Seismic data interpolation,especially irregularly sampled data interpolation,is a critical task for seismic processing and subsequent interpretation.Recently,with the development of machine learning and deep learning,...Seismic data interpolation,especially irregularly sampled data interpolation,is a critical task for seismic processing and subsequent interpretation.Recently,with the development of machine learning and deep learning,convolutional neural networks(CNNs)are applied for interpolating irregularly sampled seismic data.CNN based approaches can address the apparent defects of traditional interpolation methods,such as the low computational efficiency and the difficulty on parameters selection.However,current CNN based methods only consider the temporal and spatial features of irregularly sampled seismic data,which fail to consider the frequency features of seismic data,i.e.,the multi-scale features.To overcome these drawbacks,we propose a wavelet-based convolutional block attention deep learning(W-CBADL)network for irregularly sampled seismic data reconstruction.We firstly introduce the discrete wavelet transform(DWT)and the inverse wavelet transform(IWT)to the commonly used U-Net by considering the multi-scale features of irregularly sampled seismic data.Moreover,we propose to adopt the convolutional block attention module(CBAM)to precisely restore sampled seismic traces,which could apply the attention to both channel and spatial dimensions.Finally,we adopt the proposed W-CBADL model to synthetic and pre-stack field data to evaluate its validity and effectiveness.The results demonstrate that the proposed W-CBADL model could reconstruct irregularly sampled seismic data more effectively and more efficiently than the state-of-the-art contrastive CNN based models.展开更多
Manually picking regularly and densely distributed first breaks(FBs)are critical for shallow velocitymodel building in seismic data processing.However,it is time consuming.We employ the fullyconvolutional Seg Net to a...Manually picking regularly and densely distributed first breaks(FBs)are critical for shallow velocitymodel building in seismic data processing.However,it is time consuming.We employ the fullyconvolutional Seg Net to address this issue and present a fast automatic seismic waveform classification method to pick densely-sampled FBs directly from common-shot gathers with sparsely distributed traces.Through feeding a large number of representative shot gathers with missing traces and the corresponding binary labels segmented by manually interpreted fully-sampled FBs,we can obtain a welltrained Seg Net model.When any unseen gather including the one with irregular trace spacing is inputted,the Seg Net can output the probability distribution of different categories for waveform classification.Then FBs can be picked by locating the boundaries between one class on post-FBs data and the other on pre-FBs background.Two land datasets with each over 2000 shots are adopted to illustrate that one well-trained 25-layer Seg Net can favorably classify waveform and further pick fully-sampled FBs verified by the manually-derived ones,even when the proportion of randomly missing traces reaches50%,21 traces are missing consecutively,or traces are missing regularly.展开更多
Least squares migration can eliminate the artifacts introduced by the direct imaging of irregular seismic data but is computationally costly and of slow convergence. In order to suppress the migration noise, we propos...Least squares migration can eliminate the artifacts introduced by the direct imaging of irregular seismic data but is computationally costly and of slow convergence. In order to suppress the migration noise, we propose the preconditioned prestack plane-wave least squares reverse time migration （PLSRTM） method with singular spectrum constraint. Singular spectrum analysis （SSA） is used in the preconditioning of the take-off angle-domain common-image gathers （TADCIGs）. In addition, we adopt randomized singular value decomposition （RSVD） to calculate the singular values. RSVD reduces the computational cost of SSA by replacing the singular value decomposition （SVD） of one large matrix with the SVD of two small matrices. We incorporate a regularization term into the preconditioned PLSRTM method that penalizes misfits between the migration images from the plane waves with adjacent angles to reduce the migration noise because the stacking of the migration results cannot effectively suppress the migration noise when the migration velocity contains errors. The regularization imposes smoothness constraints on the TADCIGs that favor differential semblance optimization constraints. Numerical analysis of synthetic data using the Marmousi model suggests that the proposed method can efficiently suppress the artifacts introduced by plane-wave gathers or irregular seismic data and improve the imaging quality of PLSRTM. Furthermore, it produces better images with less noise and more continuous structures even for inaccurate migration velocities.展开更多
基金Supported by the National Natural Science Foundation of China under Grant 42274144 and under Grant 41974137.
