Model-based redatuming of seismic data: An inverse-filter approach

Planès, Thomas; Snieder, Roel; Singh, Satyan

doi:10.1190/geo2017-0475.1

Cited by 3 publications

(4 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The goal is to recover g from (noisy) samples p i jk := p(t i , r j , s k ) + i jk and q i jk := q(t i , r j , s k ) + δ i jk , with i jk and δ i jk representing the noise terms. For more details regarding the derivation of this relation we refer to [25,35,37]. A concrete example illustrating the ill-posedness of the problem is given below.…”

Section: Source Redatumingmentioning

confidence: 99%

See 1 more Smart Citation

Seismic wavefield redatuming with regularized multi-dimensional deconvolution

Luiken

Leeuwen

2020

Inverse Problems

View full text Add to dashboard Cite

In seismic imaging the aim is to obtain an image of the subsurface using reflection data. The reflection data are generated using sound waves and the sources and receivers are placed at the surface. The target zone, for example an oil or gas reservoir, lies relatively deep in the subsurface below several layers. The area above the target zone is called the overburden. This overburden will have an imprint on the image. Wavefield redatuming is an approach that removes the imprint of the overburden on the image by creating so-called virtual sources and receivers above the target zone. The virtual sources are obtained by determining the impulse response, or Green's function, in the subsurface. The impulse response is obtained by deconvolving all upand downgoing wavefields at the desired location. In this paper, we pose this deconvolution problem as a constrained least-squares problem. We describe the constraints that are involved in the deconvolution and show that they are associated with orthogonal projection operators. We show different optimization strategies to solve the constrained least-squares problem and provide an explicit relation between them, showing that they are in a sense equivalent. We show that the constrained least-squares problem remains ill-posed and that additional regularization has to be provided. We show that Tikhonov regularization leads to improved resolution and a stable optimization procedure, but that we cannot estimate the correct regularization parameter using standard parameter selection methods. We also show that the constrained least-squares can be posed in such a way that additional nonlinear regularization is possible.

show abstract

Section: Source Redatumingmentioning

confidence: 99%

“…For an extensive overview of the redatuming problem we refer to [2,26,36,34,29,28,30,24]. A key ingredient in all redatuming methods is multi-dimensional deconvolution.…”

Section: Introductionmentioning

confidence: 99%

Seismic wavefield redatuming with regularized multi-dimensional deconvolution

Luiken

Leeuwen

2020

Inverse Problems

View full text Add to dashboard Cite

show abstract

“…The goal is to recover g from (noisy) samples p ijk := p(t i , r j , s k ) + ijk and q ijk := q(t i , r j , s k ) + δ ijk , with ijk and δ ijk representing the noise terms. For more details regarding the derivation of this relation we refer to [124,126,98]. A concrete example illustrating the ill-posedness of the problem is given below.…”

Section: Source Redatumingmentioning

confidence: 99%

“…For an extensive overview of the redatuming problem we refer to [12,104,124,126,114,113,112,98]. Imaging methods based on redatuming are, for example, Marchenko imaging [125] and JMI-res [36].…”

Section: Introductionmentioning

confidence: 99%

Regularization of linear inverse problems

Luiken¹

View full text Add to dashboard Cite

We address the estimation of the impulse response at a reflector by deconvolving the up-and downgoing waves. Deconvolution in the time domain can be written as a linear system with multiple right-hand sides in the frequency domain. A straightforward way of solving these systems is by applying an iterative method like LSQR. However, these solvers are dependent on the righthand side and for every right-hand side we have to use LSQR again. This can be a costly process. We propose to solve the linear systems using block Krylov methods. We show that these methods give comparable accuracy compared to standard Krylov methods, but at a much lower computational cost. This is due to the fact that block methods are able to exploit similarities in the data and are able to constructs solutions in a much richer subspace. We also show that it is hard to solve the MDD problem in the frequency domain alone, and that additional optimization in the time domain is most likely required.This chapter is partially based on the following publication: N.A. Luiken and A. Garg. Block-Krylov methods for multi-dimensional deconvolution.

show abstract

Solving an inverse acousto-elastic scattering problems by combining full-waveform redatuming and time reversal

Assous

Lin

2021

Journal of Computational Physics

View full text Add to dashboard Cite

Model-based redatuming of seismic data: An inverse-filter approach

Cited by 3 publications

References 32 publications

Seismic wavefield redatuming with regularized multi-dimensional deconvolution

Seismic wavefield redatuming with regularized multi-dimensional deconvolution

Regularization of linear inverse problems

Solving an inverse acousto-elastic scattering problems by combining full-waveform redatuming and time reversal

Contact Info

Product

Resources

About