The gradient preconditioning approach based on seismic wave energy can effectively avoid the huge memory consumption of the gradient preconditioning algorithms based on the Hessian matrix. However, the accuracy of thi...The gradient preconditioning approach based on seismic wave energy can effectively avoid the huge memory consumption of the gradient preconditioning algorithms based on the Hessian matrix. However, the accuracy of this approach is prone to be influ- enced by the energy of reflected waves. To tackle this problem, the paper proposes a new gradient preconditioning method based on the energy of transmitted waves. The approach scales the gradient through a precondition factor, which is calculated by the ‘ap- proximate transmission wavefield’ simulation based on the nonreflecting acoustic wave equation. The method requires no computing nor storing of the Hessian matrix and its inverse matrix. Furthermore, the proposed method can effectively eliminate the effects of geometric spreading and disproportionality in the gradient illumination. The results of model experiments show that the time-domain full waveform inversion (FWI) using the gradient preconditioning based on transmitted wave energy can achieve higher inversion accuracy for deep high-velocity bodies and their underlying strata in comparison with the one using the gradient preconditioning based on seismic wave energy. The field marine seismic data test shows that our proposed method is also highly applicable to the FWI of field marine seismic data.展开更多
This paper introduces an internal multiple prediction method based on imaging profile prediction and Kirchhoff demigration.First,based on an inputted prestack time migration profile,the method predicts the prestack ti...This paper introduces an internal multiple prediction method based on imaging profile prediction and Kirchhoff demigration.First,based on an inputted prestack time migration profile,the method predicts the prestack time migration profile that only includes internal multiples by inverse scattering series method.Second,the method uses velocity-weighted Kirchhoff demigration to create shot gathers that contains only internal multiples.Internal multiple prediction based on the prestack time migration profile effectively reduces the computational cost of multiple predictions,and the internal-multiple shot gathers created by Kirchhoff demigration remarkably reduces the complexity of the practical problem.Internal multiple elimination can be conducted through the combined adaptive multiple subtraction based on event tracing.Synthetic and field data tests show that the method effectively predicts internal multiples and possesses considerable potential in field data processing,particularly in areas where internal multiples develop seriously.展开更多
Low-frequency band-shaped swell noise with strong amplitude is common in marine seismic data.The conventional high-pass fi ltering algorithm widely used to suppress swell noise often results in serious damage of effec...Low-frequency band-shaped swell noise with strong amplitude is common in marine seismic data.The conventional high-pass fi ltering algorithm widely used to suppress swell noise often results in serious damage of effective information.This paper introduces the residual learning strategy of denoising convolutional neural network(DnCNN)into a U-shaped convolutional neural network(U-Net)to develop a new U-Net with more generalization,which can eliminate low-frequency swell noise with high precision.The results of both model date tests and real data processing show that the new U-Net is capable of effi cient learning and high-precision noise removal,and can avoid the overfi tting problem which is very common in conventional neural network methods.This new U-Net can also be generalized to some extent and can eff ectively preserve low-frequency eff ective information.Compared with the conventional high-pass fi ltering method commonly used in the industry,the new U-Net can eliminate low-frequency swell noise with higher precision while eff ectively preserving low-frequency eff ective information,which is of great signifi cance for subsequent processing such as amplitude-preserving imaging and full waveform inversion.展开更多
There are lots of low wavenumber noises in the gradients of time domain full waveform inversion(FWI),which can seriously reduce the accuracy and convergence speed of FWI.Thus,we introduce an angle-dependent weighting ...There are lots of low wavenumber noises in the gradients of time domain full waveform inversion(FWI),which can seriously reduce the accuracy and convergence speed of FWI.Thus,we introduce an angle-dependent weighting factor to precondition the gradients so as to suppress the low wavenumber noises when the multi-scale FWI is implemented in the high frequency.Model experiments show that the FWI based on the gradient preconditioning with an angle-dependent weighting factor has faster convergence speed and higher inversion accuracy than the conventional FWI.The tests on real marine seismic data show that this method can adapt to the FWI of field data,and provide high-precision velocity models for the actual data processing.展开更多
基金support of the NSFCShandong Joint Fund for Marine Science Research Centers (No. U1606401)the National Natural Science Foundation of China (Nos. 41574105 and 41704114)+1 种基金the National Science and Technology Major Project of China (No.2016ZX05027-002)Taishan Scholar Project Funding (No. tspd20161007)
文摘The gradient preconditioning approach based on seismic wave energy can effectively avoid the huge memory consumption of the gradient preconditioning algorithms based on the Hessian matrix. However, the accuracy of this approach is prone to be influ- enced by the energy of reflected waves. To tackle this problem, the paper proposes a new gradient preconditioning method based on the energy of transmitted waves. The approach scales the gradient through a precondition factor, which is calculated by the ‘ap- proximate transmission wavefield’ simulation based on the nonreflecting acoustic wave equation. The method requires no computing nor storing of the Hessian matrix and its inverse matrix. Furthermore, the proposed method can effectively eliminate the effects of geometric spreading and disproportionality in the gradient illumination. The results of model experiments show that the time-domain full waveform inversion (FWI) using the gradient preconditioning based on transmitted wave energy can achieve higher inversion accuracy for deep high-velocity bodies and their underlying strata in comparison with the one using the gradient preconditioning based on seismic wave energy. The field marine seismic data test shows that our proposed method is also highly applicable to the FWI of field marine seismic data.
