Conventional amplitude inversion assumes that the input migrated image has preserved relative amplitude information and is free from the effects of illumination. Under this assumption, stretching a depth migrated image back to time and applying inversion based on 1D convolutional modeling can produce reasonable results. However, illumination effects in complex geological settings (such as shadow zones in subsalt imaging) pose a challenge to even the most advanced imaging algorithms such as reverse-time migration (RTM). Traditional approaches to compensate for illumination effects in migrated images are difficult to regularize in areas of very poor illumination. We address this problem by using the modelled response of the acquisition and imaging process, defined by Point Spread Functions (PSFs), to include these effects in forward modeling for inversion directly in the depth domain. We demonstrate this approach for poststack inversion of synthetic, subsalt data and also apply it to field data from the Gulf of Mexico.


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