Adaptive iterative deblending of simultaneous-source seismic data based on sparse inversion
Abstract Deblending of simultaneous-source seismic data is becoming more popular in seismic exploration since it can greatly improve the efficiency of seismic acquisition and reduce acquisition cost. At present, the deblending methods of simultaneous-source seismic data are mainly divided into two t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wei, Yajie [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Acta geophysica - Warsaw : De Gruyter Open, 2006, 69(2021), 2 vom: 19. März, Seite 497-507 |
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| Übergeordnetes Werk: |
volume:69 ; year:2021 ; number:2 ; day:19 ; month:03 ; pages:497-507 |
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| DOI / URN: |
10.1007/s11600-021-00561-1 |
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SPR043871097 |
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| 245 | 1 | 0 | |a Adaptive iterative deblending of simultaneous-source seismic data based on sparse inversion |
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| 520 | |a Abstract Deblending of simultaneous-source seismic data is becoming more popular in seismic exploration since it can greatly improve the efficiency of seismic acquisition and reduce acquisition cost. At present, the deblending methods of simultaneous-source seismic data are mainly divided into two types: filtering method and sparse inversion method. Compared with the filtering method, the sparse inversion method has higher precision, but the selection of its parameter value mainly depends on experience, which is not suitable for large-scale seismic data processing. In this paper, an adaptive iterative deblending method based on sparse inversion is proposed. By improving the original iterative solution method of regularization inversion model, the effective signal and blending noise are weakened simultaneously in the iterative process, so that the energy intensity of blending noise is consistent with that of the effective signal in each iterative, so as to ensure the consistency of the regular parameter calculation method of each iteration. By analyzing the distribution of coefficients in the curvelet domain of pseudo-deblending data and blending noise, it is concluded that the value of regular parameters is the maximum amplitude of residual pseudo-deblending data in the curvelet domain multiplied by a coefficient between 0 and 1. In the process of iterative deblending, the regularized parameters are obtained adaptively from the data itself. It not only ensures the accuracy of the calculation results, but also improves the calculation efficiency, which is suitable for large-scale seismic data processing. | ||
| 650 | 4 | |a Deblending |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Sparse inversion |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Adaptive |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Curvelet transform |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Cao, Jingjie |0 (orcid)0000-0001-8176-5651 |4 aut | |
| 700 | 1 | |a Huang, Xiaogang |4 aut | |
| 700 | 1 | |a Chen, Xue |4 aut | |
| 700 | 1 | |a Cai, Zhicheng |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Acta geophysica |d Warsaw : De Gruyter Open, 2006 |g 69(2021), 2 vom: 19. März, Seite 497-507 |w (DE-627)51061843X |w (DE-600)2231673-5 |x 1895-7455 |7 nnns |
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10.1007/s11600-021-00561-1 doi (DE-627)SPR043871097 (SPR)s11600-021-00561-1-e DE-627 ger DE-627 rakwb eng Wei, Yajie verfasserin (orcid)0000-0002-0531-3175 aut Adaptive iterative deblending of simultaneous-source seismic data based on sparse inversion 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2021 Abstract Deblending of simultaneous-source seismic data is becoming more popular in seismic exploration since it can greatly improve the efficiency of seismic acquisition and reduce acquisition cost. At present, the deblending methods of simultaneous-source seismic data are mainly divided into two types: filtering method and sparse inversion method. Compared with the filtering method, the sparse inversion method has higher precision, but the selection of its parameter value mainly depends on experience, which is not suitable for large-scale seismic data processing. In this paper, an adaptive iterative deblending method based on sparse inversion is proposed. By improving the original iterative solution method of regularization inversion model, the effective signal and blending noise are weakened simultaneously in the iterative process, so that the energy intensity of blending noise is consistent with that of the effective signal in each iterative, so as to ensure the consistency of the regular parameter calculation method of each iteration. By analyzing the distribution of coefficients in the curvelet domain of pseudo-deblending data and blending noise, it is concluded that the value of regular parameters is the maximum amplitude of residual pseudo-deblending data in the curvelet domain