Least-squares Gaussian beam migration in elastic media

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. S329-S340 ◽  
Author(s):  
Yubo Yue ◽  
Paul Sava ◽  
Zhongping Qian ◽  
Jidong Yang ◽  
Zhen Zou
Keyword(s):  
Least Squares ◽  
Gaussian Beam ◽  
Computational Cost ◽  
Synthetic Data ◽  
Preconditioned Conjugate Gradient ◽  
Imaging Method ◽  
Data Sets ◽  
Isotropic Media ◽  
Gaussian Beam Migration ◽  
Multicomponent Data

Gaussian beam migration (GBM) is an effective imaging method that has the ability to image multiple arrivals while preserving the advantages of ray-based methods. We have extended this method to linearized least-squares imaging for elastic waves in isotropic media. We have dynamically transformed the multicomponent data to the principal components of different wave modes using the polarization information available in the beam migration process, and then we use Gaussian beams as wavefield propagator to construct the forward modeling and adjoint migration operators. Based on the constructed operators, we formulate a least-squares migration scheme that is iteratively solved using a preconditioned conjugate gradient method. With this method, we can obtain crosstalk-attenuated multiwave images with better subsurface illumination and higher resolution than those of the conventional elastic Gaussian beam migration. This method also allows us to achieve a good balance between computational cost and imaging accuracy, which are both important requirements for iterative least-squares migrations. Numerical tests on two synthetic data sets demonstrate the validity and effectiveness of our proposed method.

Least-squares Gaussian beam migration

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. S87-S100 ◽  
Author(s):  
Hao Hu ◽  
Yike Liu ◽  
Yingcai Zheng ◽  
Xuejian Liu ◽  
Huiyi Lu
Keyword(s):  
Least Squares ◽  
Gaussian Beam ◽  
Spatial Resolution ◽  
Field Data ◽  
Synthetic Data ◽  
Data Sets ◽  
Data Set ◽  
Seismic Acquisition ◽  
Gaussian Beam Migration ◽  
Least Squares Migration

Least-squares migration (LSM) can be effective to mitigate the limitation of finite-seismic acquisition, balance the subsurface illumination, and improve the spatial resolution of the image, but it requires iterations of migration and demigration to obtain the desired subsurface reflectivity model. The computational efficiency and accuracy of migration and demigration operators are crucial for applying the algorithm. We have developed a test of the feasibility of using the Gaussian beam as the wavefield extrapolating operator for the LSM, denoted as least-squares Gaussian beam migration. Our method combines the advantages of the LSM and the efficiency of the Gaussian beam propagator. Our numerical evaluations, including two synthetic data sets and one marine field data set, illustrate that the proposed approach could be used to obtain amplitude-balanced images and to broaden the bandwidth of the migrated images in particular for the low-wavenumber components.

Least-squares Gaussian beam migration in viscoacoustic media

Geophysics ◽  
2020 ◽  
Vol 86 (1) ◽  
pp. S17-S28
Author(s):  
Yubo Yue ◽  
Yujin Liu ◽  
Yaonan Li ◽  
Yunyan Shi
Keyword(s):  
Green’S Function ◽  
Least Squares ◽  
Gaussian Beam ◽  
Green's Function ◽  
Seismic Waves ◽  
Image Resolution ◽  
Data Sets ◽  
Phase Dispersion ◽  
Using Data ◽  
Gaussian Beam Migration

Because of amplitude decay and phase dispersion of seismic waves, conventional migrations are insufficient to produce satisfactory images using data observed in highly attenuative geologic environments. We have developed a least-squares Gaussian beam migration method for viscoacoustic data imaging, which can not only compensate for amplitude decay and phase dispersion caused by attenuation, but it can also improve image resolution and amplitude fidelity through linearized least-squares inversion. We represent the viscoacoustic Green’s function by a summation of Gaussian beams, in which an attenuation traveltime is incorporated to simulate or compensate for attenuation effects. Based on the beam representation of the Green’s function, we construct the viscoacoustic Born forward modeling and adjoint migration operators, which can be effectively evaluated by a time-domain approach based on a filter-bank technique. With the constructed operators, we formulate a least-squares migration scheme to iteratively solve for the optimal image. Numerical tests on synthetic and field data sets demonstrate that our method can effectively compensate for the attenuation effects and produce images with higher resolution and more balanced amplitudes than images from acoustic least-squares Gaussian beam migration.

