3D surface‐related multiple prediction, an inversion approach

Dedem, E. J. van; Verschuur, D.J.

doi:10.1190/1.1815821

Cited by 16 publications

(9 citation statements)

References 4 publications

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“…For SRME, predicted multiples often include source signatures and directivity patterns that differ from those present in the data ͑see, e.g., Verschuur et al, 1992;Ikelle et al, 1997͒. Moreover, 2D SRME produces errors in the predicted multiples because of 3D complexity of the earth ͑Dragoset and Jeričević, 1998; Ross et al, 1999;Verschuur, 2006͒, whereas recently developed full 3D-SRME algorithms can suffer from imperfections related to incomplete acquisitions ͑see, e.g., Lin et al, 2004;Moore and Dragoset, 2004;van Borselen et al, 2004;van Dedem and Verschuur, 2005͒, including erroneous reconstructions of missing near offsets ͑Dragoset and Jeričević, 1998͒. For field data, these factors preclude iterative SRME, resulting in amplitude errors that vary for different multiple orders ͑see, e.g., Verschuur and Berkhout, 1997;Paffenholz et al, 2002͒. In practice, the second separation stage appears to be particularly challenging because adaptive ᐉ 2 -matched-filtering techniques are known to lead to residual multiple energy, high-frequency clutter, and deterioration of the primaries ͑Chen et al, 2004;Abma et al, 2005;Herrmann et al, 2007a͒.…”

Section: Introductionmentioning

confidence: 99%

Adaptive curvelet-domain primary-multiple separation

2008

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In many exploration areas, successful separation of primaries and multiples greatly determines the quality of seismic imaging. Despite major advances made by surface-related multiple elimination ͑SRME͒, amplitude errors in the predicted multiples remain a problem. When these errors vary for each type of multiple in different ways ͑as a function of offset, time, and dip͒, they pose a serious challenge for conventional least-squares matching and for the recently introduced separation by curvelet-domain thresholding. We propose a data-adaptive method that corrects amplitude errors, which vary smoothly as a function of location, scale ͑fre-quency band͒, and angle. With this method, the amplitudes can be corrected by an elementwise curvelet-domain scaling of the predicted multiples. We show that this scaling leads to successful estimation of primaries, despite amplitude, sign, timing, and phase errors in the predicted multiples. Our results on synthetic and real data show distinct improvements over conventional least-squares matching in terms of better suppression of multiple energy and high-frequency clutter and better recovery of estimated primaries.

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Section: Introductionmentioning

confidence: 99%

Adaptive curvelet-domain primary-multiple separation

2008

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“…However, it is worth noting that for seismic processing tasks several approaches exist that use a specific operator and its adjoint, particularly for data interpolation (Berkhout & Verschuur, 2006;Trad et al, 2002;van Groenestijn & Verschuur, 2009), for multiple prediction (Pica et al, 2005;van Dedem & Verschuur, 2005) and for signal/noise separation (Nemeth et al, 2000). Moreover, we refer to Fomel (2003a) for other applications (stacking, redatuming, offset continuation), for which a technique is proposed to obtain a unitary modeling operator in the context of high frequency approximation.…”

Section: Discussionmentioning

confidence: 99%

Coupling Modeling and Migration for Seismic Imaging

Chauris¹,

Donno²

2012

Seismic Waves - Research and Analysis

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“…Both apex-shifted hyperbolic representations (van Dedem and Verschuur 2000, Trad, 2002, 2003 and parabolic representations (Hargreaves et al 2003) have been explored in previous work. In general we can represent these by the following equation:…”

Section: Proposed Solution -Extension Of the Radon Modelmentioning

confidence: 98%

Multiple Attenuation Using an Apex Shift Radon Transform

et al. 2004

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Multiples from sea-floor scatterers and peg-leg multiples in complex geology are often resistant to conventional multiple removal techniques such as Radon demultiple. They have a complicated moveout behaviour in prestack gathers which can only be approximately represented by a conventional parabolic or hyperbolic Radon decomposition. Such multiples split into pairs of events, one for each of the shot or receiver side of the multiple. They are approximately parabolic after NMO correction with primary velocities but have their minimum travel times shifted to either side of zero-offset.It is possible to extend the Radon multiple model by including apex-shifted parabolas or hyperbolas in the model space. In the first case we call this an "α-Radon transform" since the apex shift is indicated by α in our notation. In the second approach we call it Stolt-Radon because we use the Stolt migration operator. In order to compute the transform it is then necessary to perform a large and somewhat costly constrained inversion. Nonetheless both techniques have the potential to attenuate multiple diffractions and other similarly complicated multiple events in complex geology.

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3D surface‐related multiple prediction, an inversion approach

Cited by 16 publications

References 4 publications

Adaptive curvelet-domain primary-multiple separation

Adaptive curvelet-domain primary-multiple separation

Coupling Modeling and Migration for Seismic Imaging

Multiple Attenuation Using an Apex Shift Radon Transform

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