multiplied by a coefficient between 0 and 1. In the process of iterative deblending, the regularized parameters are obtained adaptively from the data itself. It not only ensures the accuracy of the calculation results, but also improves the calculation efficiency, which is suitable for large-scale seismic data processing. Deblending (dpeaa)DE-He213 Sparse inversion (dpeaa)DE-He213 Adaptive (dpeaa)DE-He213 Curvelet transform (dpeaa)DE-He213 Cao, Jingjie (orcid)0000-0001-8176-5651 aut Huang, Xiaogang aut Chen, Xue aut Cai, Zhicheng aut Enthalten in Acta geophysica Warsaw : De Gruyter Open, 2006 69(2021), 2 vom: 19. März, Seite 497-507 (DE-627)51061843X (DE-600)2231673-5 1895-7455 nnns volume:69 year:2021 number:2 day:19 month:03 pages:497-507 https://dx.doi.org/10.1007/s11600-021-00561-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2008 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_4246 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 AR 69 2021 2 19 03 497-507 |
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10.1007/s11600-021-00561-1 doi (DE-627)SPR043871097 (SPR)s11600-021-00561-1-e DE-627 ger DE-627 rakwb eng Wei, Yajie verfasserin (orcid)0000-0002-0531-3175 aut Adaptive iterative deblending of simultaneous-source seismic data based on sparse inversion 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2021 Abstract Deblending of simultaneous-source seismic data is becoming more popular in seismic exploration since it can greatly improve the efficiency of seismic acquisition and reduce acquisition cost. At present, the deblending methods of simultaneous-source seismic data are mainly divided into two types: filtering method and sparse inversion method. Compared with the filtering method, the sparse inversion method has higher precision, but the selection of its parameter value mainly depends on experience, which is not suitable for large-scale seismic data processing. In this paper, an adaptive iterative deblending method based on sparse inversion is proposed. By improving the original iterative solution method of regularization inversion model, the effective signal and blending noise are weakened simultaneously in the iterative process, so that the energy intensity of blending noise is consistent with that of the effective signal in each iterative, so as to ensure the consistency of the regular parameter calculation method of each iteration. By analyzing the distribution of coefficients in the curvelet domain of pseudo-deblending data and blending noise, it is concluded that the value of regular parameters is the maximum amplitude of residual pseudo-deblending data in the curvelet domain multiplied by a coefficient between 0 and 1. In the process of iterative deblending, the regularized parameters are obtained adaptively from the data itself. It not only ensures the accuracy of the calculation results, but also improves the calculation efficiency, which is suitable for large-scale seismic data processing. Deblending (dpeaa)DE-He213 Sparse inversion (dpeaa)DE-He213 Adaptive (dpeaa)DE-He213 Curvelet transform (dpeaa)DE-He213 Cao, Jingjie (orcid)0000-0001-8176-5651 aut Huang, Xiaogang aut Chen, Xue aut Cai, Zhicheng aut Enthalten in Acta geophysica Warsaw : De Gruyter Open, 2006 69(2021), 2 vom: 19. März, Seite 497-507 (DE-627)51061843X (DE-600)2231673-5 1895-7455 nnns volume:69 year:2021 number:2 day:19 month:03 pages:497-507 https://dx.doi.org/10.1007/s11600-021-00561-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2008 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_4246 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 AR 69 2021 2 19 03 497-507 |
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10.1007/s11600-021-00561-1 doi (DE-627)SPR043871097 (SPR)s11600-021-00561-1-e DE-627 ger DE-627 rakwb eng Wei, Yajie verfasserin (orcid)0000-0002-0531-3175 aut Adaptive iterative deblending of simultaneous-source seismic data based on sparse inversion 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2021 Abstract Deblending of simultaneous-source seismic data is becoming more popular in seismic exploration since it can greatly improve the efficiency of seismic acquisition and reduce acquisition cost. At present, the deblending methods of simultaneous-source seismic data are mainly divided into two types: filtering method and sparse inversion method. Compared with the filtering method, the sparse inversion method has higher precision, but the selection of its parameter value mainly depends on experience, which is not suitable for large-scale seismic data processing. In this paper, an adaptive iterative deblending method based on sparse inversion is proposed. By improving the original iterative solution method of regularization inversion model, the effective signal and blending noise are weakened simultaneously in the iterative process, so that the energy intensity of blending noise is consistent with that of the effective signal in each iterative, so as to ensure the consistency of the regular parameter calculation method of each iteration. By analyzing the distribution