We consider methods for prediction of internal multiples that combine events in the data through convolutions and correlations, in particular methods based on the inverse scattering series and methods that predict a subset of internal multiples related to specified boundaries in the subsurface. Such methods provide kinematically accurate models when their prerequisites are satisfied but have fundamental limitations in terms of predicting accurate amplitudes; the amplitudes are under-predicted due to approximations related to scattering and anelastic attenuation losses.

We propose variations of these prediction methods that use models of attenuation in the subsurface (scattering or anelastic attenuation factors) to improve the amplitudes of the predicted internal multiples. The models of attenuation required in our approach may be derived from borehole data (logs, vertical seismic profiles) or from surface seismic data (e.g. from Q tomography) and are used in pre-processing the data input to the prediction algorithms. We illustrate the new algorithms using synthetic data for 1D earth and 3D point source. The proposed methods facilitate the adaptive subtraction of the predicted internal multiples from the data and would naturally complement workflows that estimate attenuation from the seismic data and apply Q compensation during imaging.


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