Accurate imaging of converted S-waves is one of the key technical challenges in the processing of multicomponent ocean-bottom seismic data. The widespread presence of anisotropy in the seafloor environment, characterized by fluid-solid coupled media, leads to strong coupling between P- and S-waves. This coupling introduces significant crosstalk noise, which severely degrades the resolution of seismic imaging. To address this issue, we propose a vector wavefield decoupling method tailored for fluid-solid coupled media, aiming to achieve more accurate elastic vector wave imaging for ocean-bottom node data. Specifically, we simplify the existing acoustic-elastic coupled equations for vertically transverse isotropic media. Building upon the decoupling theory developed for purely elastic media — which is based on the normalized zero-order pseudo-Helmholtz operator — we derive explicit relationships between the first-order time derivative of the pseudo-stress components of quasi-P and quasi-S waves in the decoupled system and the first-order time derivative of the synthetic pressure and deviatoric stress components in the acoustoelastic coupling system. From these relationships, we obtain first-order time derivatives of the particle vibration velocity fields for quasi-P and quasi-S waves and express them explicitly in terms of the synthetic pressure and deviatoric stress, thus constructing first-order velocity-stress equations for vector quasi-P and quasi-S waves. Wavefield decoupling tests on both homogeneous and heterogeneous media models demonstrate that the proposed method effectively separates vector quasi-P and quasi-S waves. We further apply the decoupling scheme to elastic reverse time migration. Numerical experiments on simple and complex models show that our method significantly suppresses P-S wave crosstalk and mitigates the adverse effects of wave-mode coupling, thereby enabling high-quality imaging of multicomponent seismic data acquired at the seafloor.

Imaging using S-wave seismic data based on elastic media holds the potential to address certain critical issues that conventional P-wave imaging cannot resolve, thereby offering new opportunities for oil and gas exploration. In this article, we have developed a feasible S-wave reverse time migration (RTM) imaging technique. Based on nonconversion elastic wave theory, we introduce auxiliary variables to construct a first-order pure S-wave equation, which is used to simulate the extrapolation of source wavefields in S-wave RTM. In addition, we employ the first-order velocity-stress equation for backward propagation and use decoupled elastic operators for decoupling of receiver wavefields, and utilize vector cross-correlation imaging conditions to obtain scalar imaging results. Compared with traditional P-wave RTM, the proposed S-wave RTM in this article addresses the limitations of traditional P-wave RTM in processing S-wave seismic data, demonstrating its advantages in reacting to reservoir fluid in the field of oil and gas exploration.

Full waveform inversion (FWI) has been considered one of the most promising approaches to estimating the high-resolution subsurface parameters, which takes advantage of the kinematics and dynamics information of seismic data. However, FWI is greatly dependent on the accuracy of the initial model and vulnerable to the issue of local minimum. Moreover, the multi-source and multi-parameter crosstalk artifacts make multi-source elastic FWI (MS-EFWI) more likely to trap into a suboptimal inversion result. To remedy this defect, this study proposes an efficient elastic FWI (EFWI) paradigm that combines the crosstalk-free MS-EFWI method and a well-guided initial model-building algorithm. Specifically, we apply a harmonic wavelet encoding technology to MS-EFWI, by which the multi-source wavefields can be completely deblended without crosstalk noise. The well-guided structure-oriented interpolation, with the aid of the dip information derived from the initial migration images, is designed to build a satisfactory initial model and therefore reduce the risk of cycle skipping. Numerical examples based on the 2D Overthrust model and Marmousi model further demonstrate the feasibility and robustness of the proposed method with a relatively little number of iterations.

Viscous effects cause strong energy decay and waveform changes of seismic waves. These distortions can be corrected usingQ-compensated reverse time migration ($Q$-RTM) algorithms, and high-resolution migration images can be obtained. However, all$Q$-RTM methods require a relatively accurate$Q$model. The traditional wave-equation$Q$tomography can invert the$Q$model by eliminating the difference in peak frequency between the observed and synthetic early arrivals. However, this approach only can be used to invert the$Q$value only for large-scale applications or on the surface. Moreover, the reflected wave can also be applied in the extended domain, but its computational efficiency is low compared to that of the early arrivals. To overcome these problems, this work proposes a new wave-equation-based$Q$inversion methodology to evaluate more accurate underground$Q$values in local domain. The proposed approach is applicable both to the early arrivals and reflected waves. Accordingly, we first transform the seismic data into the local domain using a sliding Gaussian window to alleviate the crosstalk noise in nearby seismic waves. Then, we use an improved cross correlation algorithm between the amplitude spectra of the observed and synthetic data to calculate the peak frequency shift of each seismic event in local domain. Thus, the inversion accuracy of$Q$can be improved by using different kinds of waves. The numerical inversion examples demonstrate the ability of our proposed method to produce satisfactory inversion results, especially in high-attenuation and deep areas. The$Q$-RTM images further illustrate the accuracy of our proposed$Q$tomography method.

