Image-space surface-related multiple prediction

Abstract

A very important aspect of removing multiples from seismic data is accurate prediction of their kinematics. We cast the multiple prediction problem as an operation in the image space parallel to the conventional surface-related multiple-prediction methodology. Though developed in the image domain, the technique shares the data-driven strengths of data-domain surface-related multiple elimination (SRME) by being independent of the earth (velocity) model. Also, the data are used to predict the multiples exactly so that a Radon transform need not be designed to separate the two types of events. The cost of the prediction is approximately the same as that of data-space methods, though it can be computed during the course of migration. The additional cost is not significant compared to that incurred by shot-profile migration, though split-spread gathers must be used. Image-space multiple predictions are generated by autoconvolving the traces in each shot-gather at every depth level during the course of a shot-profile migration. The prediction in the image domain is equivalent to that produced by migrating the data-space convolutional prediction. Adaptive subtraction of the prediction from the image is required. Subtraction in the image domain, however, provides the advantages of focused energy in a smaller domain since extrapolation removes some of the imperfections of the input data.