This paper focuses on the attenuation of Water-Layer-Related Multiples (WLRMs or peg-leg multiples) which reflect at least once between the water bottom and the water surface. WLRMs are often the most dominant multiples in shallow-water seismic data. We propose a Model-based Water-layer Demultiple (MWD) algorithm to calculate the Green's functions of the Water-Layer Primary Reflections (WLPRs: Green's functions convolved with source signature) based on the known seabed and water-layer velocity model and then convolve them with the recorded data to predict the WLRMs. Combined with adaptive subtraction, MWD can effectively attenuate WLRMs. We apply MWD to field data from the Hibernia oilfield area which has a water depth of 70-90 m. The results show that while Surface-Related Multiple Elimination (SRME) by itself has limited success, MWD is effective in attacking WLRMs. Once the WLRMs have been removed by MWD, successive SRME can then be applied to predict and eliminate other types of surface-related multiples (SRMs). The combination of MWD and SRME is demonstrated as an effective multiple attenuation package for shallow-water data and results in fewer residual multiples and better preserved primaries over tau-p gapped deconvolution. This, in turn, contributes to a more realistic velocity model and higher-quality images.

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Model-based Water-layer Demultiple (MWD) is a recently-developed method aimed at tackling the challenge of multiple attenuation in shallow water. MWD works by modeling the Green's function of the water-bottom primary reflections based on a user-supplied water-layer model, then convolving it with the recorded data to predict water-layer-related multiples. In this paper, MWD is applied to Hibernia field data which has a water depth of around 70-90 meters. The results show that while SRME by itself has limited success, MWD is effective in attacking water-layer-related multiples. The effectiveness is attributed to the fact that MWD predicts the multiple models with correct relative amplitude and a spectrum similar to the input data's. SRME, on the other hand, suffers in shallow-water situations, primarily due to cross-talk between multiples. Once the water-layer-related multiples are removed by MWD, SRME can then be applied to predict and eliminate other types of surface-related multiples which tend to have longer periodicity and less cross-talk. The combination of MWD and SRME is demonstrated as an effective demultiple package for shallow-water data and results in fewer residual multiples and better-preserved primaries over tau-p gapped deconvolution. This, in turn, contributes to a more realistic velocity model and, finally, higher quality images.

Removing the receiver ghost from marine towed streamer data before migration provides better low and high frequency response as well as a higher signal-to-noise ratio for preprocessing steps such as multiple suppression and velocity analysis. The combination of pressure data recorded by hydrophones and particle velocity data or acceleration data recorded by motion sensors has the potential to reliably derive a ghost-free wavefield. We present a progressive joint sparse p   inversion method to perform 3D deghosting using pressure data) (P , the acceleration z-component) (z A , and the acceleration ycomponent) (y A. We demonstrate the effectiveness of this method using a multi-sensor streamer data set from the North Sea.