Joint SRME and model-based water-layer demultiple for ocean bottom node

OBN SRME that combines OBN and streamer data is known to be an effective way to predict surface-related multiples in OBN data. However, the available streamer data often have limited offset/azimuth coverage. Additionally, the double source wavelets due to the cross-convolution of OBN and streamer data limit the bandwidth (loss of low and high frequency) of the multiple prediction. OBN model-based water-layer demultiple (MWD) overcomes such limitations and is a good complement of OBN SRME; MWD replaces the streamer data with the water-bottom Green’s function that has no offset/azimuth limitation and keeps the full bandwidth of the input data. With Gulf of Mexico (GOM) OBN data over the Atlantis field, we illustrate the benefit of joint SRME and MWD over solely SRME with the improved attenuation of low-frequency multiples and deep peg-leg multiples.