The resolution of seismic data is critical to seismic data processing and the subsequent interpretation of fine structures. In conventional resolution improvement methods, the seismic data is assumed stationary and th...The resolution of seismic data is critical to seismic data processing and the subsequent interpretation of fine structures. In conventional resolution improvement methods, the seismic data is assumed stationary and the noise level not changes with space, whereas the actual situation does not satisfy this assumption, so that results after resolution improvement processing is not up to the expected effect. To solve these problems, we propose a seismic resolution improvement method based on the secondary time-frequency spectrum. First, we propose the secondary time-frequency spectrum based on S transform （ST） and discuss the reflection coefficient sequence and time-dependent wavelet in the secondary time frequency spectrum. Second, using the secondary time frequency spectrum, we design a two- dimensional filter to extract the amplitude spectrum of the time-dependent wavelet. Then, we discuss the improvement of the resolution operator in noisy environments and propose a novel approach for determining the broad frequency range of the resolution operator in the time- fi＇equency-space domain. Finally, we apply the proposed method to synthetic and real data and compare the results of the traditional spectrum-modeling deconvolution and Q compensation method. The results suggest that the proposed method does not need to estimate the Q value and the resolution is not limited by the bandwidth of the source. Thus, the resolution of the seismic data is improved sufficiently based on the signal-to-noise ratio （SNR）.展开更多
Conventional predictive deconvolution assumes that the reflection coefficients of the earth conform to an uncorrelated white noise sequence. The Wiener-Hopf (WH) equation is constructed to solve the filter and elimina...Conventional predictive deconvolution assumes that the reflection coefficients of the earth conform to an uncorrelated white noise sequence. The Wiener-Hopf (WH) equation is constructed to solve the filter and eliminate the correlated components of the seismic records, attenuate multiples, and improve seismic resolution. However, in practice, the primary refl ectivity series of fi eld data rarely satisfy the white noise sequence assumption, with the result that the correlated components of the primary reflectivity series are also eliminated by traditional deconvolution. This results in signal distortion. To solve this problem, we have proposed an improved method for deconvolution. First, we estimated the wavelet correlation from seismic records using the spectrum-modeling method. Second, this wavelet autocorrelation was used to construct a new autocorrelation function which contains the correlated components caused by the existence of multiples and avoids the correlated components of the primary reflectivity series. Finally, the new autocorrelation function was brought into the WH equation, and the predictive fi lter operator was calculated for deconvolution. In this paper, we have applied this new method to simulated and field data processing, and we have compared its performance with that of traditional predictive deconvolution. Our results show that the new method can adapt to non-white refl ectivity series without changing the statistical characteristics of the primary reflection coefficient series. Compared with traditional predictive deconvolution, the new method reduces processing noise and improves fidelity, all while maintaining the ability to attenuate multiples and enhance seismic resolution.展开更多
Aliased surface waves are caused by large-space sampling intervals in three- dimensional seismic exploration and most current surface-wave suppression methods fail to account for. Thus, we propose a surface-wave suppr...Aliased surface waves are caused by large-space sampling intervals in three- dimensional seismic exploration and most current surface-wave suppression methods fail to account for. Thus, we propose a surface-wave suppression method using phase-shift and phase-filtering, named the PSPF method, in which linear phase-shift is performed to solve the coupled problem of surface and reflected waves in the FKXKY domain and then used phase and FKXKY filtering to attenuate the surface-wave energy. Processing of model and field data suggest that the PSPF method can reduce the surface-wave energy while maintaining the low-frequency information of the reflected waves.展开更多
Fracture-cave reservoirs in carbonate rocks are characterized by a large difference in fracture and cavity size,and a sharp variation in lithology and velocity,thereby resulting in complex diffraction responses.Some s...Fracture-cave reservoirs in carbonate rocks are characterized by a large difference in fracture and cavity size,and a sharp variation in lithology and velocity,thereby resulting in complex diffraction responses.Some small-scale fractures and caves cause weak diffraction energy and would be obscured by the continuous reflection layer in the imaging section,thereby making them difficult to identify.This paper develops a diffraction wave imaging method in the dip domain,which can improve the resolution of small-scale diffractors in the imaging section.Common imaging gathers(CIGs)in the dip domain are extracted by Gaussian beam migration.In accordance with the geometric differences of the diffraction being quasilinear and the reflection being quasiparabolic in the dip-domain CIGs,we use slope analysis technique to filter waves and use Hanning window function to improve the diffraction wave separation level.The diffraction dip-domain CIGs are stacked horizontally to obtain diffraction imaging results.Wavefield separation analysis and numerical modeling results show that the slope analysis method,together with Hanning window filtering,can better suppress noise to obtain the diffraction dip-domain CIGs,thereby improving the clarity of the diffractors in the diffraction imaging section.展开更多