Q-compensated reverse-time migration ( Q-RTM) has been proven as an efficient method for seismic imaging with high fidelity. However, the source (forward) and receiver (backward) wavefields propagate along the opposite direction of time, and the recursive computation with the out-of-order access requires that all the wavefields of source propagation should be stored on the hard disk. For massive amounts of seismic data, saving the source wavefield from the central processing unit (CPU) [or graphics processing unit (GPU)] device to the disk and loading these data from the hard disk to the CPU (or GPU) device become extremely intensive in time and storage, which has been a bottleneck of Q-RTM. Several methods have been developed to reduce the huge wavefield storage in acoustic media, but are not applicable in the attenuated media. In this letter, we present a reversible hybrid absorbing boundary condition for Q-RTM, which is implemented by mixing the reversible attenuation and the random boundary conditions. Based on our developed new boundary, we just need to save the wavefield at the last one or two time steps in the forward process and then reconstruct the source wavefield in the time-reversal order. Numerical results demonstrate that the method can avoid the huge seismic data input and output (I/O) requirement and improve the computational efficiency dramatically.

Q-compensated reverse time migration (Q-RTM) can compensate seismic attenuation caused by the anelastic behavior of subsurface media. Although, the traditional Q-RTM has high computational efficiency, it is instable because the high frequency or wavenumber ambient noise is exponentially boosted during forward and backward seismic wavefield propagation. The existing stable Q-RTM method costs twice as much computing time and memory compared to the traditional Q-RTM. In this letter, we propose a new Q-RTM method to address the above issues simultaneously. First, a new viscoacoustic wave equation is derived based on a wavefield decomposition method to obtain the velocity-dispersion-only and viscoacoustic wavefields efficiently. Then, a theoretical framework of stable and high-efficiency Q-RTM method is proposed based on the velocity-dispersion-only and viscoacoustic wavefields. The synthetic example shows that the new stable Q-RTM results match well with the reference images (without attenuation images). Moreover, the field data images also demonstrate the stability and high-efficiency of our proposed Q-RTM method.

Recovering signal from under-sampled and erratic noise-corrupted seismic data is indeed a challenging task because of its difficulty in simultaneous modeling of erratic noise and missing signal. Assuming that the recorded data are the superposition of low-rank and sparse components, many related works have been reported using a hybrid rank-sparsity constraint. Those published works typically detect the rank and erratic noise using empirical and global thresholds, which often fail to well characterize the varying sparsity and easily cause biased estimation in case of their nonstationary distribution. We propose an iterative double Laplacian-scaled low-rank optimization to adaptively select the sparsity and rank regularizer parameters for robust signal recovery. Comparing with the published approaches with global threshold, Laplacian-scaled mixture, which is obtained by multiplying Laplacian variable with a Gamma variable, is used to locally model the sparsity of erratic noise and the low-rank feature of signal. Then, the expectation-maximization (EM) algorithm is used to transform the Laplacian-scaled mixture problem into a localized reweighted ℓ1minimization scheme. The weighted coefficient appearing in its EM solver provides a variable constraint to locally address the rank and erratic noise, and hence, their regularizer parameters can dynamically reflect the different importance of those coefficients. We tested the effectiveness of the proposed method using under-sampled synthetic and field data that are corrupted by erratic noise and used other state-ofthe-art methods as comparisons. The results showed that more exact estimations of the signal and erratic noise can be obtained using the proposed method.

Sparse domain thresholding filters operating in a sparse domain are highly effective in removing Gaussian random noise under Gaussian distribution assumption. Erratic noise, which designates non-Gaussian noise that consists of large isolated events with known or unknown distribution, also needs to be explicitly taken into account. However, conventional sparse domain thresholding filters based on the least-squares (LS) criterion are severely sensitive to data with high-amplitude and non-Gaussian noise, i.e., the erratic noise, which makes the suppression of this type of noise extremely challenging. In this paper, we present a robust sparsity-promoting denoising model, in which the LS criterion is replaced by the Huber criterion to weaken the effects of erratic noise. The random and erratic noise is distinguished by using a data-adaptive parameter in the presented method, where random noise is described by mean square, while the erratic noise is downweighted through a damped weight. Different from conventional sparse domain thresholding filters, definition of the misfit between noisy data and recovered signal via the Huber criterion results in a nonlinear optimization problem. With the help of theoretical pseudoseismic data, an iterative robust sparsity-promoting filter is proposed to transform the nonlinear optimization problem into a linear LS problem through an iterative procedure. The main advantage of this transformation is that the nonlinear denoising filter can be solved by conventional LS solvers. Tests with several data sets demonstrate that the proposed denoising filter can successfully attenuate the erratic noise without damaging useful signal when compared with conventional denoising approaches based on the LS criterion.