2‐D deconvolution imaging condition for shot‐profile migration

Valenciano, A.; Biondi, Biondo

doi:10.1190/1.1817454

Cited by 74 publications

(42 citation statements)

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“…The two images are similar, except for a small difference in frequency content caused by the fact that I did not enter a perfect impulsive source in the shot-profile migration to avoid dispersion. Not only the flat reflector is imaged similarly in the two images, but also the strong "ghost" reflectors caused by the triplication of the wavepath (Valenciano and Biondi, 2002), visible between the surface locations of 0 and 1 km, are almost identical. Figure 3 shows the subsurface offset-domain common image gathers at the surface location of 300 meters: panel a) shot-profile migration, panel b) source-receiver migration).…”

Section: Tests On Synthetic Data Setmentioning

confidence: 58%

Equivalence of source‐receiver migration and shot‐profile migration

Biondi

2003

GEOPHYSICS

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Section: Tests On Synthetic Data Setmentioning

confidence: 58%

Equivalence of source‐receiver migration and shot‐profile migration

Biondi

2003

GEOPHYSICS

Self Cite

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“…Current research focuses on four main issues: wavefield extrapolation (Stork, 2013;Zhang and Yao, 2013), alternative imaging conditions (Valenciano and Biondi, 2003;Zhang and Sun, 2008;Liu et al, 2011), amplitude preservation (Zhang et al, 2007a;Zhang and Sun, 2008), and how to efficiently generate common image gathers (CIGs) (Xu et al, 2011). We will consider each of these issues in turn.…”

Section: Reverse-time Migrationmentioning

confidence: 99%

Least-Squares Reverse-Time Migration

Yao

Jakubowicz

2012

SEG Technical Program Expanded Abstracts 2012

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Conventional migration methods, including reverse-time migration (RTM) have two weaknesses: first, they use the adjoint of forward-modelling operators, and second, they usually apply a crosscorrelation imaging condition to extract images from reconstructed wavefields. Adjoint operators, which are an approximation to inverse operators, can only correctly calculate traveltimes (phase), but not amplitudes. To preserve the true amplitudes of migration images, it is necessary to apply the inverse of the forward-modelling operator. Similarly, crosscorrelation imaging conditions also only correct traveltimes (phase) but do not preserve amplitudes. Besides, the examples show crosscorrelation imaging conditions produce strong sidelobes. Least-squares migration (LSM) uses both inverse operators and deconvolution imaging conditions. As a result, LSM resolves both problems in conventional migration methods and produces images with fewer artefacts, higher resolution and more accurate amplitudes. At the same time, RTM can accurately handle all dips, frequencies and any type of velocity variation. Combining RTM and LSM produces least-squares reverse-time migration (LSRTM), which in turn has all the advantages of RTM and LSM.In this thesis, we implement two types of LSRTM: matrix-based LSRTM (MLSRTM) and non-linear LSRTM (NLLSRTM). MLSRTM is a matrix formulation of LSRTM and is more stable than conventional LSRTM; it can be implemented with linear inversion algorithms but needs a large amount of computer memory. NLLSRTM, by contrast, directly expresses migration as an optimisation which minimises the ‫ܮ‬ 2 norm of the residual between the predicted and observed data. NLLSRTM can be implemented using non-linear gradient inversion algorithms, such as non-linear steepest descent and non-linear conjugated-gradient solvers.We demonstrate that both MLSRTM and NLLSRTM can achieve better images with fewer artefacts, higher resolution and more accurate amplitudes than RTM using three synthetic examples. The power of LSRTM is also further illustrated using a field dataset. Finally, a simple synthetic test demonstrates that the objective function used in LSRTM is sensitive to errors in the migration velocity.As a result, it may be possible to use NLLSRTM to both refine the migrated image and estimate the migration velocity.

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“…Using a similar description of wave propagation, the common-focus-point method predicts and subsequently removes internal multiples (Berkhout and Verschuur, 2005). Imaging condition approaches to remove the effects of such multiples include using the Poynting vector (Richardson and Malcolm, 2014), deconvolution (Valenciano and Biondi, 2003), and local slopes (Sava, 2007). Postimaging approaches include filtering common-image gathers (Biondi and Shan, 2002) or filtering the final image (Youn and Zhou, 2001;Guitton et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Attenuating multiple-related imaging artifacts using combined imaging conditions

Filho

Curtis

2016

GEOPHYSICS

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The objective of prestack depth migration is to position reflectors at their correct subsurface locations. However, migration methods often also generate artifacts along with physical reflectors, which hamper interpretation. These spurious reflectors often appear at different spatial locations in the image depending on which migration method is used. Therefore, we have devised a postimaging filter that combines two imaging conditions to preserve their similarities and to attenuate their differences. The imaging filter is based on combining the two constituent images and their envelopes that were obtained from the complex vertical traces of the images. We have used the method to combine two images resulting from different migration schemes, which produce dissimilar artifacts: a conventional migration method (equivalent to reverse time migration) and a deconvolution-based imaging method. We show how this combination may be exploited to attenuate migration artifacts in a final image. A synthetic model containing a syncline and stochastically generated small-scale heterogeneities in the velocity and density distributions was used for the numerical example. We compared the images in detail at two locations where spurious events arose and also at a true reflector. We found that the combined imaging condition has significantly fewer artifacts than either constituent image individually.

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2‐D deconvolution imaging condition for shot‐profile migration

Cited by 74 publications

References 1 publication

Equivalence of source‐receiver migration and shot‐profile migration

Equivalence of source‐receiver migration and shot‐profile migration

Least-Squares Reverse-Time Migration

Attenuating multiple-related imaging artifacts using combined imaging conditions

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