The elastic reverse time migration (ERTM) gradually becomes a powerful tool for imaging complex structures, especially for using converted waves. In the common ERTM workflow, the relatively linear scattering/reflection process in the subsurface local domain can be reconstructed through accurate injection of multicomponent records and extrapolation of vector wavefields. Subsequently, imaging conditions are applied to construct meaningful depth images. One of the classical challenges in PS imaging is polarity reversal at normal incidence, which significantly impacts the stacking of images obtained from multi-shot experiments. To address this issue, various imaging conditions have been developed. Specifically, operations that combine the source-side P wave and receiver-side S wave can introduce implicit angle-dependent weighting factors, which help improve the quality of PS depth images. In this study, we conduct a comparative analysis of four specific PS imaging conditions of ERTM: the angle-domain imaging condition, the divergence and curl based imaging condition, the vector imaging condition, and the impedance gradient term from full waveform inversion. We link the imaging conditions with the PS Born scattering process to investigate the implicitly embedded angle-dependent weighting factors. By using the accurate local plane wave decomposition approach for angle-domain wavefield decomposition, various numerical experiments are carefully designed to characterize the PS imaging conditions. Their abilities for solving the polarity reversal at normal incidence are evaluated. The influences of the angle-dependent weighting factors on imaging resolutions are also quantified. Under the constructed angle-domain investigation frame, we can not only analyze existing imaging conditions but also explore potential new imaging conditions toward enhanced elastic imaging.

Among deep learning-based seismic fault detection approaches, most of the training datasets are generated by using synthetic methods to enhance the diversity of training data. However, due to the feature differences between synthetic seismic data and real data, and the difficulty of synthesizing all types of actual geological structures, networks trained using synthetic data exhibit poor generalization performance in practical data applications. In this study, we propose a barely-supervised-learning-based seismic fault detection scheme. The training data are extracted from the real seismic data and the fault labels are manually annotated by the interpreter. In this scheme, only 10% of the training data need to be labeled. Furthermore, to avoid dense annotation of faults on 3-D seismic data, we adopt an orthogonal annotation strategy, where only one inline section and one horizontal slice are annotated for one$128 {\times}128 \times 128$training sample. As a result, the pixels that have been actually annotated constitute merely 0.1563% of the total voxels in the training sample. Then, the orthogonal annotation is extended to the dense annotation of training data by using a fault registration method. We confirmed the feasibility and effectiveness of this approach using synthetic data. Accurate fault detection can be achieved by annotating a few orthogonal sections and slices from actual samples. Further application on two real seismic data demonstrated that the fault detection by this method is more continuous and accurate compared to the results predicted by other methods.

Seismic exploration risk can be decreased by high-precision migration techniques. Imaging anisotropic multicomponent seismic data in areas with developed cracks and sedimentation is challenging. We introduce an efficient anisotropic wave separation elastic reverse time migration (RTM) to image anisotropic multicomponent seismic data in this letter. The elastic waves are decomposed into P- and S-waves for subsequent anisotropic wave separation elastic RTM (AWSERTM) to reduce crosstalk noise and improve imaging accuracy. In this new method, the pseudo-decoupled wave equations of transverse isotropic (TI) media with a vertical symmetry axis vertical transversely isotropic (VTI) are derived based on the decomposition of the anisotropic elastic stiffness parameters into anisotropic P- and S-wave stiffness parameters. Forward and backward anisotropic P- and S-waves can then be efficiently obtained by numerical solution of the pseudo-decoupled wave equations using the finite difference (FD) method. Combining the vector imaging condition, the high-quality AWSERTM’s results can be obtained. Synthetic examples from the modified HESS VTI model demonstrate the correctness and progressiveness of the proposed method.

In the marine seismic acquisition, hydrophones are distributed along streamers that are towed under the sea surface, recording both the upgoing primary reflections and the downgoing free-surface reflections. The interferences between them result in notches in the frequency-domain amplitude spectra, causing missing frequencies and destroying the broadband signature. These free-surface reflections, referred to as ghost waves, are usually treated as noises and are supposed to be suppressed or removed before depth migration imaging, namely, the deghosting processing. If the ghost waves are not effectively removed, they will be projected to the subsurface image domain, further causing missing wavenumbers and influencing resolutions. The missing information can hardly be fully compensated through traditional deconvolution-type filters in the data or image domain. Recently, deep-learning-based methods have been introduced to data-domain deghosting. However, generating a sufficiently large number of training samples with and without ghost waves is quite costly, and it is difficult to guarantee the diversity, leading to the limited generalization capability of neural networks trained with these datasets. Therefore, we are motivated to use the point spread function (PSF) to project the near-surface ghost-related parameters to the subsurface local image domain. In this way, large numbers of depth images with and without ghost waves can be constructed efficiently through convolutions between PSFs and local model perturbations. The diversity of training samples can be easily satisfied. Using these paired samples, we train U-nets for deghosting processing in the image domain. Various examples validate the new deep-learning-based method.

