Joint imaging of angle-dependent reflectivity and estimation of the migration velocity model using multiple scattering

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. R859-R868
Author(s):  
Mikhail Davydenko ◽  
D. J. Verschuur
Keyword(s):  
Velocity Model ◽  
Migration Velocity ◽  
Velocity Estimation ◽  
Velocity Analysis ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Image Domain ◽  
Time Migration ◽  
Internal Multiples ◽  
Angle Dependent Reflectivity

Migration velocity analysis is an important method for providing an accurate velocity model for seismic imaging, which is crucial for correct focusing and localization of subsurface information. Conventionally, only primaries are considered as a source of information for both methods. The use of surface multiples in imaging is becoming more common due to the use of inversion-based approaches, which allow us to handle the crosstalk associated with multiples. However, including internal multiples in imaging and velocity estimation is not straightforward using the standard combination of reverse time migration in combination with image-domain velocity tomography. Incorporating internal multiples in imaging and velocity estimation is possible with the joint migration inversion (JMI) methodology, in which internal multiples are explicitly modeled using the estimated reflectivity via a data-domain objective function. However, to correctly match the observed data, the angle-dependent reflectivity and the migration velocity model need to be determined, which provide an over-parameterization of the inversion problem. Therefore, we have extended the JMI methodology to carry out velocity analysis via the extended image domain, in which the angle-dependent reflectivity is updated via data-domain matching. Examples of synthetic and field data with strong internal multiples demonstrate the validity of our method.

Automatic anisotropic migration velocity analysis for reverse-time migration

2012 ◽  
Author(s):  
Wiktor Weibull ◽  
Børge Arntsen ◽  
Marianne Houbiers ◽  
Joachim Mispel
Keyword(s):  
Migration Velocity ◽  
Velocity Analysis ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Migration Velocity Analysis

Angle-domain common-image gathers from plane-wave least-squares reverse time migration

Geophysics ◽  
2021 ◽  
pp. 1-60
Author(s):  
Chuang Li ◽  
Zhaoqi Gao ◽  
Jinghuai Gao ◽  
Feipeng Li ◽  
Tao Yang
Keyword(s):  
Inverse Problem ◽  
Plane Wave ◽  
Least Squares ◽  
Migration Velocity ◽  
Velocity Analysis ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Migration Velocity Analysis ◽  
Amplitude Versus

Angle-domain common-image gathers (ADCIGs) that can be used for migration velocity analysis and amplitude versus angle analysis are important for seismic exploration. However, because of limited acquisition geometry and seismic frequency band, the ADCIGs extracted by reverse time migration (RTM) suffer from illumination gaps, migration artifacts, and low resolution. We have developed a reflection angle-domain pseudo-extended plane-wave least-squares RTM method for obtaining high-quality ADCIGs. We build the mapping relations between the ADCIGs and the plane-wave sections using an angle-domain pseudo-extended Born modeling operator and an adjoint operator, based on which we formulate the extraction of ADCIGs as an inverse problem. The inverse problem is iteratively solved by a preconditioned stochastic conjugate gradient method, allowing for reduction in computational cost by migrating only a subset instead of the whole dataset and improving image quality thanks to preconditioners. Numerical tests on synthetic and field data verify that the proposed method can compensate for illumination gaps, suppress migration artifacts, and improve resolution of the ADCIGs and the stacked images. Therefore, compared with RTM, the proposed method provides a more reliable input for migration velocity analysis and amplitude versus angle analysis. Moreover, it also provides much better stacked images for seismic interpretation.

Least-squares reverse time migration in the presence of velocity errors

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. S567-S580 ◽  
Author(s):  
Jizhong Yang ◽  
Yunyue Elita Li ◽  
Arthur Cheng ◽  
Yuzhu Liu ◽  
Liangguo Dong
Keyword(s):  
Least Squares ◽  
Velocity Model ◽  
Migration Velocity ◽  
Resolution Limit ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Large Velocity ◽  
Iterative Inversion ◽  
Inversion Procedure

Least-squares reverse time migration (LSRTM), which aims to match the modeled data with the observed data in an iterative inversion procedure, is very sensitive to the accuracy of the migration velocity model. If the migration velocity model contains errors, the final migration image may be defocused and incoherent. We have used an LSRTM scheme based on the subsurface offset extended imaging condition, least-squares extended reverse time migration (LSERTM), to provide a better solution when large velocity errors exist. By introducing an extra dimension in the image space, LSERTM can fit the observed data even when significant errors are present in the migration velocity model. We further investigate this property and find that after stacking the extended migration images along the subsurface offset axis within the theoretical lateral resolution limit, we can obtain an image with better coherency and fewer migration artifacts. Using multiple numerical examples, we demonstrate that our method provides superior inversion results compared to conventional LSRTM when the bulk velocity errors are as large as 10%.

