Plane-wave least-squares reverse-time migration

Dai, Weihui; Schuster, Gerard T.

doi:10.1190/geo2012-0377.1

Cited by 219 publications

(45 citation statements)

References 34 publications

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“…The first strategy is to invert all of the shot gathers simultaneously for the reflectivity distribution, so this approach is denoted as overdetermined LSM (Dai, 2013). It is appropriate when the velocity model is accurate so the events in the common image gather are mostly aligned with one another.…”

Section: Least-squares Migration Theorymentioning

confidence: 99%

“…There are two different strategies for applying LSM to S distinct shot gathers Dai and Schuster, 2013). The first strategy is to invert all of the shot gathers simultaneously for the reflectivity distribution, so this approach is denoted as overdetermined LSM (Dai, 2013).…”

Section: Least-squares Migration Theorymentioning

confidence: 99%

“…To avoid such updates, we smooth the migration velocity model (McMechan, 1983;Loewenthal et al, 1987;Fletcher et al, 2005). To mitigate problems with an inaccurate migration velocity model, regularization terms or constraints (Sacchi et al, 2006;Guitton, 2006; Wang et al, 2011;Dong et al, 2012;Dai, 2013;Dai and Schuster, 2013) can be used to partly account for misaligned reflectors.There are two different strategies for applying LSM to S distinct shot gathers Dai and Schuster, 2013). The first strategy is to invert all of the shot gathers simultaneously for the reflectivity distribution, so this approach is denoted as overdetermined LSM (Dai, 2013).…”

mentioning

confidence: 99%

“…To mitigate problems with an inaccurate migration velocity model, regularization terms or constraints (Sacchi et al, 2006;Guitton, 2006; Wang et al, 2011;Dong et al, 2012;Dai, 2013;Dai and Schuster, 2013) can be used to partly account for misaligned reflectors.…”

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Making the most out of least-squares migration

et al. 2014

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SUMMARYStandard migration images can suffer from migration artifacts due to 1) poor source-receiver sampling, 2) weak amplitudes caused by geometric spreading, 3) attenuation, 4) defocusing, 5) poor resolution due to limited source-receiver aperture, and 6) ringiness caused by a ringy source wavelet. To partly remedy these problems, least-squares migration (LSM), also known as linearized seismic inversion or migration deconvolution (MD), proposes to linearly invert seismic data for the reflectivity distribution. If the migration velocity model is sufficiently accurate, then LSM can mitigate many of the above problems and lead to a more resolved migration image, sometimes with twice the spatial resolution. However, there are two problems with LSM: the cost can be an order of magnitude more than standard migration and the quality of the LSM image is no better than the standard image for velocity errors of 5% or more. We now show how to get the most from leastsquares migration by reducing the cost and velocity sensitivity of LSM. LEAST-SQUARES MIGRATION THEORYThe theory for least-squares migration is described in Nemeth et al. (1999) and Duquet et al. (2000), where the smooth background does not change with iteration number. Only the reflectivity distribution is updated at each iteration. This algorithm is equivalent to linearized waveform inversion (Lailly, 1984), and can be described as iteratively updating the reflectivity vector m bywhere α is the step length, P is the preconditioning matrix that approximates the inverse to the Hessian matrix, d is the recorded reflection data, k represents the iteration index, and L represents the linearized forward modeling operator that uses the smooth background velocity model 1 . If the preconditioner is inadequate, a conjugate gradient or quasi-Newton method is used to iteratively update the solution. In practice, the algorithm is often implemented in the time-space domain.The implementation of LSM is described in Nemeth et al. (1999) and Duquet et al. (2000) for diffraction stack migration and in Plessix and Mulder (2004) for reverse time migration. Typically, diffraction stack migration provides images with fewer artifacts because it only smears reflections along the migration ellipses. In contrast, RTM automatically generates upgoing reflections from reflectors, so residuals are also smeared between 1 A smooth velocity model is typically used so as to avoid smearing residuals along the rabbit-ear wavepaths. The smearing of residuals should only be along the migration ellipses that are tangent to the reflector boundaries (Zhan et al., 2014). reflecting interfaces (Guitton, 2006; Liu et al., 2011;Zhan et al., 2014) to give rise to unwanted migration artifacts. To avoid such updates, we smooth the migration velocity model (McMechan, 1983;Loewenthal et al., 1987;Fletcher et al., 2005). To mitigate problems with an inaccurate migration velocity model, regularization terms or constraints (Sacchi et al., 2006;Guitton, 2006; Wang et al., 2011;Dong et al., 2012;Dai, 2013;Dai an...

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Section: Least-squares Migration Theorymentioning

confidence: 99%

Section: Least-squares Migration Theorymentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Making the most out of least-squares migration

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“…It is not limited by the inclination of the underground structure and the change of the lateral velocity of the medium, and even can the image of the rotary wave [3][4][5][6]. When the finite difference method is performed for RTM imaging, the selection of the difference order directly affects the imaging precision and the amount of calculation [7][8][9], and the higher the differential order, the more the imaging is accurate, but the amount of RTM is multiplied by the forward and reverse recursion of the wave field [10,11].…”

Section: Introductionmentioning

confidence: 99%

Multi-scale high order finite different reverse time migration imaging

Zhang

Zhu

Guo

2018

J Image Video Proc.

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At present, the main difficulties in oil and gas exploration are focused on the exploration of the deep region. How to improve the efficiency of imaging on the same accuracy of migration is an urgent problem. The paper puts forward the concept of multi-scale grid for the underground medium based on finite difference time domain (FDTD) of reverse time migration (RTM), which can avoid the oversampling to high speed and hold the sampling density to low-speed layer. The method can shorten the time of RTM with the same accuracy of the traditional RTM imaging. Firstly, devise the multi-scale grid model according to the velocity model; it uses the small-scale grid for the low-speed area and large-scale grid for high-speed area; secondly, calculate the value of each point of the wave field on multi-scale model, especially the points of transition zone; finally, image the underground media according to the RTM imaging condition. The experimental results show that under the condition of the same simulation order, the multi-scale RTM imaging computational efficiency can be promoted by 25.05% average within this paper.

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