, Kexin verfasserin aut Yang, Qianqian verfasserin aut Enthalten in Acta geophysica Springer International Publishing, 2006 72(2024), 5 vom: 07. Jan., Seite 3255-3267 (DE-627)51061843X (DE-600)2231673-5 1895-7455 nnns volume:72 year:2024 number:5 day:07 month:01 pages:3255-3267 https://dx.doi.org/10.1007/s11600-023-01242-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.70 VZ AR 72 2024 5 07 01 3255-3267 |
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10.1007/s11600-023-01242-x doi (DE-627)SPR056711522 (SPR)s11600-023-01242-x-e DE-627 ger DE-627 rakwb eng 550 VZ 38.70 bkl Wu, Jizhong verfasserin aut An inverse Q filtering method with efficient Q estimation using a BP neural network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Inverse Q filtering is a common tool in seismic processing that can be used to improve the resolution of post-stack seismic data. However, it is difficult to obtain the Q value conveniently and precisely, which limits the precision and industrial application range of inverse Q filtering. In this study, a set of technology schemes based on a BP neural network is proposed to quickly acquire the Q field so that inverse Q filtering has the ability to adapt to inhomogeneous Q values. Based on the powerful nonlinear solving ability of the BP neural network, the functional mapping relationship between the Q value and spectral information of seismic data is established, which improves the anti-noise ability of Q value prediction. The application effect of the model and actual data verifies the effectiveness of the proposed scheme. Quality factor (dpeaa)DE-He213 BP neural network (dpeaa)DE-He213 Seismic attenuation (dpeaa)DE-He213 Seismic resolution (dpeaa)DE-He213 Shi, Ying verfasserin aut Wang, Kexin verfasserin aut Yang, Qianqian verfasserin aut Enthalten in Acta geophysica Springer International Publishing, 2006 72(2024), 5 vom: 07. Jan., Seite 3255-3267 (DE-627)51061843X (DE-600)2231673-5 1895-7455 nnns volume:72 year:2024 number:5 day:07 month:01 pages:3255-3267 https://dx.doi.org/10.1007/s11600-023-01242-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.70 VZ AR 72 2024 5 07 01 3255-3267 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Inverse Q filtering is a common tool in seismic processing that can be used to improve the resolution of post-stack seismic data. However, it is difficult to obtain the Q value conveniently and precisely, which limits the precision and industrial application range of inverse Q filtering. In this study, a set of technology schemes based on a BP neural network is proposed to quickly acquire the Q field so that inverse Q filtering has the ability to adapt to inhomogeneous Q values. 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Wu, Jizhong |
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Wu, Jizhong ddc 550 bkl 38.70 misc Quality factor misc BP neural network misc Seismic attenuation misc Seismic resolution An inverse Q filtering method with efficient Q estimation using a BP neural network |
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550 VZ 38.70 bkl An inverse Q filtering method with efficient Q estimation using a BP neural network Quality factor (dpeaa)DE-He213 BP neural network (dpeaa)DE-He213 Seismic attenuation (dpeaa)DE-He213 Seismic resolution (dpeaa)DE-He213 |
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an inverse q filtering method with efficient q estimation using a bp neural network |
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An inverse Q filtering method with efficient Q estimation using a BP neural network |
| abstract |
Abstract Inverse Q filtering is a common tool in seismic processing that can be used to improve the resolution of post-stack seismic data. However, it is difficult to obtain the Q value conveniently and precisely, which limits the precision and industrial application range of inverse Q filtering. In this study, a set of technology schemes based on a BP neural network is proposed to quickly acquire the Q field so that inverse Q filtering has the ability to adapt to inhomogeneous Q values. Based on the powerful nonlinear solving ability of the BP neural network, the functional mapping relationship between the Q value and spectral information of seismic data is established, which improves the anti-noise ability of Q value prediction. The application effect of the model and actual data verifies the effectiveness of the proposed scheme. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Inverse Q filtering is a common tool in seismic processing that can be used to improve the resolution of post-stack seismic data. However, it is difficult to obtain the Q value conveniently and precisely, which limits the precision and industrial application range of inverse Q filtering. In this study, a set of technology schemes based on a BP neural network is proposed to quickly acquire the Q field so that inverse Q filtering has the ability to adapt to inhomogeneous Q values. Based on the powerful nonlinear solving ability of the BP neural network, the functional mapping relationship between the Q value and spectral information of seismic data is established, which improves the anti-noise ability of Q value prediction. The application effect of the model and actual data verifies the effectiveness of the proposed scheme. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Inverse Q filtering is a common tool in seismic processing that can be used to improve the resolution of post-stack seismic data. However, it is difficult to obtain the Q value conveniently and precisely, which limits the precision and industrial application range of inverse Q filtering. In this study, a set of technology schemes based on a BP neural network is proposed to quickly acquire the Q field so that inverse Q filtering has the ability to adapt to inhomogeneous Q values. Based on the powerful nonlinear solving ability of the BP neural network, the functional mapping relationship between the Q value and spectral information of seismic data is established, which improves the anti-noise ability of Q value prediction. The application effect of the model and actual data verifies the effectiveness of the proposed scheme. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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An inverse Q filtering method with efficient Q estimation using a BP neural network |
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https://dx.doi.org/10.1007/s11600-023-01242-x |
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Shi, Ying Wang, Kexin Yang, Qianqian |
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Shi, Ying Wang, Kexin Yang, Qianqian |
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10.1007/s11600-023-01242-x |
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2024-07-25T04:51:49.537Z |
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| score |
7.3997936 |