Internal Multiple Attenuation on Radial Gathers With Inverse-scattering Series Prediction

Abstract

We present a novel workflow for pre-stack prediction and attenuation of internal multiples, applicable to data acquired with orthogonal survey geometries over geologies with mild structural variations. First, we interpolate the data pre-stack (5D) data to common midpoint (CMP) gathers with regular sampling in offset and azimuth. Such CMP gathers are suitable for noise attenuation as shown in previously published case studies. Next, we predict internal multiples in radial gathers, i.e. common midpoint and common azimuth gathers. We use the leading term of the inverse scattering series to perform the algorithm called inverse-scattering series internal multiple prediction (ISIMP) and obtain a pre-stack model of internal multiples without input of subsurface information. We follow the prediction of internal multiples with multidimensional adaptive subtraction. We apply this workflow to data from the Cooper basin, Australia. Our results demonstrate overall improvements in primary resolution and reveal weak primary events previously obscured by multiples. We note that the transformation to the radial domain has been a key enabler for this workflow.