Gaussian beam depth migration for anisotropic media

Geophysics ◽  
1995 ◽  
Vol 60 (5) ◽  
pp. 1474-1484 ◽  
Author(s):  
Tariq Alkhalifah
Keyword(s):  
Gaussian Beam ◽  
Ray Tracing ◽  
Anisotropy Parameter ◽  
Synthetic Data ◽  
Transversely Isotropic ◽  
Anisotropic Media ◽  
Inhomogeneous Media ◽  
Computational Effort ◽  
Isotropic Media ◽  
Gaussian Beam Migration

Gaussian beam migration (GBM), as it is implemented today, efficiently handles isotropic inhomogeneous media. The approach is based on the solution of the wave equation in ray‐centered coordinates. Here, I extend the method to work for 2-D migration in generally anisotropic inhomogeneous media. Extension of the Gaussian‐beam method from isotropic to anisotropic media involves modification of the kinematics and dynamics in the required ray tracing. While the accuracy of the paraxial expansion for anisotropic media is comparable to that for isotropic media, ray tracing in anisotropic media is much slower than that in isotropic media. However, because ray tracing is just a small portion of the computation in GBM, the increased computational effort in general anisotropic GBM is typically only about 40%. Application of this method to synthetic examples shows successful migration in inhomogeneous, transversely isotropic media for reflector dips up to and beyond 90°. Further applications to synthetic data of layered anisotropic media show the importance of applying the proper smoothing to the velocity field used in the migration. Also, tests with synthetic data show that the quality of anisotropic migration of steep events in a medium with velocity increasing with depth is much more sensitive to the Thomsen anisotropy parameter ε than to the parameter δ. Thus, a good estimate of ε is needed to apply anisotropic migration with confidence.

scholarly journals An Effective Acoustic Impedance Imaging Based on a Broadband Gaussian Beam Migration

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4105
Author(s):  
Shaoyong Liu ◽  
Wenting Zhu ◽  
Zhe Yan ◽  
Peng Xu ◽  
Huazhong Wang
Keyword(s):  
Gaussian Beam ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Oil And Gas ◽  
Waveform Inversion ◽  
Synthetic Data ◽  
Imaging Method ◽  
Nonlinear Inversion ◽  
Oil And Gas Exploration ◽  
Gaussian Beam Migration

The estimation of the subsurface acoustic impedance (AI) model is an important step of seismic data processing for oil and gas exploration. The full waveform inversion (FWI) is a powerful way to invert the subsurface parameters with surface acquired seismic data. Nevertheless, the strong nonlinear relationship between the seismic data and the subsurface model will cause nonconvergence and unstable problems in practice. To divide the nonlinear inversion into some more linear steps, a 2D AI inversion imaging method is proposed to estimate the broadband AI model based on a broadband reflectivity. Firstly, a novel scheme based on Gaussian beam migration (GBM) is proposed to produce the point spread function (PSF) and conventional image of the subsurface. Then, the broadband reflectivity can be obtained by implementing deconvolution on the image with respect to the calculated PSF. Assuming that the low-wavenumber part of the AI model can be deduced by the background velocity, we implemented the AI inversion imaging scheme by merging the obtained broadband reflectivity as the high-wavenumber part of the AI model and produced a broadband AI result. The developed broadband migration based on GBM as the computational hotspot of the proposed 2D AI inversion imaging includes only two GBM and one Gaussian beam demigraton (Born modeling) processes. Hence, the developed broadband GBM is more efficient than the broadband imaging using the least-squares migrations (LSMs) that require multiple iterations (every iteration includes one Born modeling and one migration process) to minimize the objective function of data residuals. Numerical examples of both synthetic data and field data have demonstrated the validity and application potential of the proposed method.

scholarly journals Target-oriented Gaussian beam migration using a modified ray tracing scheme

