Model-based Water-layer Demultiple

Summary Variable-depth streamer acquisition is becoming a key technique for providing wide bandwidth seismic data Varying the receiver depth creates wide receiver ghost diversity and produces a spectacular increase in the frequency bandwidth However, compared to conventional data, this variable-depth streamer data implies a major challenge in processing: how to deal with various receiver ghosts The ghosts have to be preserved up to the deghosting step Here we present the implication for the following de-multiple methods: Shallow-Water Demultiple, Tau-P deconvolution and Surface-related multiples elimination in deep and shallow water environments

Multiple Attenuation For Variable-depth Streamer Data: From Deep to Shallow Water

Demultiple strategy combining Radon filtering and Radon domain adaptive multiple subtraction

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 pri- maries over tau-p gapped deconvolution. This, in turn, contributes to a more realistic velocity model and higher-quality images. from the auto-correlation, to predict WLRMs. DWD pre- dicts WLRMs with correct amplitudes. However, when the water-bottom is complex (and thus multi-arrivals of WLPRs present) the water-layer model derived from the time-domain

A model-based water-layer demultiple algorithm

Suppression of surface-related and internal multiples is an outstanding challenge in seismic data processing. The former is particularly difficult in shallow water, whereas the latter is problematic for targets buried under complex, highly scattering overburdens. We have developed a two-step, amplitude- and phase-preserving, inversion-based workflow that addresses these problems. We apply robust estimation of primaries by sparse inversion (R-EPSI) to solve simultaneously for the surface-related primaries Green’s function and the source wavelet. A significant advantage of the inversion approach of the R-EPSI method is that it does not rely on an adaptive subtraction step that typically limits other demultiple methods such as surface-related multiple elimination. The resulting Green’s function is used as the input to a Marchenko equation-based approach to predict the complex interference pattern of all overburden-generated internal multiples at once. In this approach, no a priori information about the subsurface is needed. In theory, the interbed multiples can be predicted with correct amplitude and phase and, again, no adaptive filters are required. We illustrate this workflow by applying it on an Arabian Gulf field data example. It is crucial that all preprocessing steps are performed in an amplitude-preserving way to restrict any impact on the accuracy of the multiple prediction. In practice, some minor inaccuracies in the processing flow may end up as prediction errors for which corrections will be needed. Hence, we conclude that the use of conservative adaptive filters were necessary to obtain the best results after interbed multiple removal. The obtained results indicate promising suppression of surface-related and interbed multiples.

/pdf/robust-estimation-of-primaries-by-sparse-inversion-and-2u8l9hmipp.pdf

Robust estimation of primaries by sparse inversion and Marchenko equation-based workflow for multiple suppression in the case of a shallow water layer and a complex overburden: A 2D case study in the Arabian Gulf

Summary Surface related multiple elimination (SRME) method is known to have difficulties in attenuating shallow water multiples Particularly, for water-layer related multiples in very shallow water environment such as seabeach-shallow water areas because the primary water-bottom reflection that is required by SRME for predicting the multiples is not recorded due to near offset gap Thus, in these situations, predictive deconvolution in either x-t or tau-p domain is often used in processing workflow to suppress this kind of multiples However, deconvolution also attenuates primary events that have a periodicity close to that of the waterlayer In this case study, we present a two-step processing workflow for removing free-surface multiples Firstly, we use a multi-channel prediction filter estimated from the multiples for attenuating water-layer related multiples Secondly, we apply SRME for suppressing other surface multiples generated by sub-surfaces underneath the waterbottom We demonstrate that this workflow provides an optimal suppression of free-surface multiples on marine data that have been recorded in the Bohai Sea offshore eastern China

Surface multiple attenuation in seabeach-shallow water, case study on data from the Bohai Sea

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.

https://www.tandfonline.com/doi/pdf/10.1081/22020586.2010.12041899

Qing Long Xia

Papers

Surface multiple attenuation in seabeach-shallow water, case study on data from the Bohai Sea

Shallow Water Demultiple