基金support of the NSFC-Shandong Joint Fund for Marine Science Research Centers (No. U1606401)the National Natural Science Foundation of China (Nos. 41704114 and 41574105)+3 种基金the National Science and Technology Major Project of China (No. 2016Z X05027-002)the Scientific and Technological Innovation Project financially supported by Qingdao National Laboratory for Marine Science and Technology (No. 2016 ASKJ13)Taishan Scholar Project Funding (No. tspd2016 1007)the Latitudinal Project of Algorithm Research of Internal Multiple Prediction financially supported by CNOOC
文摘This paper introduces an internal multiple prediction method based on imaging profile prediction and Kirchhoff demigration.First,based on an inputted prestack time migration profile,the method predicts the prestack time migration profile that only includes internal multiples by inverse scattering series method.Second,the method uses velocity-weighted Kirchhoff demigration to create shot gathers that contains only internal multiples.Internal multiple prediction based on the prestack time migration profile effectively reduces the computational cost of multiple predictions,and the internal-multiple shot gathers created by Kirchhoff demigration remarkably reduces the complexity of the practical problem.Internal multiple elimination can be conducted through the combined adaptive multiple subtraction based on event tracing.Synthetic and field data tests show that the method effectively predicts internal multiples and possesses considerable potential in field data processing,particularly in areas where internal multiples develop seriously.
基金the Key R&D project of Shandong Province(No.2019JZZY010803)the Central Universities(No.201964016),the National Natural Science Foundation of China(No.41704114)+2 种基金the National Science and Technology Major Project of China(No.2016ZX05027-002)Taishan Scholar Project Funding(No.tspd20161007)the China Scholarship Council(No.201906335010).
文摘Low-frequency band-shaped swell noise with strong amplitude is common in marine seismic data.The conventional high-pass fi ltering algorithm widely used to suppress swell noise often results in serious damage of effective information.This paper introduces the residual learning strategy of denoising convolutional neural network(DnCNN)into a U-shaped convolutional neural network(U-Net)to develop a new U-Net with more generalization,which can eliminate low-frequency swell noise with high precision.The results of both model date tests and real data processing show that the new U-Net is capable of effi cient learning and high-precision noise removal,and can avoid the overfi tting problem which is very common in conventional neural network methods.This new U-Net can also be generalized to some extent and can eff ectively preserve low-frequency eff ective information.Compared with the conventional high-pass fi ltering method commonly used in the industry,the new U-Net can eliminate low-frequency swell noise with higher precision while eff ectively preserving low-frequency eff ective information,which is of great signifi cance for subsequent processing such as amplitude-preserving imaging and full waveform inversion.
基金funded by the National Natural Science Foundation of China(No.42074138)the Wenhai Program of the S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(No.2021WHZZB0700)the Major Scientific and Technological Innovation Project of Shandong Province(No.2019JZZY010803).
文摘There are lots of low wavenumber noises in the gradients of time domain full waveform inversion(FWI),which can seriously reduce the accuracy and convergence speed of FWI.Thus,we introduce an angle-dependent weighting factor to precondition the gradients so as to suppress the low wavenumber noises when the multi-scale FWI is implemented in the high frequency.Model experiments show that the FWI based on the gradient preconditioning with an angle-dependent weighting factor has faster convergence speed and higher inversion accuracy than the conventional FWI.The tests on real marine seismic data show that this method can adapt to the FWI of field data,and provide high-precision velocity models for the actual data processing.
