On the Free Surface Assumption for Marine Data Driven Demultiple Methods

Frijlink, Martijn; Borselen, R.G. van; Söllner, Walter

doi:10.3997/2214-4609.201400111

Cited by 6 publications

(6 citation statements)

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“…Throughout this paper S is treated as both the multiple-generating free surface and the observation surface on which x lies. This assumption can be relaxed by careful treatment of the wavefield directionality of p and g based on where we position S in relation to both surfaces (see Frijlink et al, 2011, for more details). The operator r models the reflection of the free surface, and is typically assumed to be isotropic and well approximated by a Dirac delta function δ(x − x ) scaled by a reflectivity coefficient of −1, although this can again possibly be relaxed and estimated (AlMatar, 2010).…”

Section: Theorymentioning

confidence: 99%

“…Surface-related multiple events are among the largest causes of errors in seismic subsurface imaging, and its removal from survey records is an important aspect of seismic processing. It is generally accepted that the most accurate model of surface-related multiples are derived from wave equation-based, data-driven approaches, such as ones exploiting the Rayleigh-Helmholtz reciprocity relationship between the field-measured wavefield data and its multiple-free version (Fokkema and van den Berg, 1993;Frijlink et al, 2011). The most well-known method of this type is what came to be known as Surface-Related Multiple Elimination (SRME, Verschuur, 1992;Berkhout and Verschuur, 1997;Verschuur and Berkhout, 1997).…”

Section: Introductionmentioning

confidence: 99%

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Estimation of primaries by sparse inversion with scattering-based multiple predictions for data with large gaps

Lin

Herrmann

2016

GEOPHYSICS

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We propose to solve the Estimation of Primaries by Sparse Inversion problem without any explicit data reconstruction, from a seismic record with missing near-offsets and large holes in the acquisition grid which does not produce aliasing but otherwise causes large errors in the multiple prediction. Exclusion of the unknown data as an inversion variable from the process is desirable, since it sidesteps possible issues arising from fitting observations that are also unknowns in the same inversion problem. Instead, we simulate their multiple contributions by augmenting the forward prediction model for the total wavefield with a scattering series that mimics the action of the free surface reflector within the confines of the unobserved trace locations. Each term in this scattering series simply involves convolution of the predicted wavefield once more with the current estimated surface-free Green's function at these unobserved locations. We investigate the necessary modifications to the primary estimation algorithm to account for the resulting nonlinearity in the modeling operator, and also demonstrate that just a few scattering terms are enough to satisfactorily mitigate the effects of near-offset data gaps during the inversion process. Numerical experiments on synthetic data show that the final derived method can significantly outperform explicit data reconstruction for large near-offset gaps. This is achieved with a similar computational cost and better memory efficiency compared to explicit data reconstruction. We also show on real data that our scheme outperforms pre-interpolation of the near-offset gap.

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Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of primaries by sparse inversion with scattering-based multiple predictions for data with large gaps

Lin

Herrmann

2016

GEOPHYSICS

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“…The application of equation (25) will be illustrated with Synthetic Elastodynamic Example 2. Interesting to mention is the similarity between equation (25) and deghosting procedures (e.g., Fokkema and Van den Berg 1993;Frijlink et al 2011). However, in equation (25), the deghosting procedure is expressed more generally, holding for all kinds of fields.…”

Section: U N I F I E D T H E O R Y O F M U L T I -D E P T H -L E V E mentioning

confidence: 99%

“…Separation of recorded fields into downgoing and upgoing constituents is a technique that is used in many geophysical methods. Decomposed fields form the basis for various surface-related multiple elimination and deghosting procedures (e.g., Frijlink, Van Borselen, and Söellner 2011;Majdanski et al 2011) and for depth imaging using primary and multiple reflections (e.g., Muijs, Robertsson, and Holliger 2007). Novel methodologies that make use of horizontal downhole sensors, such as the virtual source method (e.g., Bakulin and Calvert 2006;Mehta et al 2007; and multi-dimensional deconvolution Wapenaar et al 2011, rely on decomposing the seismic field at depth.…”