文摘Seismic data interpolation,especially irregularly sampled data interpolation,is a critical task for seismic processing and subsequent interpretation.Recently,with the development of machine learning and deep learning,convolutional neural networks(CNNs)are applied for interpolating irregularly sampled seismic data.CNN based approaches can address the apparent defects of traditional interpolation methods,such as the low computational efficiency and the difficulty on parameters selection.However,current CNN based methods only consider the temporal and spatial features of irregularly sampled seismic data,which fail to consider the frequency features of seismic data,i.e.,the multi-scale features.To overcome these drawbacks,we propose a wavelet-based convolutional block attention deep learning(W-CBADL)network for irregularly sampled seismic data reconstruction.We firstly introduce the discrete wavelet transform(DWT)and the inverse wavelet transform(IWT)to the commonly used U-Net by considering the multi-scale features of irregularly sampled seismic data.Moreover,we propose to adopt the convolutional block attention module(CBAM)to precisely restore sampled seismic traces,which could apply the attention to both channel and spatial dimensions.Finally,we adopt the proposed W-CBADL model to synthetic and pre-stack field data to evaluate its validity and effectiveness.The results demonstrate that the proposed W-CBADL model could reconstruct irregularly sampled seismic data more effectively and more efficiently than the state-of-the-art contrastive CNN based models.
基金financially supported by the National Key R&D Program of China(2018YFA0702504)the Fundamental Research Funds for the Central Universities(2462019QNXZ03)+1 种基金the National Natural Science Foundation of China(42174152 and 41974140)the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX 2020-03)。
文摘Manually picking regularly and densely distributed first breaks(FBs)are critical for shallow velocitymodel building in seismic data processing.However,it is time consuming.We employ the fullyconvolutional Seg Net to address this issue and present a fast automatic seismic waveform classification method to pick densely-sampled FBs directly from common-shot gathers with sparsely distributed traces.Through feeding a large number of representative shot gathers with missing traces and the corresponding binary labels segmented by manually interpreted fully-sampled FBs,we can obtain a welltrained Seg Net model.When any unseen gather including the one with irregular trace spacing is inputted,the Seg Net can output the probability distribution of different categories for waveform classification.Then FBs can be picked by locating the boundaries between one class on post-FBs data and the other on pre-FBs background.Two land datasets with each over 2000 shots are adopted to illustrate that one well-trained 25-layer Seg Net can favorably classify waveform and further pick fully-sampled FBs verified by the manually-derived ones,even when the proportion of randomly missing traces reaches50%,21 traces are missing consecutively,or traces are missing regularly.
基金supported by the National Science and Technology Major Project(No.2016ZX05014-001-008)the National Key Basic Research Program of China(No.2014CB239006)+2 种基金the National Natural Science Foundation of China(Nos.41104069 and 41274124)the Open foundation of SINOPEC Key Laboratory of Geophysics(No.33550006-15-FW2099-0033)the Fundamental Research Funds for Central Universities(No.16CX06046A)
文摘Least squares migration can eliminate the artifacts introduced by the direct imaging of irregular seismic data but is computationally costly and of slow convergence. In order to suppress the migration noise, we propose the preconditioned prestack plane-wave least squares reverse time migration （PLSRTM） method with singular spectrum constraint. Singular spectrum analysis （SSA） is used in the preconditioning of the take-off angle-domain common-image gathers （TADCIGs）. In addition, we adopt randomized singular value decomposition （RSVD） to calculate the singular values. RSVD reduces the computational cost of SSA by replacing the singular value decomposition （SVD） of one large matrix with the SVD of two small matrices. We incorporate a regularization term into the preconditioned PLSRTM method that penalizes misfits between the migration images from the plane waves with adjacent angles to reduce the migration noise because the stacking of the migration results cannot effectively suppress the migration noise when the migration velocity contains errors. The regularization imposes smoothness constraints on the TADCIGs that favor differential semblance optimization constraints. Numerical analysis of synthetic data using the Marmousi model suggests that the proposed method can efficiently suppress the artifacts introduced by plane-wave gathers or irregular seismic data and improve the imaging quality of PLSRTM. Furthermore, it produces better images with less noise and more continuous structures even for inaccurate migration velocities.
