3D surface‐related multiple modeling, principles and results

Pica, A.; Poulain, G.; David, B.; Magesan, M.; Baldock, S.; Davin, Chris; Hugonnet, P.; Herrmann, P.

doi:10.1190/1.2148121

Cited by 35 publications

(9 citation statements)

References 11 publications

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“…We propose an alternative methodology based on wavefield extrapolation (Wiggins ; Pica et al . ; Pica and Delmas ). Spadavecchia et al .…”

Section: Introductionmentioning

confidence: 98%

“…It requires neither velocity models nor interpreted horizons but it is even more expensive compared to data-driven IME. We propose an alternative methodology based on wavefield extrapolation (Wiggins 1988;Pica et al 2005;Pica and Delmas 2008). Spadavecchia et al (2011) used a Kirchhoff wavefield extrapolation (KWE) operator for the prediction of surface multiples.…”

Section: Introductionmentioning

confidence: 99%

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Internal multiple attenuation by Kirchhoff extrapolation

Lipari

Fortini

Spadavecchia

et al. 2014

Geophysical Prospecting

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Surface‐related multiple elimination is the leading methodology for surface multiple removal. This data‐driven approach can be extended to interbed multiple prediction at the expense of a huge increase of the computational burden. This cost makes model‐driven methods still attractive, especially for the three dimensional case. In this paper we present a methodology that extends Kirchhoff wavefield extrapolation to interbed multiple prediction. In Kirchhoff wavefield extrapolation for surface multiple prediction a single round trip to an interpreted reflector is added to the recorded data. Here we show that interbed multiples generated between two interpreted reflectors can be predicted by applying the Kirchhoff wavefield extrapolation operator twice. In the first extrapolation step Kirchhoff wavefield extrapolation propagates the data backward in time to simulate a round trip to the shallower reflector. In the second extrapolation step Kirchhoff wavefield extrapolation propagates the data forward in time to simulate a round trip to the deeper reflector. In the Kirchhoff extrapolation kernel we use asymptotic Green's functions. The prediction of multiples via Kirchhoff wavefield extrapolation is possibly sped up by computing the required traveltimes via a shifted hyperbola approximation. The effectiveness of the method is demonstrated by results on both synthetic and field data sets.

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“…We propose an alternative methodology based on wavefield extrapolation (Wiggins ; Pica et al . ; Pica and Delmas ). Spadavecchia et al .…”

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Internal multiple attenuation by Kirchhoff extrapolation

Lipari

Fortini

Spadavecchia

et al. 2014

Geophysical Prospecting

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show abstract

“…This affects the multiple removal. Solutions derived from modelbased method (Pica et al, 2005) have recently been proposed for handling reverberations due to the shallow water layer. For instance, Wang et al (2011) designed a model-based waterlayer demultiple (MWD) method that predicts multiples from a shallow seafloor, overcoming the above-mentioned issue faced by SWD; but at the expense of requiring accurate information of the multiple generators.…”

Section: Introductionmentioning

confidence: 99%

Shallow water demultiple using hybrid multichannel prediction

Yang¹,

Hung²

2013

ASEG Extended Abstracts

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INTRODUCTIONIn a previous paper, we presented a fully data driven method, Shallow Water Demultiple (SWD), based on the concept of multichannel prediction for attenuating surface related short period multiples in shallow water (Hung et al., 2010b). The described method is robust, but it may have problems with the first few orders of multiples generated by the seafloor (and any events just beneath the seafloor) when the seafloor is very shallow and hence its reflections are indistinct or in the postcritical angle range. In this case, the model of Water Layer Related Multiples (WLRMs) obtained from the convolution of multichannel prediction operators with seismic data is dominated by low frequencies, primarily due to the near-offset extrapolation required for the convolution process. This affects the multiple removal. Solutions derived from modelbased method (Pica et al., 2005) have recently been proposed for handling reverberations due to the shallow water layer. For instance, Wang et al. (2011) designed a model-based waterlayer demultiple (MWD) method that predicts multiples from a shallow seafloor, overcoming the above-mentioned issue faced by SWD; but at the expense of requiring accurate information of the multiple generators. For practical reasons, we normally only pick the seafloor, among all the shallow multiple generators, for the application of MWD. For very shallow water environment, however, even the seafloor may not be readily available.In this paper, we present a hybrid approach combining the strengths of SWD and model-based methods. We show that the multichannel prediction operator estimated by SWD can be used as an accurate kinematic representation of the seafloor reflection with a higher signal-to-noise (S/N) ratio, allowing the travel time of the seafloor to be automatically estimated. With this information the Green's functions of water-layer primary reflections can be modelled and used for multiple prediction and removal. We first present the theory of the method and then demonstrate its effectiveness on synthetics and field data acquired offshore Australia. METHOD AND RESULTSThe DELPHI feedback model (Verschuur et al., 2005) forms the basis of surface related multiple elimination (SRME). In practice, the prediction is usually the first step of SRME, and in the form of (1) where M is multiple model, ΔP is primary response, P is acquired seismic data, and w is source wavelet. By formulating this in another way, Biersteker (2001) describes it as a multichannel prediction problem:where F is the time and offset truncated multichannel prediction operator, which can be estimated by minimizing the prediction error between seismic data P and multiple M.Comparing with the SRME formulation in Equation (1), the operator F is equivalent to a scaled version of the primaries (Hargreaves, 2006), we can make use of this property in performing MWD, but instead of requiring a known waterlayer model, we propose to derive the Green's function of the water-layer reflections by utilizing the travel time information of...

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“…Recently, deriving from model based methods (Pica et al, 2005) some solutions have been proposed for handling reverberations due to shallow water. For instance, Wang et al (2011) designed a model based method that predicts the multiples due to shallow seafloor.…”

Section: Introductionmentioning

confidence: 99%

Shallow water demultiple with seafloor reflection modeling using multichannel prediction operator

Yang

Hung

2012

SEG Technical Program Expanded Abstracts 2012

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This paper presents an extension of our previous effort on multiple attenuation in shallow water environment. While our previous workflow, termed 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. Our new method is a hybrid approach in which shallow WLRMs are handled by using the Green's function of the seafloor primary reflections, while the rest of the multiples are handled by SWD. The Green's function in this case is derived from auto-picking the traveltime of the multichannel prediction operator estimated from SWD. We show the application of the method for attenuating shallow water multiples on field data examples from offshore Australia.

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3D surface‐related multiple modeling, principles and results

Cited by 35 publications

References 11 publications

Internal multiple attenuation by Kirchhoff extrapolation

Internal multiple attenuation by Kirchhoff extrapolation

Shallow water demultiple using hybrid multichannel prediction

Shallow water demultiple with seafloor reflection modeling using multichannel prediction operator

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