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.

Separating compressional and shear wavefields is an important step in elastic reverse-time migration, which can remove wave-mode crosstalk artifacts and improve imaging quality. In vertical (VTI) and titled (TTI) transversely isotropic media, the state-of-art techniques for wavefield separation are based on either non-stationary filter or low-rank approximation. They both require intensive Fourier transforms for models with strong heterogeneity. Based on the eigenform analysis, we develop an efficient pseudo-Helmholtz decomposition method for VTI and TTI media, which produces vector P- and S-wavefields with the same amplitudes, phases and physical units as the input elastic wavefields. Starting from the elastic VTI wave equations, we first derive the analytical eigenvalues and eigenvectors, then use the Taylor expansion to approximate the square-root term in the eigenvalues, and finally obtain a zero-order and a first-order pseudo-Helmholtz decomposition operator. Because the zero-order operator is the true solution for the case of ε = δ, it produces accurate wavefield separation results for elliptical anisotropic media. The first-order separation operator is more accurate for non-elliptical anisotropy. Since the proposed pseudo-Helmholtz decomposition requires solving an anisotropic Poisson’s equation, we propose two fast numerical solvers. One is based on the sparse LU factorization, which can be repeatedly applied to the input elastic wavefields once computing the lower and upper triangular matrices. The second solver assumes the model parameters are laterally homogeneous within a given migration aperture. This assumption allows us to efficiently solve the anisotropic Poisson’s equation in the z − k_x domain, where k_x and z denote the horizontal wavenumber and depth, respectively. Using the coordinate transform, we extend the pseudo-Helmholtz decomposition to TTI media. The separated vector wavefields are used to produce PP and PS images by applying a dot-product imaging condition. Several numerical examples demonstrate the feasibility and applicability of the proposed methods.

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.

With a finite recording aperture, a limited source spectrum and unbalanced illumination, traditional imaging methods are insufficient to generate satisfactory depth profiles with high resolution and high amplitude fidelity. This is because traditional migration uses the adjoint operator of the forward modelling rather than the inverse operator. We propose a least-squares migration approach based on the time-domain Gaussian beam summation, which helps to balance subsurface illumination and improve image resolution. Based on the Born approximation for the isotropic acoustic wave equation, we derive a linear time-domain Gaussian beam modelling operator, which significantly reduces computational costs in comparison with the spectral method. Then, we formulate the corresponding adjoint Gaussian beam migration, as the gradient of an L₂-norm waveform misfit function. An L₁-norm regularization is introduced to the inversion to enhance the robustness of least-squares migration, and an approximated diagonal Hessian is used as a pre-conditioner to speed convergence. Synthetic and field data examples demonstrate that the proposed approach improves imaging resolution and amplitude fidelity in comparison with traditional Gaussian beam migration.

Anisotropy is a common phenomenon in subsurface strata and should be considered in seismic imaging and inversion. Seismic imaging in a vertical transversely isotropic (VTI) medium does not take into account the effects of the tilt angles, which can lead to degraded migrated images in areas with strong anisotropy. To correct such waveform distortion, reduce related image artifacts, and improve migration resolution, a tilted transversely isotropic (TTI) least-squares reverse time migration (LSRTM) method is presented. In the LSRTM, a pure qP-wave equation is used and solved with the finite-difference method. We have analyzed the stability condition for the pure qP-wave equation using the matrix method, which is used to ensure the stability of wave propagation in the TTI medium. Based on this wave equation, we derive a corresponding demigration (Born modeling) and adjoint migration operators to implement TTI LSRTM. Numerical tests on the synthetic data show the advantages of TTI LSRTM over VTI RTM and VTI LSRTM when the recorded data contain strong effects caused by large tilt angles. Our numerical experiments illustrate that the sensitivity of the adopted TTI LSRTM to the migration velocity errors is much higher than that to the anisotropic parameters (including epsilon, delta, and tilted angle parameters), and its sensitivity to the epsilon model and tilt angle is higher than that to the delta model.