Using least-squares Kirchhoff for better shallow imaging of spatially sparse dual-WAZ data

WAZ’s sparse spatial sampling tends to limit our ability to push for shallow higher resolution image. In the deepwater GOM, there is typically more than one WAZ survey available. However, direct Kirchhoff imaging from multiple data sets suffers poor swing cancellation. To mitigate this problem, we can combine multi-WAZ data and perform interpolation prior to Kirchhoff. Interpolation reduces migration swings but is often not perfect for complex structures and smears details. We propose to use preconditioned least-squares (LS) Kirchhoff to obtain a higher resolution image that can simultaneously benefit from dual-WAZ data sets and overcome the limitation of coarse sampling.