Anisotropic migration velocity analysis using reverse-time migration

Geophysics ◽  
2014 ◽  
Vol 79 (1) ◽  
pp. R13-R25 ◽  
Author(s):  
Wiktor Waldemar Weibull ◽  
Børge Arntsen
Keyword(s):  
Wave Equation ◽  
Seismic Anisotropy ◽  
Migration Velocity ◽  
Image Point ◽  
Velocity Analysis ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Migration Velocity Analysis ◽  
Isotropic Media

Seismic anisotropy, if not accounted for, can cause significant mispositioning of the reflectors in depth-migrated images. Accounting for anisotropy in depth migration requires velocity analysis tools that can estimate the anisotropic background velocity field. We extended wave equation migration velocity analysis to deal with 2D tilted transverse isotropic media. The velocities were obtained automatically by nonlinear optimization of the focusing and stack power of common-image point gathers constructed using an extended imaging condition. We used the elastic two-way wave equation to reconstruct the wavefields needed for the image and gradient computations. This led to an anisotropic migration velocity analysis algorithm based on reverse-time migration. We illustrated the method with synthetic and field data examples based on marine surface seismic acquisition. The results showed that the method significantly improves the quality of the depth-migrated image. However, as is common in the case of velocity analysis using surface seismic data, the estimation of anisotropic parameters seems to be strongly nonunique.

Improving the least-squares image by using angle information to avoid cycle skipping

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. S581-S598 ◽  
Author(s):  
Bin He ◽  
Yike Liu ◽  
Yanbao Zhang
Keyword(s):  
Least Squares ◽  
Real Data ◽  
Velocity Model ◽  
Migration Velocity ◽  
Common Source ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Propagation Angle ◽  
Time Migration ◽  
The Common

In the past few decades, the least-squares reverse time migration (LSRTM) algorithm has been widely used to enhance images of complex subsurface structures by minimizing the data misfit function between the predicted and observed seismic data. However, this algorithm is sensitive to the accuracy of the migration velocity model, which, in the case of real data applications (generally obtained via tomography), always deviates from the true velocity model. Therefore, conventional LSRTM faces a cycle-skipping problem caused by a smeared image when using an inaccurate migration velocity model. To address the cycle-skipping problem, we have introduced an angle-domain LSRTM algorithm. Unlike the conventional LSRTM algorithm, our method updates the common source-propagation angle image gathers rather than the stacked image. An extended Born modeling operator in the common source-propagation angle domain is was derived, which reproduced kinematically accurate data in the presence of velocity errors. Our method can provide more focused images with high resolution as well as angle-domain common-image gathers (ADCIGs) with enhanced resolution and balanced amplitudes. However, because the velocity model is not updated, the provided image can have errors in depth. Synthetic and field examples are used to verify that our method can robustly improve the quality of the ADCIGs and the finally stacked images with affordable computational costs in the presence of velocity errors.

Joint least-squares reverse time migration of primary and prismatic waves

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. S29-S40 ◽  
Author(s):  
Jizhong Yang ◽  
Yuzhu Liu ◽  
Yunyue Elita Li ◽  
Arthur Cheng ◽  
Liangguo Dong ◽  
...  
Keyword(s):  
Least Squares ◽  
Nonlinear Least Squares ◽  
Velocity Model ◽  
Migration Velocity ◽  
Gradient Algorithm ◽  
Normal Equation ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Recorded Data

Direct imaging of the steeply dipping structures is challenging for conventional reverse time migration (RTM), especially when there are no strong reflectors in the migration velocity model. To address this issue, we have enhanced the imaging of the steeply dipping structures by incorporating the prismatic waves. We formulate the imaging problem in a nonlinear least-squares optimization framework because the prismatic waves cannot be linearly mapped from the model perturbation. Primary and prismatic waves are jointly imaged to provide a single consistent image that includes structures illuminated by both types of waves, avoiding the complexities in scaling and/or interpreting primary and prismatic images separately. A conjugate gradient algorithm is used to iteratively solve the least-squares normal equation. This inversion procedure can become unstable if directly using the recorded data for migration because it is hindered by the crosstalk caused by imaging primary waves with the prismatic imaging operator. Therefore, we isolate the prismatic waves from the recorded data and image them with the prismatic imaging operator. Our scheme only requires a kinematically accurate and smooth migration velocity model, without the need to explicitly embed the strong reflectors in the migration velocity model. Realistic 2D numerical examples demonstrate that our method can resolve the steeply dipping structures much better than conventional least-squares RTM of primary waves.

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