Predicting Internal Multiples by Inversion Based Imaging

To handle internal multiples in imaging can be done via data-driven procedures (e.g. Marchenko-based multiple removal) or via an image-driven process that includes an option to model internal multiples from the reflectivities. The advantage of data-driven methods is that it can utilize the physics implicitly available in the data, while the image-driven method has to make this physical model more explicit via an image, which may create some leakage. However, a main advantage of the image-driven approach is that it is very insensitive to coarse source sampling, while data-driven methods requires dense data. The aspect of coarse sampling and its effect on the final multiple suppression effects of the image-driven approach is demonstrated.