In elastic reverse time migration (RTM), wavefield separation is an important step to remove crosstalk artifacts and improve imaging quality. State-of-the-art techniques for wavefield separation in isotropic elastic media include using the Helmholtz decomposition and introducing an auxiliary wave equation. Although these two approaches produce pure-mode vector wavefields with correct amplitudes, phases, and physical units, their computational costs are still high under current computational capability, especially for 3D large-scale problems. Based on the P- and S-wave dispersion relations, we have developed an efficient wavefield separation strategy for elastic RTM. Instead of solving a vector Poisson’s equation in the Helmholtz decomposition, we modify the phases of source wavelet as well as multicomponent records and scale the amplitudes of extrapolated wavefields with the squares of P- and S-wave velocities. This operation allows us to produce vector P- and S-wavefields with the same phases and amplitudes as the input coupled wavefields while significantly reducing computational costs. With the separated vector wavefields, we implemented a modified dot-product imaging condition for elastic RTM. In comparison with the previously proposed dot-product imaging condition, this modified imaging condition enables us to eliminate the effects of multiplication with a cosine function and hence produces migrated images with accurate amplitudes. Several 2D and 3D numerical examples are used to demonstrate the feasibility and robustness of our method for imaging complex subsurface structures.

Using adjoint-based elastic reverse time migration, it is difficult to produce high-quality reflectivity images due to the limited acquisition apertures, band-limited source time function, and irregular subsurface illumination. Through iteratively computing the Hessian inverse, least-squares migration enables us to reduce the point-spread-function effects and improve the image resolution and amplitude fidelity. By incorporating anisotropy in the 2D elastic wave equation, we have developed an elastic least-squares reverse time migration (LSRTM) method for multicomponent data from the vertically transversely isotropic (VTI) media. Using the perturbed stiffness parameters C33 and C55 as PP and PS reflectivities, we linearize the elastic VTI wave equation and obtain a Born modeling (demigration) operator. Then, we use the Lagrange multiplier method to derive the corresponding adjoint wave equation and reflectivity kernels. With linearized forward modeling and adjoint migration operators, we solve a linear inverse problem to estimate the subsurface reflectivity models for ΔC33 and ΔC55. To reduce the artifacts caused by data over-fitting, we introduce total-variation regularization into the reflectivity inversion, which promotes a sparse solution in terms of the model derivatives. To accelerate the convergence of LSRTM, we use source illumination to approximate the diagonal Hessian and use it as a preconditioner for the misfit gradient. Numerical examples help us determine that our elastic VTI LSRTM method can improve the spatial resolution and amplitude fidelity in comparison to adjoint migration.

Wave-mode decomposition plays a very important role in elastic reverse time migration (ERTM). Improved imaging quality can be achieved due to reduced wave-mode crosstalk artifacts. The current state-of-the-art methods for anisotropic wavefield separation are based on either splitting model strategy, low-rank approximation, or lower-upper (LU) factorization. Most of these involve expensive matrix computation and Fourier transforms with strong model assumptions. Based on the anisotropic-Helmholtz (ani-Helmholtz) decomposition operator and decoupled formulations, we develop a novel and efficient P-/S-wave-mode vector decomposition method in vertical transverse isotropic (VTI) media with application in ERTM. We first review the basics of classical Helmholtz decomposition and isotropic decoupled formulations. In addition, the ani-Helmholtz decomposition operator is built from the P- and S-wave polarizations of the Christoffel equation in VTI media. We then derive novel decoupled formulations of anisotropic P-/S-waves based on the obtained ani-Helmholtz operator. Moreover, we use the first-order Taylor expansion to approximate the normalization term from the derived decoupled formulations and obtain an efficient ani-Helmholtz decomposition approach, which produces vector P- and S-wavefields with correct units, phases, and amplitudes. Compared with the previous studies, our approach mitigates model assumptions, avoids intricate calculations, such as LU factorization or low-rank approximation, and only needs three fast Fourier transforms at each time step. In addition, the graphic processing unit technique is used to dramatically accelerate various functions of ERTM, such as wavefields extrapolation, decomposition, and imaging. Three synthetic examples demonstrate the effectiveness and feasibility of our proposed approach.

Most existing traditional and deep learning (DL)-based methods used for random noise attenuation or reconstruction of seismic data typically only process two-dimensional (2-D) data. Very few methods are able to perform both denoising and reconstruction tasks for three-dimensional (3-D) seismic data. We develop a framework based on a self-supervised 3-D partial convolutional neural network (3-DPCNN) for multi-task processing of single 3-D seismic data volume, including random noise attenuation, reconstruction, and simultaneous denoising and reconstruction. The proposed method utilizes 3-D spatial structure information via 3-D convolution kernels and exploits Bernoulli sampling to generate training data pairs and test data. Attributed to Bernoulli sampling, the 3-DPCNN can be trained with only one noisy and/or corrupted seismic data volume; therefore, all supervised information is derived from the original data, and no external supervised information is required. The data augmentation strategy does not always boost the performance of the 3-DPCNN. Therefore, whether it is used and which transform is randomly employed are determined by the task type and the data. In addition, a double ensemble learning strategy is employed to boost 3-DPCNN performance and avoid randomness in the predictions. We evaluate the proposed method using multiple synthetic and field data. The experiments show that the proposed method has remarkable denoising and reconstruction abilities and is competitive with and even superior to a variety of traditional and DL-based benchmark algorithms.