Section: Introductionmentioning

confidence: 99%

Unified multi‐depth‐level field decomposition

Grobbe

Neut

Slob

et al. 2015

Geophysical Prospecting

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A B S T R A C TWavefield decomposition forms an important ingredient of various geophysical methods. An example of wavefield decomposition is the decomposition into upgoing and downgoing wavefields and simultaneous decomposition into different wave/field types. The multi-component field decomposition scheme makes use of the recordings of different field quantities (such as particle velocity and pressure). In practice, different recordings can be obscured by different sensor characteristics, requiring calibration with an unknown calibration factor. Not all field quantities required for multi-component field decomposition might be available, or they can suffer from different noise levels. The multi-depth-level decomposition approach makes use of field quantities recorded at multiple depth levels, e.g., two horizontal boreholes closely separated from each other, a combination of a single receiver array combined with free-surface boundary conditions, or acquisition geometries with a high-density of vertical boreholes. We theoretically describe the multi-depth-level decomposition approach in a unified form, showing that it can be applied to different kinds of fields in dissipative, inhomogeneous, anisotropic media, e.g., acoustic, electromagnetic, elastodynamic, poroelastic, and seismoelectric fields. We express the one-way fields at one depth level in terms of the observed fields at multiple depth levels, using extrapolation operators that are dependent on the medium parameters between the two depth levels. Lateral invariance at the depth level of decomposition allows us to carry out the multi-depth-level decomposition in the horizontal wavenumber-frequency domain. We illustrate the multi-depth-level decomposition scheme using two synthetic elastodynamic examples. The first example uses particle velocity recordings at two depth levels, whereas the second example combines recordings at one depth level with the Dirichlet free-surface boundary condition of zero traction. Comparison with multicomponent decomposed fields shows a perfect match in both amplitude and phase for both cases. The multi-depth-level decomposition scheme is fully customizable to the desired acquisition geometry. The decomposition problem is in principle an inverse problem. Notches may occur at certain frequencies, causing the multi-depth-level composition matrix to become uninvertible, requiring additional notch filters. We can add multi-depth-level free-surface boundary conditions as extra equations to the multi-component composition matrix, thereby overdetermining this inverse problem. The combined multi-component-multi-depth-level decomposition on a land data set clearly shows improvements in the decomposition results, compared with the performance of the multi-component decomposition scheme. *

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“…Alternatively, multiple elimination can be handled as an inverse problem. It has been shown that multidimensional deconvolution (MDD) of the upgoing wavefield with the downgoing wavefield at the receiver level yields a reflection response without surface‐related multiples (Amundsen 2001; Frijlink et al 2011). This method has been applied to remove multiples from ocean bottom cable (OBC) data by various authors (Holvik & Amundsen 2005; Wang et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Data matching for free-surface multiple attenuation by multidimensional deconvolution

Neut¹,

Frijlink²,

Borselen³

2012

Geophysical Journal International

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SUMMARY A common strategy for surface‐related multiple elimination of seismic data is to predict multiples by a convolutional model and subtract these adaptively from the input gathers. Problems can be posed by interfering multiples and primaries. Removing multiples by multidimensional deconvolution (MDD) (inversion) does not suffer from these problems. However, this approach requires data to be consistent, which is often not the case, especially not at interpolated near‐offsets. A novel method is proposed to improve data consistency prior to inversion. This is done by backpropagating first‐order multiples with a time‐gated reference primary event and matching these with early primaries in the input gather. After data matching, multiple elimination by MDD can be applied with a deterministic inversion scheme.

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On the Free Surface Assumption for Marine Data Driven Demultiple Methods

Cited by 6 publications

References 0 publications

Estimation of primaries by sparse inversion with scattering-based multiple predictions for data with large gaps

Estimation of primaries by sparse inversion with scattering-based multiple predictions for data with large gaps

Unified multi‐depth‐level field decomposition

Data matching for free-surface multiple attenuation by multidimensional deconvolution

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