Improving multiple prediction in image space using ADCIGs for limited-offset recordings

Madhav Vyas

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ABSTRACT

Auto-convolution is a convenient way of generating multiple models and is the basis for Surface Related Multiple Prediction (SRMP). The completeness of the multiple model, however, relies on recording all the primary paths that contribute to multiple generation. In practice, with limited offset recordings and in areas of complex subsurface geometries (especially steep dips), we might record only the multiple path and not the primary, leading to incomplete multiple models. In image space, this translates to modeling multiples at fewer opening angles than are actually present. In this article, I show that the Angle Domain Common Image Gathers (ADCIGs) of multiple models provide useful angular information which may be used to infill or extrapolate missing angles and account for the missing multiples in the model generated using SRMP.