Time-domain least-squares migration using the Gaussian beam summation method

2018 ◽  
Vol 214 (1) ◽  
pp. 548-572 ◽  
Author(s):  
Jidong Yang ◽  
Hejun Zhu ◽  
George McMechan ◽  
Yubo Yue
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 ◽  
2021 ◽  
pp. 1-37
Author(s):  
Ram Tuvi ◽  
Zeyu Zhao ◽  
Mrinal Kanti Sen

We consider the problem of image-domain least-squares migration based on efficiently constructing the Hessian matrix with sparse beam data. Specifically, we use the ultra-wide-band phase space beam summation method, where beams are used as local basis functions to represent scattered data collected at the surface. The beam domain data are sparse. One can identify seismic events with significant contributions so that only beams with non-negligible amplitudes need to be used to image the subsurface. In addition, due to the beams' spectral localization, only beams that pass near an imaging point need to be taken into account. These two properties reduce the computational complexity of computing the Hessian matrix - an essential ingredient for least-squares migration. As a result, we can efficiently construct the Hessian matrix based on analyzing the sparse beam domain data.


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

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

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