scholarly journals Least-squares reverse time migration with local Radon-based preconditioning

Geophysics ◽  
2017 ◽  
Vol 82 (2) ◽  
pp. S75-S84 ◽  
Author(s):  
Gaurav Dutta ◽  
Matteo Giboli ◽  
Cyril Agut ◽  
Paul Williamson ◽  
Gerard T. Schuster
Keyword(s):  
Least Squares ◽  
Radon Transform ◽  
Field Data ◽  
Signal To Noise Ratio ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Inverse Mapping ◽  
Time Migration ◽  
Discrete Radon Transform ◽  
L2 Norm

Least-squares migration (LSM) can produce images with better balanced amplitudes and fewer artifacts than standard migration. The conventional objective function used for LSM minimizes the L2-norm of the data residual between the predicted and the observed data. However, for field-data applications in which the recorded data are noisy and undersampled, the conventional formulation of LSM fails to provide the desired uplift in the quality of the inverted image. We have developed a least-squares reverse time migration (LSRTM) method using local Radon-based preconditioning to overcome the low signal-to-noise ratio (S/N) problem of noisy or severely undersampled data. A high-resolution local Radon transform of the reflectivity is used, and sparseness constraints are imposed on the inverted reflectivity in the local Radon domain. The sparseness constraint is that the inverted reflectivity is sparse in the Radon domain and each location of the subsurface is represented by a limited number of geologic dips. The forward and the inverse mapping of the reflectivity to the local Radon domain and vice versa is done through 3D Fourier-based discrete Radon transform operators. The weights for the preconditioning are chosen to be varying locally based on the relative amplitudes of the local dips or assigned using quantile measures. Numerical tests on synthetic and field data validate the effectiveness of our approach in producing images with good S/N and fewer aliasing artifacts when compared with standard RTM or standard LSRTM.

Improved subsalt images with least-squares reverse time migration

2017 ◽  
Vol 5 (3) ◽  
pp. SN25-SN32 ◽  
Author(s):  
Ping Wang ◽  
Shouting Huang ◽  
Ming Wang
Keyword(s):  
Least Squares ◽  
Field Data ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Lateral Velocity ◽  
Image Domain ◽  
Time Migration ◽  
Iterative Inversion ◽  
Velocity Variations ◽  
Subsalt Imaging

Complex overburdens often distort reservoir images in terms of structural positioning, stratigraphic resolution, and amplitude fidelity. One prime example of a complex overburden is in the deepwater Gulf of Mexico, where thick and irregular layers of remobilized (i.e., allochthonous) salt are situated above prospective reservoir intervals. The highly variant salt layers create large lateral velocity variations that distort wave propagation and the illumination of deeper reservoir targets. In subsalt imaging, tools such as reflection tomography, full-waveform inversion, and detailed salt interpretation are needed to derive a high-resolution velocity model that captures the lateral velocity variations. Once a velocity field is obtained, reverse time migration (RTM) can be applied to restore structural positioning of events below and around the salt. However, RTM by nature is unable to fully recover the reflectivity for desired amplitudes and resolution. This shortcoming is well-recognized by the imaging community, and it has propelled the emergence of least-squares RTM (LSRTM) in recent years. We have investigated how current LSRTM methods perform on subsalt images. First, we compared the formulation of data-domain versus image-domain least-squares migration, as well as methods using single-iteration approximation versus iterative inversion. Then, we examined the resulting subsalt images of several LSRTM methods applied on the synthetic and field data. Among our tests, we found that image-domain single-iteration LSRTM methods, including an extension of an approximate inverse Hessian method in the curvelet domain, not only compensated for amplitude loss due to poor illumination caused by complex salt bodies, but it also produced subsalt images with fewer migration artifacts in the field data. In contrast, an iterative inversion method showed its potential for broadening the bandwidth in the subsalt, but it was less effective in reducing migration artifacts and noise. Based on our understanding, we evaluated the current state of LSRTM for subsalt imaging.

