Water-layer Related Multiples Research Articles

Shallow Water Demultiple

Multiples due to shallow water are observed in seismic data acquired in various places such as the Gippsland Basin of Australia. These short period multiple reflections often pose problems to the interpretation of geological structures. They are not easily handled by conventional surface-related multiple elimination (SRME) methods because the recorded primary waterbottom reflection, which is required by SRME, is often indistinct in shallow water situations due to the near offset gap. Hence, predictive deconvolution in the x-t or ?-p domain is frequently used for attenuating shallow water multiples. However, besides multiples, deconvolution also attenuates primary events that have a periodicity which is close to that of the water-layer. In this paper, we present a workflow that involves first attenuating short-period water-layer related multiples (WLRMs) ? a process that we term shallow water demultiple (SWD); and then suppressing other longperiod free surface multiples using conventional SRME. SWD is a wavefield-consistent method that first makes use of WLRMs in the data to reconstruct the missing water-bottom primary reflection and then uses the reflection for predicting shallow WLRMs. It is data driven and takes into account the spatial varying nature of subsurface structures. Since the WLRM model predicted by SWD has similar amplitude and phase as the input data, very short matching filters, which are not possible if deconvolution is used, can be utilised in the adaptive subtraction process. We demonstrate, through real-data examples, that our workflow provides an optimal multiple attenuation solution in shallow water environment in comparison with conventional methods such as ?-p deconvolution or SRME alone.

Shallow water demultiple using hybrid multichannel prediction

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