Time-domain least-squares Gaussian beam migration with L1 regularization

Yang, Jidong (Department of Geosciences, University of Texas–Dallas) | Zhu, Hejun (Department of Geosciences, University of Texas–Dallas)


With a finite recording aperture, a limited source spectrum and irregular acquisition geometry, conventional imaging methods are insufficient to generate satisfactory depth profiles with high resolution and high amplitude fidelity. This is because conventional migration is commonly formuated as the adjoint, instead of the inverse operator of the forward modeling. We propose a time-domain least-squares Gaussian beam migration, which helps us to balance subsurface illumination and improve image resolution. Based on the Born approximation for the acoustic wave equation, we first derive alinear time-domain Gaussian beam modeling operator. Then, we formulate its adjoint operator, i.e., Gaussianbeam migration, as the gradient of an L2-norm misfit function. An L1-norm regularization is introduced into the inversion to enhance the robustness of leastsquares migration. Synthetic examples for Marmousi model demonstrate that the proposed approach significantly improves imaging resolution and amplitude fidelity in comparison with conventional Gaussian beam migration.

Presentation Date: Tuesday, October 16, 2018

Start Time: 1:50:00 PM

Location: 207A (Anaheim Convention Center)

Presentation Type: Oral