Sparsity-promoting Least-squares Migration with the Linearized Inverse Scattering Imaging Condition

Author(s):  
P.A. Witte ◽  
M. Yang ◽  
F.J. Herrmann
Geophysics ◽  
2021 ◽  
pp. 1-43
Author(s):  
Shaoping Lu ◽  
Lingyun Qiu ◽  
Xiang Li

Surface-related multiple wavefields constitute redundant information in conventional migration and can often be difficult to attenuate. However, when used for migration, multiple wavefields can improve subsurface illumination. Unfortunately, the process of imaging using multiples involves the management of crosstalk, which largely restricts its application. Crosstalk causes phantom images formed by spurious correlation of unrelated events in a migration process. These events can be unrelated orders of multiples in the source and receiver wavefields; they can also be one event associated with a reflector in the source wavefield and another event generated by a different reflector in the receiver wavefield. In this paper, we first examine crosstalk by explicitly investigating its generation mechanisms in a migration process and classifying it into different categories based on causality. Following this analysis, crosstalk can be predicted in a migration process and subtracted in the image domain; however, this method is usually difficult to apply due to the complexity of wavefield separation and adaptive subtraction. Furthermore, we present different algorithms to attenuate the crosstalk, including a deconvolution imaging condition, a least-squares migration (LSM) method, and an advanced algorithm combining LSM with a deconvolution imaging condition. We illustrate these different strategies on synthetic examples. A deconvolution imaging condition can attenuate some crosstalk, but it is less effective at suppressing strong coherent crosstalk events. However, the LSM method can fundamentally address the crosstalk issue, and this approach is further optimized when combined with a deconvolution imaging condition.


Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. S315-S325 ◽  
Author(s):  
Yuting Duan ◽  
Antoine Guitton ◽  
Paul Sava

Least-squares migration can produce images with improved resolution and reduced migration artifacts, compared with conventional imaging. We have developed a method for elastic least-squares reverse time migration (LSRTM) based on a new perturbation imaging condition that yields scalar images of squared P- and S-velocity perturbations. These perturbation images do not suffer from polarity reversals that are common for more conventional elastic imaging methods. We use 2D synthetic and field-data examples to demonstrate the proposed LSRTM algorithm using the perturbation imaging condition. Our results show that elastic LSRTM improves the energy focusing and illumination of the elastic images and it attenuates artifacts resulting, for instance, from sparseness in the wavefield sampling and crosstalk of the P- and S-modes. Compared with RTM images, the LSRTM images provide more accurate relative amplitude information that is useful for reservoir characterization.


2019 ◽  
Author(s):  
Bruno Dias ◽  
Cláudio Guerra ◽  
André Bulcão ◽  
Roberto Dias

2020 ◽  
Author(s):  
Lian Duan ◽  
Alejandro Valenciano ◽  
Nizar Chemingui

Geophysics ◽  
2009 ◽  
Vol 74 (5) ◽  
pp. H27-H33 ◽  
Author(s):  
Jun Ji

To reduce the migration artifacts arising from incomplete data or inaccurate operators instead of migrating data with the adjoint of the forward-modeling operator, a least-squares migration often is considered. Least-squares migration requires a forward-modeling operator and its adjoint. In a derivation of the mathematically correct adjoint operator to a given forward-time-extrapolation modeling operator, the exact adjoint of the derived operator is obtained by formulating an explicit matrix equation for the forward operation and transposing it. The programs that implement the exact adjoint operator pair are verified by the dot-product test. The derived exact adjoint operator turns out to differ from the conventional reverse-time-migration (RTM) operator, an implementation of wavefield extrapolation backward in time. Examples with synthetic data show that migration using the exact adjoint operator gives similar results for a conventional RTM operator and that least-squares RTM is quite successful in reducing most migration artifacts. The least-squares solution using the exact adjoint pair produces a model that fits the data better than one using a conventional RTM operator pair.


Geophysics ◽  
2021 ◽  
pp. 1-61
Author(s):  
Luana Nobre Osorio ◽  
Bruno Pereira-Dias ◽  
André Bulcão ◽  
Luiz Landau

Least-squares migration (LSM) is an effective technique for mitigating blurring effects and migration artifacts generated by the limited data frequency bandwidth, incomplete coverage of geometry, source signature, and unbalanced amplitudes caused by complex wavefield propagation in the subsurface. Migration deconvolution (MD) is an image-domain approach for least-squares migration, which approximates the Hessian operator using a set of precomputed point spread functions (PSFs). We introduce a new workflow by integrating the MD and the domain decomposition (DD) methods. The DD techniques aim to solve large and complex linear systems by splitting problems into smaller parts, facilitating parallel computing, and providing a higher convergence in iterative algorithms. The following proposal suggests that instead of solving the problem in a unique domain, as conventionally performed, we split the problem into subdomains that overlap and solve each of them independently. We accelerate the convergence rate of the conjugate gradient solver by applying the DD methods to retrieve a better reflectivity, which is mainly visible in regions with low amplitudes. Moreover, using the pseudo-Hessian operator, the convergence of the algorithm is accelerated, suggesting that the inverse problem becomes better conditioned. Experiments using the synthetic Pluto model demonstrate that the proposed algorithm dramatically reduces the required number of iterations while providing a considerable enhancement in the image resolution and better continuity of poorly illuminated events.


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