Least-squares reverse time migration: towards true amplitude imaging and improving the resolution

Dong, Shuqian; Cai, Jun; Guo, M.; Suh, Sang; Zhang, Z.; Wang, B.; Li, Z.

doi:10.1190/segam2012-1488.1

Cited by 80 publications

(49 citation statements)

References 5 publications

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“…LSRTM, similar to the full waveform inversion (FWI), attempts to minimize the misfit between the observed and simulated data by an iterative algorithm to refine seismic images towards the true reflectivity (Dong et al, 2012;Zeng et al, 2014). Therefore, LSRTM can produce migration images with better quality than conventional migrations (Ji, 2009;Dai et al, 2012Luo and Hale, 2014;Dutta and Schuster, 2014;Zhang and Schuster, 2014;Tan and Huang, 2014a;Aldawood et al, 2015;Wong et al, 2015;Y.…”

Section: Introductionmentioning

confidence: 97%

Least-squares reverse time migration with and without source wavelet estimation

Zhang

Zhou

Chen

et al. 2016

Journal of Applied Geophysics

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Section: Introductionmentioning

confidence: 97%

Least-squares reverse time migration with and without source wavelet estimation

Zhang

Zhou

Chen

et al. 2016

Journal of Applied Geophysics

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“…This implementation strongly emphasizes the matching of the amplitudes and the phases of the predicted and observed data. However, for real data, it is not easy to match the amplitudes directly because of the following factors (Dong et al, 2012;Zhang et al, 2013):…”

Section: Introductionmentioning

confidence: 99%

A cross-correlation objective function for least-squares migration and visco-acoustic imaging

Dutta

Sinha

Schuster

2014

SEG Technical Program Expanded Abstracts 2014

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SUMMARYConventional acoustic least-squares migration inverts for a reflectivity image that best matches the amplitudes of the observed data. However, for field data applications, it is not easy to match the recorded amplitudes because of the viscoelastic nature of the earth and inaccuracies in the estimation of source signature and strength at different shot locations. To relax the requirement for strong amplitude matching of least-squares migration, we use a normalized cross-correlation objective function that is only sensitive to the similarity between the predicted and the observed data. Such a normalized cross-correlation objective function is also equivalent to a time-domain phase inversion method where the main emphasis is only on matching the phase of the data rather than the amplitude. Numerical tests on synthetic and field data show that such an objective function can be used as an alternative to visco-acoustic least-squares reverse time migration (Q p -LSRTM) when there is strong attenuation in the subsurface and the estimation of the attenuation parameter Q p is insufficiently accurate.

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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%

Making the most out of the least (squares migration)

Dutta

Huang

Dai

et al. 2014

SEG Technical Program Expanded Abstracts 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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Least-squares reverse time migration: towards true amplitude imaging and improving the resolution

Cited by 80 publications

References 5 publications

Least-squares reverse time migration with and without source wavelet estimation

Least-squares reverse time migration with and without source wavelet estimation

A cross-correlation objective function for least-squares migration and visco-acoustic imaging

Making the most out of the least (squares migration)

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