2019 ◽  
Vol 16 (6) ◽  
pp. 1301-1319 ◽  
Author(s):  
Rui Zhang ◽  
Jian-Ping Huang ◽  
Su-Bin Zhuang ◽  
Zhen-Chun Li
Keyword(s):  
Gaussian Beam ◽  
Ray Tracing ◽  
Large Scale ◽  
High Efficiency ◽  
Complex Structure ◽  
Back Propagation ◽  
Imaging Method ◽  
Computation Efficiency ◽  
Simple Implementation ◽  
Gaussian Beam Migration

Abstract For large-scale 3D seismic data, target-oriented reservoir imaging is more attractive than conventional full-volume migration, in terms of computation efficiency. Gaussian beam migration (GBM) is one of the most robust depth imaging method, which not only keeps the advantages of ray methods, such as high efficiency and flexibility, but also allows us to solve caustics and multipathing problems. But conventional Gaussian beam migration requires slant stack for prestack data, and ray tracing from beam center location to subsurface, which is not easy to be directly applied for target-oriented imaging. In this paper, we modify the conventional Gaussian beam migration scheme, by shooting rays from subsurface image points to receivers to implement wavefield back-propagation. This modification helps us to achieve a better subsurface illumination in complex structure and allows simple implementation for target reservoir imaging. Significantly, compared with the wavefield-based GBM, our method does not reconstruct the subsurface snapshots, which has higher efficiency. But the proposed method is not as efficient as the conventional Gaussian beam migration. Synthetic and field data examples demonstrate the validity and the target-oriented imaging capability of our method.

Least-squares Gaussian beam migration in viscoacoustic media

2019 ◽  
Author(s):  
Yubo Yue ◽  
Pengyuan Sun ◽  
Jianlei Zhang ◽  
Shihu Wang ◽  
Jun Liao ◽  
...  
Keyword(s):  
Least Squares ◽  
Gaussian Beam ◽  
Gaussian Beam Migration

A True-Amplitude Imaging Method Based on Gaussian Beam Migration and Demigration

2020 ◽  
Vol 177 (10) ◽  
pp. 4707-4718
Author(s):  
Shaoyong Liu ◽  
Rushan Wu ◽  
Bo Feng ◽  
Huazhong Wang ◽  
Song Guo
Keyword(s):  
Gaussian Beam ◽  
Imaging Method ◽  
Gaussian Beam Migration

scholarly journals Least-squares Gaussian beam migration

2017 ◽  
Vol 14 (1) ◽  
pp. 184-196 ◽  
Author(s):  
Maolin Yuan ◽  
Jianping Huang ◽  
Wenyuan Liao ◽  
Fuyou Jiang
Keyword(s):  
Least Squares ◽  
Gaussian Beam ◽  
Gaussian Beam Migration

Gaussian beam imaging of fractures near the wellbore using sonic logging tools after removing dispersive borehole waves

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. D133-D143
Author(s):  
David Li ◽  
Xiao Tian ◽  
Hao Hu ◽  
Xiao-Ming Tang ◽  
Xinding Fang ◽  
...  
Keyword(s):  
Gaussian Beam ◽  
Field Data ◽  
Gas Production ◽  
Dipole Source ◽  
Data Sets ◽  
Data Set ◽  
Receiver Array ◽  
Gaussian Beam Migration ◽  
Sonic Logging ◽  
Borehole Waves

The ability to image near-wellbore fractures is critical for wellbore integrity monitoring as well as for energy production and waste disposal. Single-well imaging uses a sonic logging instrument consisting of a source and a receiver array to image geologic structures around a wellbore. We use cross-dipole sources because they can excite waves that can be used to image structures farther away from the wellbore than traditional monopole sources. However, the cross-dipole source also will excite large-amplitude, slowly propagating dispersive waves along the surface of the borehole. These waves will interfere with the formation reflection events. We have adopted a new fracture imaging procedure using sonic data. We first remove the strong amplitude borehole waves using a new nonlinear signal comparison method. We then apply Gaussian beam migration to obtain high-resolution images of the fractures. To verify our method, we first test our method on synthetic data sets modeled using a finite-difference approach. We then validate our method on a field data set collected from a fractured natural gas production well. We are able to obtain high-quality images of the fractures using Gaussian beam migration compared with Kirchhoff migration for the synthetic and field data sets. We also found that a low-frequency source (around 1 kHz) is needed to obtain a sharp image of the fracture because high-frequency wavefields can interact strongly with the fluid-filled borehole.

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