Attenuation of 2D specularly-reflected multiples in image space

Gabriel Alvarez

gabriel@sep.stanford.edu

ABSTRACT

I propose a new method to attenuate specularly-reflected multiples in the image space. The method is based on the difference in mapping between primaries and multiples in Subsurface Offset Domain Common Image Gathers (SODCIGs). I migrate the data with a velocity slower than that of the primaries but faster than that of the multiples. The primaries are therefore undermigrated whereas the multiples are overmigrated. For positive data offsets, the primaries are mapped to positive subsurface offsets and the multiples to negative subsurface offsets in SODCIGs. I then apply a tapered mute to eliminate the primaries and do adjoint migration on the multiples with the same velocity model to get an estimate of the multiples in data space. Similarly, a tapered mute is applied to eliminate the multiples and adjoint migration used to obtain an estimate of the primaries in data space. The estimate of the multiples is adaptively matched to the data with the estimate of the primaries used as a weight function to prevent matching the primaries. I illustrate the method with a 2D synthetic dataset and show that the primaries can be well recovered although some residual from the water bottom multiple remains.