Water‐bottom multiple attenuation by Kirchhoff extrapolation

Spadavecchia, Emmanuel; Lipari, Vincenzo; Bienati, N.; Drufuca, G.

doi:10.1111/j.1365-2478.2012.01131.x

Cited by 5 publications

(7 citation statements)

References 23 publications

(22 reference statements)

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“…Surface‐related multiples are predicted via KWE in two extrapolation steps (Spadavecchia et al . ). KWE first downward propagates the recorded data from the acquisition surface to the reflector where the multiple to be predicted made the last upward bounce.…”

Section: Kirchhoff Wavefield Extrapolationmentioning

confidence: 97%

“…In our implementation (Spadavecchia et al . ) the KWE operator

\overset{G}{true}

can be approximated by means of rays of minimal traveltime, allowing surface multiples prediction in a single datuming step:

m (x_{k}, ω) = \sum_{i} {\tilde{w}}_{i k} {normale}^{j ω {\tilde{τ}}_{i k}} d ({italicx}_{i}, ω),

…”

Section: Kirchhoff Wavefield Extrapolationmentioning

confidence: 99%

“…By doing this expansion the internal multiple prediction operator becomes the cascade of two extrapolation steps involving the fast KWE operator

\overset{G}{true}

described in equation , which is the same operator used for the prediction of surface multiples (Spadavecchia et al . ):

\hat{boldm} = {({boldG}_{02})}^{T} {boldG}_{02} {({boldG}_{01}^{T} {boldG}_{01})}^{- T} {boldp}_{bold2} = {\overset{G}{true}}_{02} {({\overset{G}{true}}_{01})}^{- T} {boldp}_{bold2} .

…”

Section: Compact Prediction Operatormentioning

confidence: 99%

“…Spadavecchia et al . () used a Kirchhoff wavefield extrapolation (KWE) operator for the prediction of surface multiples. The KWE operator predicts the multiples generated by an interpreted reflector by adding a round trip through the subsurface to the recorded data.…”

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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Section: Kirchhoff Wavefield Extrapolationmentioning

confidence: 97%

“…In our implementation (Spadavecchia et al . ) the KWE operator

\overset{G}{true}

can be approximated by means of rays of minimal traveltime, allowing surface multiples prediction in a single datuming step:

m (x_{k}, ω) = \sum_{i} {\tilde{w}}_{i k} {normale}^{j ω {\tilde{τ}}_{i k}} d ({italicx}_{i}, ω),

…”

Section: Kirchhoff Wavefield Extrapolationmentioning

confidence: 99%

“…By doing this expansion the internal multiple prediction operator becomes the cascade of two extrapolation steps involving the fast KWE operator

\overset{G}{true}

described in equation , which is the same operator used for the prediction of surface multiples (Spadavecchia et al . ):

\hat{boldm} = {({boldG}_{02})}^{T} {boldG}_{02} {({boldG}_{01}^{T} {boldG}_{01})}^{- T} {boldp}_{bold2} = {\overset{G}{true}}_{02} {({\overset{G}{true}}_{01})}^{- T} {boldp}_{bold2} .

…”

Section: Compact Prediction Operatormentioning

confidence: 99%

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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“…The target reservoir is still affected by many multiples events both peg-leg and intra-bed, that make the interpretation ambiguous. To predict peg-leg multiples, a tool based on Kirchhoff Extrapolation [4] was used ( Figure 2). This algorithm predicts multiples that made the last upward bounce at the sea bottom.…”

Section: Geological Summarymentioning

confidence: 99%

An Application of Depth Imaging to Support the Appraisal Phase of a Reservoir Affected by Poor Signal to Noise Ratio

et al. 2013

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In this paper a workflow that integrates the most advanced tools for seismic data processing and imaging to overcome limits imposed by poor seismic data in the definition of reservoir geometries with the details needed to properly locate the appraisal wells is presented. The area of study is the north-western coast of Australia. Here the reservoir, Middle Jurassic sandstones, is affected by a very poor signal to noise ratio due to poor energy penetration and illumination associated with reefs, faults and a high level of multiples content that completely masks the target area. In the following, we will describe our approach to depth imaging with robust velocity analysis, effective attenuation of multiples, choise of most suitable migration algorithm and constant interaction between geological and geophysical expertise. The application of this integrated approach was the added value of the project and the main driver for guiding the interpretation of the reservoir, providing a strong support for well planning.

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Shallow water multiple attenuation through the reconstruction of Green's impulse response

Liu,

Zhou,

2024

Geophysical Prospecting

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Removal of water‐layer‐related multiples in shallow water remains a challenge due to a lack of near‐offset data. We have developed a shallow water multiple attenuation approach by constructing the impulse response of the water bottom for the removal of water‐layer‐related multiples. The impulse response of the seafloor is formed by the convolution of its traveltime response and amplitude response. It uses the superposition of the original shallow water demultiple operator to obtain traveltime information and uses the deconvolution method to estimate its dynamic components from the original shallow water demultiple operator to build new operators. The impulse response can be correctly constructed not only for near offsets but for moderate and far offsets as well. A three‐term subtraction strategy by predicting source‐side, receiver‐side and both source‐side and receiver‐side multiples is adopted in our approach for attenuating shallow multiples without over‐prediction. Numerical examples with two synthetic datasets and 2D and 3D field datasets demonstrate that our approach gives a desirable performance.

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Water‐bottom multiple attenuation by Kirchhoff extrapolation

Cited by 5 publications

References 23 publications

Internal multiple attenuation by Kirchhoff extrapolation

Internal multiple attenuation by Kirchhoff extrapolation

An Application of Depth Imaging to Support the Appraisal Phase of a Reservoir Affected by Poor Signal to Noise Ratio

Shallow water multiple attenuation through the reconstruction of Green's impulse response

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