Most traditional ground roll separation methods utilize only the difference in geometric characteristics between the ground roll and the refl ection wave to separate them.When the geometric characteristics of data are...Most traditional ground roll separation methods utilize only the difference in geometric characteristics between the ground roll and the refl ection wave to separate them.When the geometric characteristics of data are complex,these methods often lead to damage of the reflection wave or incompletely suppress the ground roll.To solve this problem,we proposed a novel ground roll separation method via threshold filtering and constraint of seismic wavelet support in the curvelet domain;this method is called the TFWS method.First,curvelet threshold fi ltering(CTF)is performed by using the diff erence of the curvelet coeffi cient of the refl ection wave and the ground roll in the location,scale,and slope of their events to eliminate most of the ground roll.Second,the degree of the local damaged signal or the local residual noise is estimated as the local weighting coeffi cient.Under the constraints of seismic wavelet and local weighting coeffi cient,the L1 norm of the refl ection coeffi cient is minimized in the curvelet domain to recover the damaged refl ection wave and attenuate the residual noise.The local weighting coeffi cient in this paper is obtained by calculating the local correlation coeffi cient between the high-pass fi ltering result and the CFT result.We applied the TFWS method to simulate and fi eld data and compared its performance with that of frequency and wavenumber filtering and the CFT method.Results show that the TFWS method can attenuate not only linear ground roll,aliased ground roll,and nonlinear noise but also strong noise with a slope close to the refl ection events.展开更多
基金financially supported by the National 973 Project(No.2014CB239006)the National Natural Science Foundation of China(No.41104069 and 41274124)the Fundamental Research Funds for Central Universities(No.R1401005A)
文摘The resolution of seismic data is critical to seismic data processing and the subsequent interpretation of fine structures. In conventional resolution improvement methods, the seismic data is assumed stationary and the noise level not changes with space, whereas the actual situation does not satisfy this assumption, so that results after resolution improvement processing is not up to the expected effect. To solve these problems, we propose a seismic resolution improvement method based on the secondary time-frequency spectrum. First, we propose the secondary time-frequency spectrum based on S transform （ST） and discuss the reflection coefficient sequence and time-dependent wavelet in the secondary time frequency spectrum. Second, using the secondary time frequency spectrum, we design a two- dimensional filter to extract the amplitude spectrum of the time-dependent wavelet. Then, we discuss the improvement of the resolution operator in noisy environments and propose a novel approach for determining the broad frequency range of the resolution operator in the time- fi＇equency-space domain. Finally, we apply the proposed method to synthetic and real data and compare the results of the traditional spectrum-modeling deconvolution and Q compensation method. The results suggest that the proposed method does not need to estimate the Q value and the resolution is not limited by the bandwidth of the source. Thus, the resolution of the seismic data is improved sufficiently based on the signal-to-noise ratio （SNR）.
基金supported by Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents(No.2017RCJJ034)
文摘Conventional predictive deconvolution assumes that the reflection coefficients of the earth conform to an uncorrelated white noise sequence. The Wiener-Hopf (WH) equation is constructed to solve the filter and eliminate the correlated components of the seismic records, attenuate multiples, and improve seismic resolution. However, in practice, the primary refl ectivity series of fi eld data rarely satisfy the white noise sequence assumption, with the result that the correlated components of the primary reflectivity series are also eliminated by traditional deconvolution. This results in signal distortion. To solve this problem, we have proposed an improved method for deconvolution. First, we estimated the wavelet correlation from seismic records using the spectrum-modeling method. Second, this wavelet autocorrelation was used to construct a new autocorrelation function which contains the correlated components caused by the existence of multiples and avoids the correlated components of the primary reflectivity series. Finally, the new autocorrelation function was brought into the WH equation, and the predictive fi lter operator was calculated for deconvolution. In this paper, we have applied this new method to simulated and field data processing, and we have compared its performance with that of traditional predictive deconvolution. Our results show that the new method can adapt to non-white refl ectivity series without changing the statistical characteristics of the primary reflection coefficient series. Compared with traditional predictive deconvolution, the new method reduces processing noise and improves fidelity, all while maintaining the ability to attenuate multiples and enhance seismic resolution.
