Seismic forward modeling is crucial for exploration geophysics, especially in seismic exploration. As research has progressed, the limitations of forward models assuming homogeneous media have become increasingly evident. These models no longer adequately address the complexities of real-world scenarios. Furthermore, the multiscale heterogeneity of subsurface structures significantly influences exploration outcomes and geological interpretations. Therefore, constructing a random medium model that accurately reflects heterogeneity is necessary. However, the current modeling methods face issues of low efficiency and insufficient flexibility, making it difficult to construct complex random media models. Therefore, this article proposes a random medium modeling method that combines Wasserstein convolutional generative adversarial networks (WCGANs) and velocity masking. A training dataset is constructed through spectral decomposition, and the random media parameters are constrained into a composite conditional vector. The Wasserstein distance and gradient penalty are added to the loss function to train the networks. Also, the generator and discriminator are improved using convolutional network structures. After comprehensive evaluation through visual inspection, principal component analysis (PCA), and multiscale sliced Wasserstein distance (MS-SWD), the results show that this method improves training stability and the diversity of generated random perturbation models. Consequently, using WCGANs can improve modeling efficiency, while masking techniques can control the characteristics of different regions in the model, simulating complex geological structures. Therefore, by combining these two methods, it is possible to establish a regional multiscale random media model. This approach efficiently simulates subsurface heterogeneity, addressing issues of long modeling times and inflexibility.

Complex topography is a challenging issue in onshore seismic exploration. The rugged terrain and lateral change of near-surface velocity pose a significant obstacle to the accurate imaging of seismic data. The adaptive focused beam migration method retains the good applicability of the ray methods for calculating the seismic wavefield under complex surface conditions. It can effectively solve the contradiction between the imaging accuracy of deep and shallow strata in traditional Gaussian beam migration. We extend the adaptive focused beam migration approach to the deep domain imaging of seismic data under complex surface conditions. First, the basic principles of the adaptive focused beam are reviewed. Then, Green’s functions of the seismic source and the receiving point of rugged topography are characterized by the adaptive focused beam, and an adaptive focused beam prestack depth migration method based on cross correlation imaging is proposed. The full-wave-arrival imaging strategy is applied to image all wave arrivals of subsurface imaging points. A single input seismic trace is adopted for imaging, which can directly emit the focused beam from the receiving point of rugged topography for wave field continuation, thus avoiding multiple focusing. As a result, the applicability of the migration approach to complex surface conditions was improved, and the imaging accuracy of the migration method was also effectively enhanced. The numerical model migration test of different rugged topography conditions and tectonic forms verified that the proposed method was an effective prestack depth migration applicable to accurately imaging seismic data under the rugged topography condition.

Anisotropic elastic wave vector decomposition for common-shot multicomponent data is a critical step in multicomponent seismic exploration. It is more difficult than the separation for elastic wavefield snapshots because the spatial derivatives along vertical direction cannot be calculated directly. In this letter, we propose an effective workflow to implement elastic wave vector decomposition for common-shot multicomponent data based on the pure-wave equation in transversely isotropic (TI) media. First, we extrapolate the multicomponent data backward in time to a reference surface using an anisotropic elastic wave equation, implement wave-mode separation by the projection of the extrapolated anisotropic elastic wavefields onto the polarization vectors of P- and S-wave-modes, and record the separated pure-wave data at the reference depth. Second, we extrapolate the separated anisotropic pure-wave data forward in time using the anisotropic pure-wave equations, perform the vector decomposition by the projection of the extrapolated pure-wave wavefields onto the polarization vectors of P- and S-modes, and record the vector anisotropic pure-wave data at the recording surface. This method can produce vector pure-mode data from the anisotropic medium with relatively high accuracy. Synthetic examples show the feasibility of this method.