of coefficients in the curvelet domain of pseudo-deblending data and blending noise, it is concluded that the value of regular parameters is the maximum amplitude of residual pseudo-deblending data in the curvelet domain multiplied by a coefficient between 0 and 1. In the process of iterative deblending, the regularized parameters are obtained adaptively from the data itself. It not only ensures the accuracy of the calculation results, but also improves the calculation efficiency, which is suitable for large-scale seismic data processing. Deblending (dpeaa)DE-He213 Sparse inversion (dpeaa)DE-He213 Adaptive (dpeaa)DE-He213 Curvelet transform (dpeaa)DE-He213 Cao, Jingjie (orcid)0000-0001-8176-5651 aut Huang, Xiaogang aut Chen, Xue aut Cai, Zhicheng aut Enthalten in Acta geophysica Warsaw : De Gruyter Open, 2006 69(2021), 2 vom: 19. März, Seite 497-507 (DE-627)51061843X (DE-600)2231673-5 1895-7455 nnns volume:69 year:2021 number:2 day:19 month:03 pages:497-507 https://dx.doi.org/10.1007/s11600-021-00561-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2008 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_4246 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 AR 69 2021 2 19 03 497-507 |
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10.1007/s11600-021-00561-1 doi (DE-627)SPR043871097 (SPR)s11600-021-00561-1-e DE-627 ger DE-627 rakwb eng Wei, Yajie verfasserin (orcid)0000-0002-0531-3175 aut Adaptive iterative deblending of simultaneous-source seismic data based on sparse inversion 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2021 Abstract Deblending of simultaneous-source seismic data is becoming more popular in seismic exploration since it can greatly improve the efficiency of seismic acquisition and reduce acquisition cost. At present, the deblending methods of simultaneous-source seismic data are mainly divided into two types: filtering method and sparse inversion method. Compared with the filtering method, the sparse inversion method has higher precision, but the selection of its parameter value mainly depends on experience, which is not suitable for large-scale seismic data processing. In this paper, an adaptive iterative deblending method based on sparse inversion is proposed. By improving the original iterative solution method of regularization inversion model, the effective signal and blending noise are weakened simultaneously in the iterative process, so that the energy intensity of blending noise is consistent with that of the effective signal in each iterative, so as to ensure the consistency of the regular parameter calculation method of each iteration. By analyzing the distribution of coefficients in the curvelet domain of pseudo-deblending data and blending noise, it is concluded that the value of regular parameters is the maximum amplitude of residual pseudo-deblending data in the curvelet domain multiplied by a coefficient between 0 and 1. In the process of iterative deblending, the regularized parameters are obtained adaptively from the data itself. It not only ensures the accuracy of the calculation results, but also improves the calculation efficiency, which is suitable for large-scale seismic data processing. Deblending (dpeaa)DE-He213 Sparse inversion (dpeaa)DE-He213 Adaptive (dpeaa)DE-He213 Curvelet transform (dpeaa)DE-He213 Cao, Jingjie (orcid)0000-0001-8176-5651 aut Huang, Xiaogang aut Chen, Xue aut Cai, Zhicheng aut Enthalten in Acta geophysica Warsaw : De Gruyter Open, 2006 69(2021), 2 vom: 19. März, Seite 497-507 (DE-627)51061843X (DE-600)2231673-5 1895-7455 nnns volume:69 year:2021 number:2 day:19 month:03 pages:497-507 https://dx.doi.org/10.1007/s11600-021-00561-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2008 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_4246 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 AR 69 2021 2 19 03 497-507 |
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10.1007/s11600-021-00561-1 doi (DE-627)SPR043871097 (SPR)s11600-021-00561-1-e DE-627 ger DE-627 rakwb eng Wei, Yajie verfasserin (orcid)0000-0002-0531-3175 aut Adaptive iterative deblending of simultaneous-source seismic data based on sparse inversion 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2021 Abstract Deblending of simultaneous-source seismic data is becoming more popular in seismic exploration since it can greatly improve the efficiency of seismic acquisition and reduce acquisition cost. At present, the deblending methods of simultaneous-source seismic data are mainly divided into two types: filtering method and sparse inversion method. Compared with the filtering method, the sparse inversion method has higher precision, but the selection of its parameter value mainly depends on experience, which is not suitable for large-scale seismic data processing. In this paper, an adaptive iterative deblending method based on sparse inversion is proposed. By improving the original iterative solution method of regularization inversion model, the effective signal and blending noise are weakened simultaneously in the iterative process, so that the energy