Simultaneous source acquisition can reduce survey time and expense. Deblending is a typical method for processing simultaneous source seismic data, which is achieved by removing as much blending noise as feasible while maintaining as much useful signal as possible. We propose a novel deblending method with the shaping regularization-based iterative framework in the sparse 3-D transform domain in this letter. The core concept behind this method is to process the 2-D blended data block by block. Using the block-matching technique, we can extract similar blocks from the pseudo-deblended data and then put these 2-D blocks together to make 3-D arrays. The arrays are then transformed to the sparse 3-D transform domain to eliminate blending noise. Due to the correlation between similar blocks, the signal can be represented with greater sparsity in the 3-D transform domain than in the conventional 2-D transform domain. It is this sparsity that threshold shrinkage can be particularly successful at attenuating blending noise while retaining the characteristics of useful signals. To get the final deblended seismic data, the overlapping deblended blocks are restored to their original places and combined using a specific averaging procedure. To verify the practicability of our method, we test it with noisy blended data. The experimental results indicate that our approach can effectively remove blending and white Gaussian noise.

Seismic deconvolution aims to improve the vertical seismic resolution by compressing the seismic wavelet and extending the seismic frequency bandwidth. Deconvolution in the frequency domain is normally implemented in three steps: wavelet estimation, deconvolution operator construction, and data filtering. We propose a seismic resolution enhancement approach by spectral shaping using shaping-regularized inversion. Instead of designing a deconvolution operator based on the extracted wavelet, we formulate an inverse problem using the seismic spectrum as the diagonal kernel matrix and a predefined filter as the expected spectrum. Assuming the randomness in the reflectivity and smoothness of the spectral shaping operator, we estimate the spectral shaping operator by inversion via a shaping regularization scheme, which imposes constraints by shaping the estimated spectral shaping operator model to the admissible model space. The role of the shaping regularization in inversion is to ensure the continuity and smoothness of the spectral shaping operator. We use a synthetic model and real land seismic data to demonstrate the effectiveness of the proposed approach in seismic resolution enhancement.

The simultaneous-source technique improves the acquisition efficiency in marine seismic exploration. A high-quality deblending procedure is an important step when implementing the simultaneous-source technique. Deblending is often implemented in a domain (other than the common-source domain) where the blending noise is incoherent. In this article, we present a denoising method based on a deep convolutional neural network (CNN) for deblending. The denoising generalization ability of CNN-based denoising on real seismic data is limited because the training dataset, especially the labeled data, is often difficult to acquire. To make the CNN applicable to real data, we generate the training dataset directly from the real common-shot blended record itself, which has the same dynamic seismic wavefield characteristics as the real data. The input dataset for the CNN is acquired by adding A to B. A is the random time-delay encoding data of each trace of the common-shot blended data, while B is the data of each trace of the common-shot blended data; then, the blended data naturally become labeled data. The CNN model obtained through deep learning is used to remove the blending noise to complete the separation of blended data. Numerical tests using synthetic data and real data show that our method can provide high-precision separation results.

Conventional multichannel spectral inversion (SI) methods are usually implemented in a 2-D model, which only considers the lateral continuity inside a section and ignores the continuous features between sections in 3-D space. On the other hand, the principle of odd–even decomposition, which can significantly improve the thin-layer recognition ability, is poorly adapted in multichannel with complex structures. We propose a structural geosteering shear backfill mapping (SBM) method to alleviate these issues. After that, we establish a 3-D bipolar SI objective function based on structural geosteering SBM and solve it with 3-D norm regularization. Examples using synthetic and 3-D field data show that the 3-D bipolar SI based on structural geosteering SBM is highly adaptable to complex structures and can provide a better inversion result than the conventional multichannel sparse spike inversion in terms of guaranteeing strata continuities and retrieving weak reflectivity.

Simultaneous towed-streamer marine acquisition has the advantages of reducing the total time requirements and costs of surveys. However, the seismic records obtained are blended seismic data, so the successful deblending of such data is the key to this method. In this paper, we propose an effective deblending method with a new thresholding operator based on the shaping regularization framework in the contourlet domain. The new thresholding operator consists of an adaptive Bayesian threshold and a new thresholding function. Because of its multiresolution, locality, and directionality properties, the contourlet transform can effectively capture geometrical structures, which are the main features in natural images. To make the traditional Bayesian threshold adaptive in the contourlet domain, we propose a scale adjustment factor, a direction adjustment factor, and an attenuation factor to modify the threshold, and we also adopt local adaptive elliptic windows to estimate the standard deviations of useful signals; eventually, we obtain an adaptive Bayesian threshold. Furthermore, the new thresholding function can overcome the shortcomings of the existing soft and hard thresholding functions. Experimental results demonstrate that our method can effectively separate blended data.

The low-rankness property of the Hankel matrix formulated from the clean seismic data corresponding to a few number of linear events has been successively leveraged in many low-rank (LR) approximation methods for seismic data denoising. The common scheme in these rank-reduction methods is to compute the best LR approximation of the formulated Hankel matrix and then obtain the denoised data from the LR matrix. However, without utilizing the Hankel structure when computing the LR approximation, if we rearrange the denoised data into a Hankel matrix, it is in general not exactly LR as expected. In this paper, we propose a Hankel LR (HLR) approximation method to simultaneously exploit both the Hankel structure and the LR property underlying the clean seismic data. The formulated HLR approximation problem is solved by an alternating-minimization-based algorithm. We provide rigorously convergence analysis of the proposed algorithm. The superior performance of the proposed HLR approximation method is demonstrated on both synthetic and field seismic data.