The deep reflection seismic technique is essential to detect the basement of petroliferous basins and the fine structure of lithosphere. The traditional stacking or time migration is usually adopted for imaging deep reflection seismic data, but the accuracy is too limited to acquire precise imaging profiles. Development of an effective prestack depth migration method for deep reflection seismic data is the urgent requirement in the field of lithosphere structure detection. The Gaussian beam summation (GBS) migration requires no local slant stacking or phase approximation. It has higher imaging precision and better flexibility and applicability to the acquisition system, which can be well applied to imaging long-array and large-trace-interval deep reflection seismic data. In this letter, the GBS migration is extended into depth-domain imaging of deep reflection seismic data. Firstly, the basic principles of GBS migration are reviewed. Then, the validity of the method is verified by a migration test on a simple crustal-scale model. Finally, according to the crust-mantle structure of typical areas, a crustal model of western Sichuan and eastern Tibet and a classical collision model are established for migration experiments, and accurate deep-domain images are obtained. We mainly conducted numerical tests for the GBS migration of deep reflection seismic data in this letter. It was verified through imaging studies by the typical crust-mantle structure model that the proposed method was a prestack depth migration method suitable for the precise imaging of deep reflection seismic data, which provided a numerical basis for its subsequent imaging application in field data.

Imaging PS-wave is essential for converted wave exploration, especially in tilted transversely isotropic (TTI) media. Compared with the relatively low imaging accuracy of anisotropic time migration for complex structures, an accurate anisotropic PS-wave depth migration approach would be preferable. As an effective depth migration technique, Gaussian beam summation (GBS) migration can provide high-precision imaging for complex geological structures. In this letter, we extend the GBS to PS-waves imaging in anisotropic media and present an angle-domain GBS migration method for converted waves in 2-D TTI media. We first introduce the anisotropic ray-tracing-based angle-domain GBS imaging condition of PS-waves in TTI media, in which the sign of the incidence angle of P-waves is applied to decide the PS-wave polarity. After calculating the propagation angles at imaging points by using the data of real-value travel time of anisotropic Gaussian beams, we can get the P-wave incidence angles and then extract the corresponding PS-wave angle-domain common-image gathers (ADCIGs). The performance of our method is verified by two numerical tests, indicating that it is an effective migration algorithm for accurately imaging converted waves in 2-D TTI media.

Anelasticity of the earth subsurface medium, which is quantified by the quality factor$Q$, causes the dissipation of seismic energy. Strong attenuation effect resulting from geology such as gas clouds (gas-filled sandstone) is a challenging problem for high-resolution imaging. To compensate the attenuation effect, first we need to accurately estimate the attenuation parameter. However, it is difficult to directly derive a heterogeneous attenuation Q model. This research letter proposes a method to derive a Q model corresponding to strong attenuative media from marine reflection seismic data using convolutional neural network (CNN), a popular deep learning framework. We treat Q anomaly detection problem as a semantic segmentation task and train a network to perform a pixel-by-pixel prediction to invert a pixel group that belongs to the strong level of attenuation probability. The proposed method uses a volume of marine 3-D reflection seismic data for network training and validation, which needs only a small part of real data as the training set due to the feature of U-Net. In the final stage, to evaluate the attenuation model, we validate the predicted heterogeneous Q model using deabsorption prestack depth migration (Q-PSDM), a high-resolution imaging result in depth domain with appropriate compensation is obtained.

Processing pressure shear (PS)-wave data is more challenging than PP-wave data because of the asymmetry of the source-to-receiver ray paths, particularly for irregular surfaces and sparse acquisition. Gaussian beam summation (GBS) migration is an effective method for imaging seismic data from irregular surfaces. In this letter, we introduce a converted PS-wave migration method for irregular surfaces using GBS, in which a scalar wavefield is used for wavefield propagation imaging. Cross correlation imaging is performed using forward-continued source wavefields calculated with the P-wave and reverse-continued wavefields calculated using the S-waves from the receivers. The corresponding Green's function is constructed as a superposition integral of the Gaussian beams emitted from the source and the receivers on the irregular surfaces, respectively. Numerical tests demonstrate that the method is a flexible and effective alternative for accurate imaging of converted PS-wave data from irregular surfaces.