intensity of blending noise is consistent with that of the effective signal in each iterative, so as to ensure the consistency of the regular parameter calculation method of each iteration. By analyzing the distribution of coefficients in the curvelet domain of pseudo-deblending data and blending noise, it is concluded that the value of regular parameters is the maximum amplitude of residual pseudo-deblending data in the curvelet domain multiplied by a coefficient between 0 and 1. In the process of iterative deblending, the regularized parameters are obtained adaptively from the data itself. It not only ensures the accuracy of the calculation results, but also improves the calculation efficiency, which is suitable for large-scale seismic data processing. Deblending (dpeaa)DE-He213 Sparse inversion (dpeaa)DE-He213 Adaptive (dpeaa)DE-He213 Curvelet transform (dpeaa)DE-He213 Cao, Jingjie (orcid)0000-0001-8176-5651 aut Huang, Xiaogang aut Chen, Xue aut Cai, Zhicheng aut Enthalten in Acta geophysica Warsaw : De Gruyter Open, 2006 69(2021), 2 vom: 19. März, Seite 497-507 (DE-627)51061843X (DE-600)2231673-5 1895-7455 nnns volume:69 year:2021 number:2 day:19 month:03 pages:497-507 https://dx.doi.org/10.1007/s11600-021-00561-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2008 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_4246 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 AR 69 2021 2 19 03 497-507 |
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Enthalten in Acta geophysica 69(2021), 2 vom: 19. März, Seite 497-507 volume:69 year:2021 number:2 day:19 month:03 pages:497-507 |
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Wei, Yajie @@aut@@ Cao, Jingjie @@aut@@ Huang, Xiaogang @@aut@@ Chen, Xue @@aut@@ Cai, Zhicheng @@aut@@ |
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At present, the deblending methods of simultaneous-source seismic data are mainly divided into two types: filtering method and sparse inversion method. Compared with the filtering method, the sparse inversion method has higher precision, but the selection of its parameter value mainly depends on experience, which is not suitable for large-scale seismic data processing. In this paper, an adaptive iterative deblending method based on sparse inversion is proposed. By improving the original iterative solution method of regularization inversion model, the effective signal and blending noise are weakened simultaneously in the iterative process, so that the energy intensity of blending noise is consistent with that of the effective signal in each iterative, so as to ensure the consistency of the regular parameter calculation method of each iteration. By analyzing the distribution of coefficients in the curvelet domain of pseudo-deblending data and blending noise, it is concluded that the value of regular parameters is the maximum amplitude of residual pseudo-deblending data in the curvelet domain multiplied by a coefficient between 0 and 1. In the process of iterative deblending, the regularized parameters are obtained adaptively from the data itself. 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Wei, Yajie |
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Wei, Yajie misc Deblending misc Sparse inversion misc Adaptive misc Curvelet transform Adaptive iterative deblending of simultaneous-source seismic data based on sparse inversion |
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Adaptive iterative deblending of simultaneous-source seismic data based on sparse inversion Deblending (dpeaa)DE-He213 Sparse inversion (dpeaa)DE-He213 Adaptive (dpeaa)DE-He213 Curvelet transform (dpeaa)DE-He213 |
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Adaptive iterative deblending of simultaneous-source seismic data based on sparse inversion |
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Adaptive iterative deblending of simultaneous-source seismic data based on sparse inversion |
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Wei, Yajie |
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Wei, Yajie Cao, Jingjie Huang, Xiaogang Chen, Xue Cai, Zhicheng |
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adaptive iterative deblending of simultaneous-source seismic data based on sparse inversion |
| title_auth |
Adaptive iterative deblending of simultaneous-source seismic data based on sparse inversion |
| abstract |