基金supported by the National Natural Science Foundation of China(No.41274124)the National Science and Technology Major Project(No.2016ZX05014-001-008HZ)
文摘Aliased surface waves are caused by large-space sampling intervals in three- dimensional seismic exploration and most current surface-wave suppression methods fail to account for. Thus, we propose a surface-wave suppression method using phase-shift and phase-filtering, named the PSPF method, in which linear phase-shift is performed to solve the coupled problem of surface and reflected waves in the FKXKY domain and then used phase and FKXKY filtering to attenuate the surface-wave energy. Processing of model and field data suggest that the PSPF method can reduce the surface-wave energy while maintaining the low-frequency information of the reflected waves.
基金funded jointly by the National Natural Science Foundation of China(No.41104069)Shandong Province Higher Educational Science and Technology Program(No.J17KA197)+1 种基金Open Foundation of Shandong Provincial Key Laboratory of Depositional Mineralization&Sedimentary Minerals of Shandong University of Science and Technology(No.DMSM2018018)Chunhui Research Foundation of Shengli College,China University of Petroleum(No.KY2017007)。
文摘Fracture-cave reservoirs in carbonate rocks are characterized by a large difference in fracture and cavity size,and a sharp variation in lithology and velocity,thereby resulting in complex diffraction responses.Some small-scale fractures and caves cause weak diffraction energy and would be obscured by the continuous reflection layer in the imaging section,thereby making them difficult to identify.This paper develops a diffraction wave imaging method in the dip domain,which can improve the resolution of small-scale diffractors in the imaging section.Common imaging gathers(CIGs)in the dip domain are extracted by Gaussian beam migration.In accordance with the geometric differences of the diffraction being quasilinear and the reflection being quasiparabolic in the dip-domain CIGs,we use slope analysis technique to filter waves and use Hanning window function to improve the diffraction wave separation level.The diffraction dip-domain CIGs are stacked horizontally to obtain diffraction imaging results.Wavefield separation analysis and numerical modeling results show that the slope analysis method,together with Hanning window filtering,can better suppress noise to obtain the diffraction dip-domain CIGs,thereby improving the clarity of the diffractors in the diffraction imaging section.
基金supported by Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents(No.2017RCJJ034)the National Natural Science Foundation of China(No.41676039)the National Science and Technology Major Project(2017ZX05049002-005)。
文摘Most traditional ground roll separation methods utilize only the difference in geometric characteristics between the ground roll and the refl ection wave to separate them.When the geometric characteristics of data are complex,these methods often lead to damage of the reflection wave or incompletely suppress the ground roll.To solve this problem,we proposed a novel ground roll separation method via threshold filtering and constraint of seismic wavelet support in the curvelet domain;this method is called the TFWS method.First,curvelet threshold fi ltering(CTF)is performed by using the diff erence of the curvelet coeffi cient of the refl ection wave and the ground roll in the location,scale,and slope of their events to eliminate most of the ground roll.Second,the degree of the local damaged signal or the local residual noise is estimated as the local weighting coeffi cient.Under the constraints of seismic wavelet and local weighting coeffi cient,the L1 norm of the refl ection coeffi cient is minimized in the curvelet domain to recover the damaged refl ection wave and attenuate the residual noise.The local weighting coeffi cient in this paper is obtained by calculating the local correlation coeffi cient between the high-pass fi ltering result and the CFT result.We applied the TFWS method to simulate and fi eld data and compared its performance with that of frequency and wavenumber filtering and the CFT method.Results show that the TFWS method can attenuate not only linear ground roll,aliased ground roll,and nonlinear noise but also strong noise with a slope close to the refl ection events.
