Shallow water demultiple using hybrid multichannel prediction

Abstract

This paper presents an extension of our previous effort on multiple attenuation in shallow water environment. While our previous workflow, termed as Shallow Water Demultiple (SWD), is robust in suppressing water-layer related multiples (WLRMs) with shallow seafloor, it faces difficulties when the seafloor is too shallow and complex because of the near offset gap related to acquisition. The wavelet stretch resulted from near offset extrapolation causes spectral distortion in multiple model from SWD which leads to sub-optimized subtraction result in shallow part of the data. The new method is a hybrid approach in which shallow WLRMs are handled by using the Greenâ??s function of the seafloor primary reflections, while the rest of the multiples are handled by SWD. The Greenâ??s function in this case is derived from auto-picking the traveltime of the multichannel prediction operator estimated from SWD. The approach combines the strengths of SWD and model-based methods. We show that the multichannel prediction operator estimated in SWD can be used as an accurate kinematic representation of seafloor reflection with high signal-to-noise (S/N) ratio. With this operator, the traveltime of the seafloor event can then be automatically estimated. Making use of this traveltime information, the Green's functions of the water-layer primary reflections can be modeled for tackling the shallow peg-lag multiples that have difficulty handled by SWD. We show the application of our hybrid method for suppressing shallow water multiples on field data acquired from offshore Australia