Paper Summary
Least-squares imaging in image domain utilizes Point Spread Functions (PSFs) to estimate the Hessian matrix. The conventional way to compute PSFs involves cascaded operations-modelling followed by migration. In this paper, we propose a least-squares Kirchhoff imaging method in which the PSFs are computed explicitly with a simplified formula and then reshaped with the Kirchhoff image. A preconditioned iterative conjugate solver is chosen for the inversion as the final step of the least-squares imaging. The field example demonstrates the improvements in image resolution and clarity with the proposed method.