Reverse Time Migration(RTM) is a high precision imaging method of seismic wavefield at present,but low-frequency noises severely affect its imaging results.Thus one of most important aspect of RTM is to select the pro...Reverse Time Migration(RTM) is a high precision imaging method of seismic wavefield at present,but low-frequency noises severely affect its imaging results.Thus one of most important aspect of RTM is to select the proper noise suppression method.The wavefield characteristics of the Poynting vector are analyzed and the upgoing,downgoing,leftgoing and rightgoing waves are decomposed using the Poynting vector of the acoustic wave equation.The normalized wavefield decomposition cross-correlation imaging condition is used to suppress low-frequency noises in RTM and improve the imaging precision.Numerical experiments using the Mamousi velocity model are performed and the results demonstrate that the upgoing,downgoing,leftgoing and rightgoing waves are well decomposed using the Poynting vector.Compared with the normalized cross-correlation imaging and Laplacian filtering method,the results indicate that the low-frequency noises are well suppressed by using the normalized wavefield decomposition cross-correlation imaging condition.展开更多
Reverse-time migration has attracted more and more attention owing to the advantages of high imaging accuracy, no dip restriction, and adaptation to complex velocity models. Cross-correlation imaging method is typical...Reverse-time migration has attracted more and more attention owing to the advantages of high imaging accuracy, no dip restriction, and adaptation to complex velocity models. Cross-correlation imaging method is typically used in conventional reverse-time migration that produces images with strong low-frequency noise. Wavefield decomposition imaging can suppress such noise; however, some residual noise persists in the imaging results. We propose a 2D multidirectional wavefield decomposition method based on the traditional wavefield decomposition method. First, source wavefields and receiver wavefields are separated into eight subwavefields, respectively. Second, cross-correlation imaging is applied to selected subwavefields to produce subimages. Finally, the subimages are stacked to generate the final image. Numerical examples suggest that the proposed method can eliminate the low-frequency noise effectively and produce high-quality imaging profiles.展开更多
P-and S-wave separation plays an important role in elastic reverse-time migration.It can reduce the artifacts caused by crosstalk between different modes and improve image quality.In addition,P-and Swave separation ca...P-and S-wave separation plays an important role in elastic reverse-time migration.It can reduce the artifacts caused by crosstalk between different modes and improve image quality.In addition,P-and Swave separation can also be used to better understand and distinguish wave types in complex media.At present,the methods for separating wave modes in anisotropic media mainly include spatial nonstationary filtering,low-rank approximation,and vector Poisson equation.Most of these methods require multiple Fourier transforms or the calculation of large matrices,which require high computational costs for problems with large scale.In this paper,an efficient method is proposed to separate the wave mode for anisotropic media by using a scalar anisotropic Poisson operator in the spatial domain.For 2D problems,the computational complexity required by this method is 1/2 of the methods based on solving a vector Poisson equation.Therefore,compared with existing methods based on pseudoHelmholtz decomposition operators,this method can significantly reduce the computational cost.Numerical examples also show that the P and S waves decomposed by this method not only have the correct amplitude and phase relative to the input wavefield but also can reduce the computational complexity significantly.展开更多
With the development of seismic exploration,passive-source seismic data has attracted increasing attention.Ambient noise passive seismic sources exists widely in nature and industrial production.Passive seismic data i...With the development of seismic exploration,passive-source seismic data has attracted increasing attention.Ambient noise passive seismic sources exists widely in nature and industrial production.Passive seismic data is important in logging while drilling(LWD),large-scale structural exploration,etc.In this paper,we proposed a passive multiple reverse time migration imaging(PMRTMI)method based on wavefield decomposition and normalized imaging conditions method.This method differs from seismic interferometry in that it can use raw passive seismic data directly in RTM imaging without reconstruction of virtual active gather,and we use the wavefield decomposition method to eliminate the low frequency noise in RTM.Further,the energy normalized imaging condition is used in full wavefield decomposition,which can not only enhance the image quality of both edge and deep information but also overcome the wrong energy problem caused by uneven distribution of passive sources;furthermore,this method exhibits high efficiency.Finally,numerical examples with the Marmousi model show the effectiveness of the method.展开更多
文摘Reverse Time Migration(RTM) is a high precision imaging method of seismic wavefield at present,but low-frequency noises severely affect its imaging results.Thus one of most important aspect of RTM is to select the proper noise suppression method.The wavefield characteristics of the Poynting vector are analyzed and the upgoing,downgoing,leftgoing and rightgoing waves are decomposed using the Poynting vector of the acoustic wave equation.The normalized wavefield decomposition cross-correlation imaging condition is used to suppress low-frequency noises in RTM and improve the imaging precision.Numerical experiments using the Mamousi velocity model are performed and the results demonstrate that the upgoing,downgoing,leftgoing and rightgoing waves are well decomposed using the Poynting vector.Compared with the normalized cross-correlation imaging and Laplacian filtering method,the results indicate that the low-frequency noises are well suppressed by using the normalized wavefield decomposition cross-correlation imaging condition.