Abstract Deblending of simultaneous-source seismic data is becoming more popular in seismic exploration since it can greatly improve the efficiency of seismic acquisition and reduce acquisition cost. At present, the deblending methods of simultaneous-source seismic data are mainly divided into two types: filtering method and sparse inversion method. Compared with the filtering method, the sparse inversion method has higher precision, but the selection of its parameter value mainly depends on experience, which is not suitable for large-scale seismic data processing. In this paper, an adaptive iterative deblending method based on sparse inversion is proposed. By improving the original iterative solution method of regularization inversion model, the effective signal and blending noise are weakened simultaneously in the iterative process, so that the energy intensity of blending noise is consistent with that of the effective signal in each iterative, so as to ensure the consistency of the regular parameter calculation method of each iteration. By analyzing the distribution of coefficients in the curvelet domain of pseudo-deblending data and blending noise, it is concluded that the value of regular parameters is the maximum amplitude of residual pseudo-deblending data in the curvelet domain multiplied by a coefficient between 0 and 1. In the process of iterative deblending, the regularized parameters are obtained adaptively from the data itself. It not only ensures the accuracy of the calculation results, but also improves the calculation efficiency, which is suitable for large-scale seismic data processing. © Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2021 |
| abstractGer |
Abstract Deblending of simultaneous-source seismic data is becoming more popular in seismic exploration since it can greatly improve the efficiency of seismic acquisition and reduce acquisition cost. At present, the deblending methods of simultaneous-source seismic data are mainly divided into two types: filtering method and sparse inversion method. Compared with the filtering method, the sparse inversion method has higher precision, but the selection of its parameter value mainly depends on experience, which is not suitable for large-scale seismic data processing. In this paper, an adaptive iterative deblending method based on sparse inversion is proposed. By improving the original iterative solution method of regularization inversion model, the effective signal and blending noise are weakened simultaneously in the iterative process, so that the energy intensity of blending noise is consistent with that of the effective signal in each iterative, so as to ensure the consistency of the regular parameter calculation method of each iteration. By analyzing the distribution of coefficients in the curvelet domain of pseudo-deblending data and blending noise, it is concluded that the value of regular parameters is the maximum amplitude of residual pseudo-deblending data in the curvelet domain multiplied by a coefficient between 0 and 1. In the process of iterative deblending, the regularized parameters are obtained adaptively from the data itself. It not only ensures the accuracy of the calculation results, but also improves the calculation efficiency, which is suitable for large-scale seismic data processing. © Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2021 |
| abstract_unstemmed |
Abstract Deblending of simultaneous-source seismic data is becoming more popular in seismic exploration since it can greatly improve the efficiency of seismic acquisition and reduce acquisition cost. At present, the deblending methods of simultaneous-source seismic data are mainly divided into two types: filtering method and sparse inversion method. Compared with the filtering method, the sparse inversion method has higher precision, but the selection of its parameter value mainly depends on experience, which is not suitable for large-scale seismic data processing. In this paper, an adaptive iterative deblending method based on sparse inversion is proposed. By improving the original iterative solution method of regularization inversion model, the effective signal and blending noise are weakened simultaneously in the iterative process, so that the energy intensity of blending noise is consistent with that of the effective signal in each iterative, so as to ensure the consistency of the regular parameter calculation method of each iteration. By analyzing the distribution of coefficients in the curvelet domain of pseudo-deblending data and blending noise, it is concluded that the value of regular parameters is the maximum amplitude of residual pseudo-deblending data in the curvelet domain multiplied by a coefficient between 0 and 1. In the process of iterative deblending, the regularized parameters are obtained adaptively from the data itself. It not only ensures the accuracy of the calculation results, but also improves the calculation efficiency, which is suitable for large-scale seismic data processing. © Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2021 |
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Adaptive iterative deblending of simultaneous-source seismic data based on sparse inversion |
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https://dx.doi.org/10.1007/s11600-021-00561-1 |
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Cao, Jingjie Huang, Xiaogang Chen, Xue Cai, Zhicheng |
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Cao, Jingjie Huang, Xiaogang Chen, Xue Cai, Zhicheng |
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10.1007/s11600-021-00561-1 |
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2024-07-03T21:27:40.184Z |
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| score |
7.400832 |