Least-squares reverse time migration of multiples in viscoacoustic media

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. S285-S297
Author(s):  
Zhina Li ◽  
Zhenchun Li ◽  
Qingqing Li ◽  
Qingyang Li ◽  
Miaomiao Sun ◽  
...  
Keyword(s):  
Least Squares ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Velocity Model ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Q Model ◽  
Standard Linear Solid ◽  
Standard Linear

The migration of multiples can provide complementary information about the subsurface, but crosstalk artifacts caused by the interference between different-order multiples reduce its reliability. To mitigate the crosstalk artifacts, least-squares reverse time migration (LSRTM) of multiples is suggested by some researchers. Multiples are more affected by attenuation than primaries because of the longer travel path. To avoid incorrect waveform matching during the inversion, we propose to include viscosity in the LSRTM implementation. A method of LSRTM of multiples is introduced based on a viscoacoustic wave equation, which is derived from the generalized standard linear solid model. The merit of the proposed method is that it not only compensates for the amplitude loss and phase change, which cannot be achieved by traditional RTM and LSRTM of multiples, but it also provides more information about the subsurface with fewer crosstalk artifacts by using multiples compared with the viscoacoustic LSRTM of primaries. Tests on sensitivity to the errors in the velocity model, the Q model, and the separated multiples reveal that accurate models and input multiples are vital to the image quality. Numerical tests on synthetic models and real data demonstrate the advantages of our approach in improving the quality of the image in terms of amplitude balancing and signal-to-noise ratio.

scholarly journals Q-least-squares reverse time migration with viscoacoustic deblurring filters

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. S425-S438 ◽  
Author(s):  
Yuqing Chen ◽  
Gaurav Dutta ◽  
Wei Dai ◽  
Gerard T. Schuster
Keyword(s):  
Convergence Rate ◽  
Least Squares ◽  
Field Data ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Acoustic Data ◽  
Numerical Tests ◽  
Time Migration ◽  
Preconditioning Method ◽  
Number Of Iterations

Viscoacoustic least-squares reverse time migration, also denoted as Q-LSRTM, linearly inverts for the subsurface reflectivity model from lossy data. Compared with conventional migration methods, it can compensate for the amplitude loss in the migrated images due to strong subsurface attenuation and can produce reflectors that are accurately positioned in depth. However, the adjoint [Formula: see text] propagators used for backward propagating the residual data are also attenuative. Thus, the inverted images from [Formula: see text]-LSRTM with a small number of iterations are often observed to have lower resolution when compared with the benchmark acoustic LSRTM images from acoustic data. To increase the resolution and accelerate the convergence of [Formula: see text]-LSRTM, we used viscoacoustic deblurring filters as a preconditioner for [Formula: see text]-LSRTM. These filters can be estimated by matching a simulated migration image to its reference reflectivity model. Numerical tests on synthetic and field data demonstrate that [Formula: see text]-LSRTM combined with viscoacoustic deblurring filters can produce images with higher resolution and more balanced amplitudes than images from acoustic RTM, acoustic LSRTM, and [Formula: see text]-LSRTM when there is strong attenuation in the background medium. Our preconditioning method is also shown to improve the convergence rate of [Formula: see text]-LSRTM by more than 30% in some cases and significantly compensate for the lossy artifacts in RTM images.

Pure qP-wave least-squares reverse time migration in vertically transverse isotropic media and its application to field data

2020 ◽  
Vol 17 (2) ◽  
pp. 208-220
Author(s):  
Jian-Ping Huang ◽  
Xin-Ru Mu ◽  
Zhen-Chun Li ◽  
Qing-Yang Li ◽  
Shuang-Qi Yuan ◽  
...  
Keyword(s):  
Least Squares ◽  
Field Data ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Isotropic Media ◽  
Transverse Isotropic

2D least-squares elastic reverse time migration of multicomponent seismic data

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. S523-S538 ◽  
Author(s):  
Bingluo Gu ◽  
Jianguang Han ◽  
Zhiming Ren ◽  
Zhenchun Li
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Adjoint Operator ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Multicomponent Seismic ◽  
Isotropic Media ◽  
Wavefield Decomposition

Elastic reverse time migration (ERTM) is a state-of-the-art imaging technique used for determining complicated subsurface structures. However, the migrated images often suffer from low spatial resolution, low signal-to-noise ratio (S/N), and unbalanced amplitudes because the theoretical hypothesis of ERTM cannot be satisfied in practice. Although elastic least-squares reverse time migration (ELSRTM) has been proposed to address the issues of ERTM, the resulting images are generally represented by parameter perturbations such as P- and S-velocity perturbations, which have the different physical meanings from the ERTM images. To produce improved ERTM images, we used a least-squares RTM method for elastic data in isotropic media by applying least-squares inversion to ERTM. In the least-squares ERTM method, the forward operator generates multicomponent seismic data from the migrated images by applying elastic wavefield decomposition, scalar wavefield extrapolation, and wavefield recomposition operators. Additionally, the adjoint operator generates PP and PS images using ERTM, at which point the wavefield decomposition operator and scalar imaging condition are applied in the imaging process. Compared to conventional ERTM, our least-squares ERTM method enables us to produce improved ERTM images with higher resolution, more balanced amplitudes, and fewer artifacts. Several synthetic and field data examples were used to validate the effectiveness of the proposed least-squares ERTM method.

