Practical benefits of Kirchhoff least-squares migration deconvolution

Images from Kirchhoff migration can suffer from uneven illumination and contamination by migration artefacts. One of the issues is that migration is not a true inverse operation – it is based on the adjoint of the forward modelling operator. In contrast, least squares migration approximates the inverse of the forward modelling and hence the impact of detrimental effects on the image can be reduced. Here, we describe benefits of a non-iterative Kirchhoff least squares method (migration deconvolution). We present a workflow and demonstrate that the method can be used to attenuate image artefacts, help balance image illumination, and increase clarity of AVO attributes.