One important requirement for least-squares imaging (LSM) is having an accurate migration velocity model. LSM with large velocity errors results in erroneous reflector locations, strong swing artifact and even non-convergence. To mitigate these issues, we develop a novel least-squares imaging framework in the subsurface reflection angle domain. Instead of using high-wavenumber velocity perturbations for the reflectivity model as is done in traditional LSM, we parameterize the wave equation with an angle-dependent reflectivity, and derive the corresponding linearized forward modeling and adjoint migration operators. Because Gaussian Beam migration naturally incorporates the propagation directions in wavefield extrapolation, we represent the Green’s function using the Gaussian beam summation method. To improve the common-image gather (CIG) quality for low fold and low SNR data, a shaping regularization over the incident angle direction is introduced into the conjugate gradient scheme to iteratively update the angle-dependent reflectivity model. A flattening-enhanced summation is used to compute the stacked images by accounting for the depth moveout of CIGs caused by velocity errors, and produces constructive stacking results. Numerical experiments for a land survey demonstrate that the proposed method can improve LSM convergence and produce high-quality angle-dependent and stacked images with inaccurate migration velocity models.

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