Least-squares reverse time migration with an angle-dependent weighting factor

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. S299-S310 ◽  
Author(s):  
Kai Yang ◽  
Jianfeng Zhang
Keyword(s):  
Least Squares ◽  
Step Length ◽  
Weighting Factor ◽  
Deep Part ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Pass Filter ◽  
Time Migration ◽  
L2 Norm ◽  
The Right

Least-squares reverse time migration (LSRTM) produces higher quality images than conventional RTM. However, directly using the standard gradient formula, the inverted images suffer from low-wavenumber noise. Using a simple high-pass filter on the gradient can alleviate the effect of the low-wavenumber noise. But, owing to the illumination issue, the amplitudes are not balanced and in the deep part they are often weak. These two issues can be mitigated by the iterative approach, but it needs more iterations. We introduced an angle-dependent weighting factor to weight the gradient of LSRTM to suppress the low-wavenumber noise and also to emphasize the gradient in the deep part. An optimal step length for the L2-norm objective function is also presented to scale the gradient to the right order. Two numerical examples performed with the data synthesized on the Sigsbee2A and Marmousi models indicate that when using this weighted gradient combined with the preconditioned [Formula: see text]-BFGS algorithm with the optimal step length, only a few iterations can achieve satisfying results.

Sparsity-promoting multi-parameter pseudoinverse Born inversion in acoustic media

Geophysics ◽  
2021 ◽  
pp. 1-62
Author(s):  
Milad Farshad ◽  
Hervé Chauris
Keyword(s):  
Least Squares ◽  
Cross Talk ◽  
Radon Transform ◽  
Weighted Least Squares ◽  
Variable Density ◽  
Imaging Method ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Acoustic Media

Least-squares reverse-time migration has become the method of choice for quantitative seismic imaging. The main drawback of such scheme is that it requires many migration/modeling cycles. The convergence of least-squares reverse-time migration can be accelerated by using a suitable preconditioner. In the context of extended domain in a variable density acoustic media, the pseudoinverse Born operator is the recommended preconditioner, providing quantitative results within a single iteration. This method consists of two steps: application of the pseudoinverse Born operator, and inversion of two parameters using an efficient weighted least-squares approach based on the Radon transform. As expected, cross-talk artifacts are generated in the second step due to limited acquisition. We present a variable density pseudoinverse Born operator constrained with the ℓ1-norm for each model parameter to suppress the artifacts. The fast iterative shrinkage-thresholding algorithm is used to carry out the optimization problem. In classical iterative least-squares migration, the ℓ1-norm constraints would affect the whole imaging process. As the imaging method is split into two steps, only the Radon transform part is modified, where no wave-based operators are involved. Through numerical experiments, we verify the robustness of the proposed method against different migration artifacts including the parameter cross-talk, interfaces with abrupt truncations, sparse shot acquisition geometry, noisy data and high contrast complex structures.

Eliminating artifacts in migration of surface-related multiples: An application to marine data

2016 ◽  
Vol 4 (4) ◽  
pp. SQ51-SQ57 ◽  
Author(s):  
Yikang Zheng ◽  
Yibo Wang ◽  
Xu Chang
Keyword(s):  
Radon Transform ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Seismic Events ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Valuable Complement ◽  
Marine Data ◽  
The One

Migration of multiples can use surface-related multiples to provide extra illumination of the subsurface; however, the migrated images usually contain many migration artifacts. We have developed an efficient workflow to eliminate the artifacts in migration of surface-related multiples and applied it to marine data. Our workflow was based on the criterion that true seismic events in angle-domain common-image gathers (ADCIGs) should be flat. The ADCIGs were obtained via the one-way wave-equation migration and then processed by high-resolution parabolic Radon transform to separate the artifacts. Using the adjoint Radon transform, the filtered ADCIGs can be stacked to get the final image with a high signal-to-noise ratio. We also discovered that the ADCIGs can be extracted from Fourier finite-difference migration more efficiently than by reverse time migration. In the application to marine data, most noise generated by the crosscorrelation of undesired seismic events was suppressed. This shows that the final images can be a valuable complement to conventional migration using primaries only.

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