基金This work was supported by National Natural Science Foundation of China （No. 41474110） and the Scientific Research Starting Foundation of China University of Petroleum-Beijing at Karamay （No. RCYJ2018A-01-001）.
文摘Reverse-time migration has attracted more and more attention owing to the advantages of high imaging accuracy, no dip restriction, and adaptation to complex velocity models. Cross-correlation imaging method is typically used in conventional reverse-time migration that produces images with strong low-frequency noise. Wavefield decomposition imaging can suppress such noise; however, some residual noise persists in the imaging results. We propose a 2D multidirectional wavefield decomposition method based on the traditional wavefield decomposition method. First, source wavefields and receiver wavefields are separated into eight subwavefields, respectively. Second, cross-correlation imaging is applied to selected subwavefields to produce subimages. Finally, the subimages are stacked to generate the final image. Numerical examples suggest that the proposed method can eliminate the low-frequency noise effectively and produce high-quality imaging profiles.
基金supported by the National Key R&D Program of China(No.2018YFA0702505)the project of CNOOC Limited(Grant No.CNOOC-KJ GJHXJSGG YF 2022-01)+1 种基金R&D Department of China National Petroleum Corporation(Investigations on fundamental experiments and advanced theoretical methods in geophysical prospecting application,2022DQ0604-02)NSFC(Grant Nos.U23B20159,41974142,42074129,12001311)。
文摘P-and S-wave separation plays an important role in elastic reverse-time migration.It can reduce the artifacts caused by crosstalk between different modes and improve image quality.In addition,P-and Swave separation can also be used to better understand and distinguish wave types in complex media.At present,the methods for separating wave modes in anisotropic media mainly include spatial nonstationary filtering,low-rank approximation,and vector Poisson equation.Most of these methods require multiple Fourier transforms or the calculation of large matrices,which require high computational costs for problems with large scale.In this paper,an efficient method is proposed to separate the wave mode for anisotropic media by using a scalar anisotropic Poisson operator in the spatial domain.For 2D problems,the computational complexity required by this method is 1/2 of the methods based on solving a vector Poisson equation.Therefore,compared with existing methods based on pseudoHelmholtz decomposition operators,this method can significantly reduce the computational cost.Numerical examples also show that the P and S waves decomposed by this method not only have the correct amplitude and phase relative to the input wavefield but also can reduce the computational complexity significantly.
基金sponsored by the Natural Science Foundation of China(No.41874139)the Natural Science Foundation of China(No.41674124)Jilin Province Foundation for Excellent Youths(No.20190103139JH)
文摘With the development of seismic exploration,passive-source seismic data has attracted increasing attention.Ambient noise passive seismic sources exists widely in nature and industrial production.Passive seismic data is important in logging while drilling(LWD),large-scale structural exploration,etc.In this paper,we proposed a passive multiple reverse time migration imaging(PMRTMI)method based on wavefield decomposition and normalized imaging conditions method.This method differs from seismic interferometry in that it can use raw passive seismic data directly in RTM imaging without reconstruction of virtual active gather,and we use the wavefield decomposition method to eliminate the low frequency noise in RTM.Further,the energy normalized imaging condition is used in full wavefield decomposition,which can not only enhance the image quality of both edge and deep information but also overcome the wrong energy problem caused by uneven distribution of passive sources;furthermore,this method exhibits high efficiency.Finally,numerical examples with the Marmousi model show the effectiveness of the method.
