Comparison of elastic and acoustic reverse‐time migration on the synthetic elastic Marmousi‐II OBC dataset

Lu, Ruqian; Traynin, Peter; Anderson, John E.

doi:10.1190/1.3255430

Cited by 18 publications

(7 citation statements)

References 12 publications

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“…In our previous synthetic example, we used a P-wave source, which is common in elastic migration or inversion of marine (ocean-bottom) data, including synthetic data (Lu et al, 2009;Guasch et al, 2012;Raknes and Arntsen, 2014) and field data (Sears et al, 2008(Sears et al, , 2010Jiao et al, 2012). The recorded wavefield thus contains only PP-and converted PS-reflections.…”

Section: Source-type Testmentioning

confidence: 99%

“…In this case, an imaging condition is used such as computing the ray-based excitation time of the reflection at the reflector (Chang and McMechan, 1987) or calculating the zero-lag crosscorrelation of the vector and scalar potentials (Yan and Sava, 2008;Du et al, 2012;Duan and Sava, 2015) to get the migration images. The benefit of elastic RTM is that it uses the correct kinematics to handle multicomponent data and migrates different wave modes to their correct subsurface positions (Lu et al, 2009;Jiao et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Elastic least-squares reverse time migration

Feng

Schuster

2016

SEG Technical Program Expanded Abstracts 2016

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We use elastic least-squares reverse time migration (LSRTM) to invert for the reflectivity images of P-and S-wave impedances. Elastic LSRTM solves the linearized elastic-wave equations for forward modeling and the adjoint equations for backpropagating the residual wavefield at each iteration. Numerical tests on synthetic data and field data reveal the advantages of elastic LSRTM over elastic reverse time migration (RTM) and acoustic LSRTM. For our examples, the elastic LSRTM images have better resolution and amplitude balancing, fewer artifacts, and less crosstalk compared with the elastic RTM images. The images are also better focused and have better reflector continuity for steeply dipping events compared to the acoustic LSRTM images. Similar to conventional leastsquares migration, elastic LSRTM also requires an accurate estimation of the P-and S-wave migration velocity models. However, the problem remains that, when there are moderate errors in the velocity model and strong multiples, LSRTM will produce migration noise stronger than that seen in the RTM images.

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Section: Source-type Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Elastic least-squares reverse time migration

Feng

Schuster

2016

SEG Technical Program Expanded Abstracts 2016

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“…Even if anisotropy and elasticity are assumed in wavefield migration, such limitations still exist. Consequently, this type of migration often leads to images with poor resolution and unbalanced illumination due to such practical acquisition constraints, even though image amplitudes are more reliable when compared with acoustic and/or isotropic imaging (Lu et al, 2009;Phadke and Dhubia, 2012;Du et al, 2014;Hobro et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Elastic least-squares reverse time migration using the energy norm

Rocha

Sava

2017

SEG Technical Program Expanded Abstracts 2017

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Incorporating anisotropy and elasticity into least-squares migration is an important step toward recovering accurate amplitudes in seismic imaging. An efficient way to extract reflectivity information from anisotropic elastic wavefields exploits properties of the energy norm. We derive linearized modeling and migration operators based on the energy norm to perform anisotropic least-squares reverse time migration (LSRTM) describing subsurface reflectivity and correctly predicting observed data without costly decomposition of wave modes. Imaging operators based on the energy norm have no polarity reversal at normal incidence and remove backscattering artifacts caused by sharp interfaces in the earth model, thus accelerating convergence and generating images of higher quality when compared with images produced by conventional methods. With synthetic and field data experiments, we find that our elastic LSRTM method generates high-quality images that predict the data for arbitrary anisotropy, without the complexity of wave-mode decomposition and with a high convergence rate.

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“…Elastic reverse-time migration (ERTM) (Chang and McMechan, 1987;Yan and Sava, 2008) is a promising tool to achieve high resolution imaging in complex geological settings (Lu et al, 2009): it is based on the full elastic wave equation and, in principle, does not require any single-scattering approximation. A central component of ERTM is receiver-side wavefield back-extrapolation in which the scattered wavefield between a physical source and any image point in the subsurface is estimated by projecting the data acquired by receivers located near or at the Earth's surface down into the subsurface.…”

Section: Introductionmentioning

confidence: 99%

Exact Wavefield Extrapolation for Elastic Reverse-time Migration

Ravasi¹,

Curtis²

2013

London 2013, 75th Eage Conference en Exhibition Incorporating SPE Europec

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SUMMARYA fundamental step of any wave equation migration algorithm is represented by the numerical projection of the recorded data down into the subsurface where reflections occur. The geophysical community refers to this concept as wavefield extrapolation and the extrapolated wavefield is called the receiver wavefield.In elastic reverse-time migration, standard wavefield extrapolation only uses partial information contained in elastic waves, and artificial wave energy is extrapolated as a consequence, polluting seismic images.We propose an exact extrapolation formula, which fully employs the velocity-stress nature of the elastic wavefield to create an improved estimate of the extrapolated receiver wavefield. A synthetic example illustrates the resulting improvements in imaging, providing evidence of the importance of using the full recorded data.

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Comparison of elastic and acoustic reverse‐time migration on the synthetic elastic Marmousi‐II OBC dataset

Cited by 18 publications

References 12 publications

Elastic least-squares reverse time migration

Elastic least-squares reverse time migration

Elastic least-squares reverse time migration using the energy norm

Exact Wavefield Extrapolation for Elastic Reverse-time Migration

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