High-Resolution Elastic Reverse-Time Migration Using the Point-Spread Function Deconvolution With Density Scatterers

High-resolution and amplitude-preserved imaging is crucial for mapping impedance interfaces and identifying hydrocarbon reservoirs in the subsurface. Although elastic reverse-time migration (RTM) is capable of imaging complicated structures, it actually is the adjoint of seismic forward modeling and produces unsatisfactory images with irregular acquisition systems and uneven illumination. To address this issue, we develop an elastic image-domain least-squares migration (LSM) method based on the point-spread function (PSF) deconvolution. Instead of choosing P- and S-wave velocities as the reflectivities in conventional elastic LSM, we define the density perturbation as the reflectivity model. Full-wavefield elastic modeling and PS-separation-based RTM are then applied to compute PSFs. This framework does not involve the crosstalk issue because only two types of PSFs, which correspond to the diagonal blocks of the Hessian matrix, are generated. Then, the two kinds of PSFs are, respectively, used for local image deconvolution for PP and PS images to correct the Hessian blurring effect, in which the unit partitioning with multidimensional Gaussian functions is adopted to generate optimal local windows. Numerical experiments demonstrate the feasibility of the proposed method and the potential to enhance image resolution and amplitude fidelity.