Sparse wave-equation deconvolution imaging for improved shallow water demultiple

The attenuation of surface related multiples is typically one of the most challenging steps in the processing of shallow water marine projects. Least-squares wave-equation deconvolution imaging is a powerful tool to address this challenge, but images derived from a deep target level may produce sub-optimal demultiple results for the shallower section. We introduce image domain sparseness weights to the least-squares problem, derived from a water-bottom depth estimate. This provides a reflectivity with a sharp contrast at the water-bottom, and the corresponding multiple prediction exhibits improved temporal resolution compared to least-squares wave-equation deconvolution imaging. We also illustrate how the multiple prediction from sparse wave-equation deconvolution imaging may be combined with source-side targeted multiple prediction to improve multiple attenuation for complex multiple generators. Data examples from the Central North Sea and the West of Shetland confirm the benefits of the proposed methods in attenuating residual multiples.