Least-squares Gaussian beam migration in time-space domain

Author(s):  
Jidong Yang ◽  
Hejun Zhu
2019 ◽  
Author(s):  
Yubo Yue ◽  
Pengyuan Sun ◽  
Jianlei Zhang ◽  
Shihu Wang ◽  
Jun Liao ◽  
...  

2017 ◽  
Vol 14 (1) ◽  
pp. 184-196 ◽  
Author(s):  
Maolin Yuan ◽  
Jianping Huang ◽  
Wenyuan Liao ◽  
Fuyou Jiang

Geophysics ◽  
2020 ◽  
Vol 86 (1) ◽  
pp. S17-S28
Author(s):  
Yubo Yue ◽  
Yujin Liu ◽  
Yaonan Li ◽  
Yunyan Shi

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.


Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. S87-S100 ◽  
Author(s):  
Hao Hu ◽  
Yike Liu ◽  
Yingcai Zheng ◽  
Xuejian Liu ◽  
Huiyi Lu

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.


Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. S329-S340 ◽  
Author(s):  
Yubo Yue ◽  
Paul Sava ◽  
Zhongping Qian ◽  
Jidong Yang ◽  
Zhen Zou

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.


Geophysics ◽  
2013 ◽  
Vol 78 (4) ◽  
pp. T113-T132 ◽  
Author(s):  
Yang Liu

Spatial finite-difference (FD) coefficients are usually determined by the Taylor-series expansion (TE) or optimization methods. The former can provide high accuracy on a smaller wavenumber or frequency zone, and the latter can give moderate accuracy on a larger zone. Present optimization methods applied to calculate FD coefficients are generally gradient-like or global optimization-like algorithms, and thus iterations are involved. They are more computationally expensive, and sometimes the global solution may not be found. I examined second-order spatial derivatives and computed the optimized spatial FD coefficients over the given wavenumber range using the least-squares (LS) method. The results indicated that the FD accuracy increased with increasing operator length and decreasing wavenumber range. Therefore, for the given error and operator length, globally optimal spatial FD coefficients can be easily obtained. Some optimal FD coefficients were given. I developed schemes to obtain optimized LS-based spatial FD coefficients by minimizing the relative error of space-domain dispersion relation for second-order derivatives and time-space-domain dispersion relation for the acoustic wave equation. I discovered that minimizing the relative error of the space-domain dispersion relation provides less phase velocity error for small wavenumbers, compared to minimizing the absolute error. I also found that minimizing the relative error of the time-space-domain dispersion relation can reduce relative errors of phase velocity. Accuracy analysis demonstrated the correctness and advantage of schemes. I gave three examples of 2D acoustic FD modeling for a homogeneous, a large velocity-contrast, and a heterogeneous model, respectively. LS-based spatial FD operators have variable lengths for different velocities. Modeling examples demonstrated that the proposed LS-based FD scheme can maintain the same modeling precision while using a shorter spatial FD operator length, thus reducing the computation cost relative to conventional TE-based FD schemes, particularly for the higher order.


2015 ◽  
Author(s):  
Hao Hu* ◽  
Yike Liu ◽  
Xuejian Liu ◽  
Huiyi Lu ◽  
Yingcai Zheng

2018 ◽  
Author(s):  
Yubo Yue ◽  
Shaohua Zhang ◽  
Yunyan Shi ◽  
Na Lei ◽  
Yonglai Yu

2021 ◽  
Author(s):  
Jidong Yang ◽  
Jianping Huang ◽  
Zhenchun Li ◽  
Hejun Zhu ◽  
George McMechan

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