Imaging incomplete data and simultaneous-source data using least-squares reverse-time migration with shaping regularization

2014 ◽  
Author(s):  
Zhiguang Xue* ◽  
Yangkang Chen ◽  
Sergey Fomel ◽  
Junzhe Sun
Keyword(s):  
Least Squares ◽  
Incomplete Data ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Source Data ◽  
Simultaneous Source

scholarly journals Seismic imaging of incomplete data and simultaneous-source data using least-squares reverse time migration with shaping regularization

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. S11-S20 ◽  
Author(s):  
Zhiguang Xue ◽  
Yangkang Chen ◽  
Sergey Fomel ◽  
Junzhe Sun
Keyword(s):  
Least Squares ◽  
Incomplete Data ◽  
Gradient Algorithm ◽  
Reverse Time ◽  
Crosstalk Noise ◽  
Reverse Time Migration ◽  
Direct Imaging ◽  
Time Migration ◽  
Source Data ◽  
Simultaneous Source

Simultaneous-source acquisition improves the efficiency of the seismic data acquisition process. However, direct imaging of simultaneous-source data may introduce crosstalk artifacts in the final image. Likewise, direct imaging of incomplete data avoids the step of data reconstruction, but it can suffer from migration artifacts. We have proposed to incorporate shaping regularization into least-squares reverse time migration (LSRTM) and use it for suppressing interference noise caused by simultaneous-source data or migration artifacts caused by incomplete data. To implement LSRTM, we have applied lowrank one-step reverse time migration and its adjoint iteratively in the conjugate-gradient algorithm to minimize the data misfit. A shaping operator imposing structure constraints on the estimated model was applied at each iteration. We constructed the shaping operator as a structure-enhancing filtering to attenuate migration artifacts and crosstalk noise while preserving structural information. We have carried out numerical tests on synthetic models in which the proposed method exhibited a fast convergence rate and was effective in attenuating migration artifacts and crosstalk noise.

Substituting smoothing with low-rank decomposition — Applications to least-squares reverse time migration of simultaneous source and incomplete seismic data

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. S267-S283 ◽  
Author(s):  
Yangkang Chen ◽  
Min Bai ◽  
Yatong Zhou ◽  
Qingchen Zhang ◽  
Yufeng Wang ◽  
...  
Keyword(s):  
Inverse Problem ◽  
Least Squares ◽  
Low Rank ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Decomposition Operator ◽  
Rank Decomposition ◽  
Simultaneous Source ◽  
Low Rank Decomposition

Seismic migration can be formulated as an inverse problem, the model of which can be iteratively inverted via the least-squares migration framework instead of approximated by applying the adjoint operator to the observed data. Least-squares reverse time migration (LSRTM) has attracted more and more attention in modern seismic imaging workflows because of its exceptional performance in obtaining high-resolution true-amplitude seismic images and the fast development of the computational capability of modern computing architecture. However, due to a variety of reasons, e.g., insufficient shot coverage and data sampling, the image from least-squares inversion still contains a large amount of artifacts. This phenomenon results from the ill-posed nature of the inverse problem. In traditional LSRTM, the minimum least-squares energy of the model is used as a constraint to regularize the inverse problem. Considering the residual noise caused by the smoothing operator in traditional LSRTM, we regularize the model using a powerful low-rank decomposition operator, which can better suppress the migration artifacts in the image during iterative inversion. We evaluate in detail the low-rank decomposition operator and the way to apply it along the geologic structure of seismic reflectors. We comprehensively analyze the performance of our algorithm in attenuating crosstalk noise caused by simultaneous source acquisition and migration artifacts caused by insufficient space sampling via two synthetic examples and one field data example. Our results indicate that compared to the conventional smoothing operator, our low-rank decomposition operator can help obtain a cleaner LSRTM image and obtain a slightly better edge-preserving performance.

Preserving the discontinuities in least-squares reverse time migration of simultaneous-source data

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. S185-S196 ◽  
Author(s):  
Yangkang Chen ◽  
Hanming Chen ◽  
Kui Xiang ◽  
Xiaohong Chen
Keyword(s):  
Least Squares ◽  
Singular Spectrum Analysis ◽  
Low Rank ◽  
Superior Performance ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Seismic Image ◽  
Recorded Data ◽  
Simultaneous Source

The simultaneous-source shooting technique can accelerate field acquisition and improve spatial sampling but it will cause strong interferences in the recorded data and artifacts in the final image. The previously proposed structural smoothing operator can effectively attenuate artifacts for relatively simple reflection structures during least-squares inversion, but it will cause damage to complicated reflection events such as discontinuities. To preserve discontinuities in a seismic image, we apply the singular spectrum analysis (SSA) operator to attenuate artifacts during least-squares inversion. Considering that global SSA cannot deal with overcomplicated data very well, we use local SSA to remove noise and to better preserve the steeply dipping components. The local SSA operator corresponds to a local low-rank constraint applied in the inversion process. The migration operator used in the study is the reverse time migration (RTM) operator. Tests using the Marmousi model showed the superior performance of the proposed algorithm in preserving the discontinuities of seismic images.

scholarly journals A GPU implementation of least-squares reverse time migration

2021 ◽  
Vol 1719 (1) ◽  
pp. 012030
Author(s):  
Phudit Sombutsirinun ◽  
Chaiwoot Boonyasiriwat
Keyword(s):  
Least Squares ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Gpu Implementation
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