We report case study results for attenuation of free-surface multiples from deep-water ocean bottom node (OBN) data using a data-driven multiple prediction method that combines OBN and towed-streamer data through multidimensional convolution, similar to the well-known surface-related multiple elimination (SRME) method.

We illustrate the properties of the proposed multiple prediction method using synthetic and field data and note that availability of suitably acquired and processed streamer data is critical to the success of this approach.

In our case study, we have data from five streamer surveys with offsets up to 10 km and broad range of azimuths. Correspondingly, the results of data-driven multiple attenuation are good for the OBN data with offsets up to about the maximum offset of the streamer data. We also compute a model-based prediction of the free-surface multiples using the anisotropic velocity model and prior depth images available for this field.

The data-driven and the model-based approaches of predicting free-surface multiples have complementary properties. We combine models computed with both approaches to attenuate free-surface multiples in the OBN upgoing and downgoing data, as needed for subsalt imaging.