Gradient normalized least-squares reverse-time migration imaging technology
Least-squares reverse-time migration (LSRTM) can overcome the problems of low resolution and unbalanced amplitude energy of deep formation imaging in reverse-time migration (RTM); hence, it can obtain a more accurate imaging profile. In the conventional conjugate gradient LSRTM, the gradient is obtained based on cross correlation without a precondition operator, and the source has a great influence on the gradient, causing the convergence rate to be slow. In the framework of conventional conjugate gradient LSRTM, a normalized cross-correlation of the source wavefield was used in this study to effectively weaken the influence of the source effect and reduce the low-frequency noise. The idea of normalized cross-correlation of the source wavefield was adopted to improve the steepest descent gradient to further accelerate the iterative convergence speed and complete the final migration imaging. Model and field data examples verify the advantages of the proposed methods over conventional methods in reducing source effects, improving convergence speed, and enhancing underground deep illumination.
|WOS Research Area|
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Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||Department of Earth and Space Sciences|
1.College of Marine Geosciences,Ocean University of China,Qingdao,China
2.Key Lab of Submarine Geosciences and Prospecting Techniques,College of Marine Geo Sciences,Ocean University of China Qingdao,Qingdao,China
3.Department of Earth and Space Sciences,Southern University of Science and Technology,Shenzhen,China
Sun，Yanfeng,Xu，Xiugang,Tang，Le. Gradient normalized least-squares reverse-time migration imaging technology[J]. Frontiers in Earth Science,2022,10.
Sun，Yanfeng,Xu，Xiugang,&Tang，Le.(2022).Gradient normalized least-squares reverse-time migration imaging technology.Frontiers in Earth Science,10.
Sun，Yanfeng,et al."Gradient normalized least-squares reverse-time migration imaging technology".Frontiers in Earth Science 10(2022).
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