3D Multi‐source Least‐squares Reverse Time Migration

Dai, Weihui; Boonyasiriwat, Chaiwoot; Schuster, Gerard T.

doi:10.1190/1.3513494

Cited by 40 publications

(17 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another difficulty is that the imaging condition is the correlation between the downgoing and upgoing fields, which can give rise to ringiness in the multiples migration image because the recorded data can be considered as a very ringy wavelet. To overcome these problems, we propose leastsquares reverse time migration of multiples (LSRTMM), which can improve the image quality by removing artifacts, balancing amplitudes, and suppressing crosstalk (Dai et al, 2010(Dai et al, , 2011(Dai et al, , 2012. Moreover, LSRTMM also deconvolves the ringy source wavelet of each virtual geophone by the inherent source deconvolution in LSM.…”

Section: Introductionmentioning

confidence: 98%

Least-squares reverse time migration of multiples

2014

Self Cite

View full text Add to dashboard Cite

The theory of least-squares reverse time migration of multiples (RTMM) is presented. In this method, least squares migration (LSM) is used to image free-surface multiples where the recorded traces are used as the time histories of the virtual sources at the hydrophones and the surface-related multiples are the observed data. For a single source, the entire free-surface becomes an extended virtual source where the downgoing freesurface multiples more fully illuminate the subsurface compared to the primaries. Since each recorded trace is treated as the time history of a virtual source, knowledge of the source wavelet is not required and the ringy time series for each source is automatically deconvolved. If the multiples can be perfectly separated from the primaries, numerical tests on synthetic data for the Sigsbee2B and Marmousi2 models show that leastsquares reverse time migration of multiples (LSRTMM) can significantly improve the image quality compared to RTMM or standard reverse time migration (RTM) of primaries. However, if there is imperfect separation and the multiples are strongly interfering with the primaries then LSRTMM images show no significant advantage over the primary migration images.In some cases, they can be of worse quality. Applying LSRTMM to Gulf of Mexico data shows higher signal-to-noise imaging of the salt bottom and top compared to standard RTM images. This is likely attributed to the fact that the target body is just below the sea bed so that the deep water multiples do not have strong interference with the primaries. Migrating a sparsely sampled version of the Marmousi2 ocean bottom seismic data shows that LSM of primaries and LSRTMM provides significantly better imaging than standard RTM.A potential liability of LSRTMM is that multiples require several round trips between the reflector and the free surface, so that high frequencies in the multiples suffer greater attenuation compared to the primary reflections. This can lead to lower resolution in the migration image compared to that computed from primaries. Another liability is that the multiple migration image is more down-dip limited than the standard primaries migration image. Finally, if the surface-related multiple elimination method is imperfect and there are strong multiples interfering with the primaries, then the resulting LSRTMM image can be significantly degraded. We conclude that LSRTMM can be a useful complement, not a replacement, for RTM of primary reflections.

show abstract

Section: Introductionmentioning

confidence: 98%

Least-squares reverse time migration of multiples

2014

Self Cite

View full text Add to dashboard Cite

show abstract

“…And it was later developed for waveequation migration algorithms by Kuehl and Sachhi (2002). Then, LSM is performed with the Kirchhoff migration operator (Huang et al, 2013a;Liu et al, 2013), one-way equation migration operator (Huang et al, , c, 2013bYang and Zhang, 2008;Kuehl and Sachhi, 2001) and two-way equation migration operator (Li et al, 2015Huang et al, 2014a;Dai and Schuster, 2013;Dai et al, 2012Dai et al, , 2011Dai et al, , 2010.…”

Section: Introductionmentioning

confidence: 98%

Plane-wave least-squares reverse time migration for rugged topography

Huang

Wang

et al. 2015

J. Earth Sci.

View full text Add to dashboard Cite

We present a method based on least-squares reverse time migration with plane-wave encoding (P-LSRTM) for rugged topography. Instead of modifying the wave field before migration, we modify the plane-wave encoding function and fill constant velocity to the area above rugged topography in the model so that P-LSRTM can be directly performed from rugged surface in the way same to shot domain reverse time migration. In order to improve efficiency and reduce I/O (input/output) cost, the dynamic encoding strategy and hybrid encoding strategy are implemented. Numerical test on SEG rugged topography model show that P-LSRTM can suppress migration artifacts in the migration image, and compensate amplitude in the middle-deep part efficiently. Without data correction, P-LSRTM can produce a satisfying image of near-surface if we could get an accurate near-surface velocity model. Moreover, the pre-stack P-LSRTM is more robust than conventional RTM in the presence of migration velocity errors.

show abstract

“…The problems associated with LSRTM can be broadly grouped into two major categories: 1) inadequate physics taken into account by the modeling and the adjoint equations, and 2) the computational cost. For reducing the computational cost, phase-encoded migration (Morton and Ober, 1998;Romero et al, 2000) was proposed that was later extended to multisource LSRTM by Dai et al (2010) and several other authors. A similar approach was proposed by Herrmann and Li (2012) as they used a combination of randomized dimensionality-reduction and divide-andconquer-techniques to decimate the LSM problem as a series of smaller sub-problems where each sub-problem involved iterating on a small randomized subset of the data.…”

Section: Introductionmentioning

confidence: 99%

Least-squares reverse time migration with radon preconditioning

Dutta

Agut

Giboli

et al. 2016

SEG Technical Program Expanded Abstracts 2016

View full text Add to dashboard Cite

SummaryWe present a least-squares reverse time migration (LSRTM) method using Radon preconditioning to regularize noisy or severely undersampled data. A high resolution local radon transform is used as a change of basis for the reflectivity and sparseness constraints are applied to the inverted reflectivity in the transform domain. This reflects the prior that for each location of the subsurface the number of geological dips is limited. The forward and the adjoint mapping of the reflectivity to the local Radon domain and back are done through 3D Fourierbased discrete Radon transform operators. The sparseness is enforced by applying weights to the Radon domain components which either vary with the amplitudes of the local dips or are thresholded at given quantiles. Numerical tests on synthetic and field data validate the effectiveness of the proposed approach in producing images with improved SNR and reduced aliasing artifacts when compared with standard RTM or LSRTM.

show abstract

3D Multi‐source Least‐squares Reverse Time Migration

Cited by 40 publications

References 13 publications

Least-squares reverse time migration of multiples

Least-squares reverse time migration of multiples

Plane-wave least-squares reverse time migration for rugged topography

Least-squares reverse time migration with radon preconditioning

Contact Info

Product

Resources

About