The internal multiple prediction method based on Inverse Scattering Series is an efficient tool to predict internal multiple energy present in the data. It is entirely data driven, without the need for, e.g., interpreted horizons or a velocity model. When applied as a quality assurance and control tool it provides the interpreter with valuable information on areas where there is a high chance of internal multiple contamination and interference with primary energy. Predicting a model of the internal multiples present in the data, can allow an interpreter to have a better control of the reservoir geometry and to reduce uncertainties in structural interpretation. Additional application of adaptive subtraction can further improve data for analysis and quantitative interpretation, and can lead to improved reservoir characterization. In this work, we focus on the data driven prediction of internal multiples in time migrated data, after stack, and present our initial work on interpreter-guided adaptive subtraction.


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