Since subsurface structures are often anisotropic, conventional anisotropic acoustic wave-equation depth migration methods are generally limited to one-way wave equations. In contrast, two-way wave-equation depth migration (TWDM) offers superior imaging capabilities to the one-way wave migration method. This study advances seismic imaging by extending full acoustic wave-equation depth migration to vertical transversely isotropic (VTI) media. It utilizes a generalized two-way phase shift propagator and the Thomas dispersion equation to address the complexities of anisotropic media. This approach performs the generalized phase operator without lateral velocity approximation and significantly enhances subsurface imaging amplitudes due to the complete wave equation used. Numerical examples illustrate the effectiveness and superiority of the proposed method.

We have explored an unsupervised deep learning (DL)-based approach for the efficient and effective reconstruction of noisy and incomplete (N-I) seismic data. This method does not require clean and complete (C-C) seismic data as labeled data. In each iteration, the seismic data input to the network is first subjected to patching techniques, dividing the 2-D or 3-D data into many 1-D signals. Then, to boost the efficiency, a reconstruction error-based patch selection (REBPS) is employed to choose patches that contain more complex structures, which are then fed into the network. The network adopts an encoder and corresponding decoder architecture to compress and reconstruct data features, attenuating noise within the seismic data and performing an initial reconstruction of the missing parts. To improve the reconstruction accuracy, we employ the projection onto convex sets (POCS) algorithm, ultimately obtaining reconstructed data from one iteration. In this process, the output results of each POCS iteration serve as the input for the next round. Through experimental verification using both synthetic and field seismic data, the results show that our proposed method surpasses other comparative methods in the quality of seismic data reconstruction.

Since seismic imaging creates an image of the subsurface structure based on information received from the measured wavefield, it is essential to fully utilize the reflected waves. Full Wavefield Modelling (FWMod) was developed with recursive and iterative up-and-down wavefield propagation, using one-way wave propagation, to model both primary and multiple reflections. Using FWMod as the modelling engine, FullWavefield Migration (FWM), has been introduced to directly image data including internal multiples, where internal multiple crosstalk is suppressed automatically via an inversion-based data-fitting process. This avoids the need for applying internal multiple removal, which is often challenging. Conventional one-way wave propagators calculated in the wavenumber domain, like the phase shift (PS) operator, have limitations when applied to strongly inhomogeneous media. Even when computing a new operator at each lateral grid point, they still suffer difficulties because the medium is assumed to be locally homogeneous. In the past, matrix eigendecomposition has been proposed as a way to create accurate, local velocity-based one-way propagation operators. In this paper, an accurate propagator based on eigendecomposition is incorporated into FWMod and FWM. In the numerical examples, four models with strong lateral velocity variations were used to test the propagator. With a comparison of the conventional FWM based on the PS operator with input data including FWMod and a finite-difference approach, the numerical examples demonstrated that the proposed method has the potential to significantly enhance image reflectivity, suppress internal multiples, and maintain convergence speed during the least-squares inversion.

Epilepsy, a chronic neuropsychiatric brain disorder characterized with recurrent seizures, is closely associated with abnormal neural communications within the brain. Despite that the phase-amplitude coupling (PAC) has been suggested to offer a new way to observe neural interactions during epilepsy, however, few studies pay attention to alterations of the epileptic functional brain network based on PAC, especially on the [Formula: see text] PAC. Therefore, we use scalp electroencephalography (EEG) data of epileptic patients and the [Formula: see text] PAC modulation index (MI) to construct functional brain networks to examine variations of neural interactions during different epileptic phases. Statistically, the findings show that between-channel MI values in the post-ictal period significantly increase compared to that in the pre-ictal period, and the between-channel MI value has a close association with the information of phase and amplitude provided by the channels. Importantly, in both the phase-amplitude and amplitude-phase functional brain networks, the average node degree is remarkably higher in the post-ictal period than that in the pre-ictal period, whereas the characteristic path length in the ictal and post-ictal periods is significantly lower than that in the pre-ictal period. Besides, the average betweenness centrality in the post-ictal period is remarkably higher than that in the ictal period. Interestingly, the positive correlations between within-channel MI values and between-channel MI values can be observed during the pre-ictal, ictal and post-ictal periods. These findings suggest that the [Formula: see text] PAC-based functional brain network may provide a novel perspective to understanding alterations of neural interactions during the epileptic evolution, and may contribute to effectively controlling the spread of epileptic seizures.