Re‐Exploration into Migration of Seismic Data

Chen, Sheng‐Chang; Hua‐Ming, Zhou

doi:10.1002/cjg2.20213

Cited by 6 publications

(2 citation statements)

References 39 publications

(29 reference statements)

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“…Where obs d represents the observation data, m manifests the model parameter, and L is the inverse migration operator. Here, we employ reverse time demigration for data reconstruction [20], and the formula can be described as follows:…”

Section: Basic Principles Of Lsrtmmentioning

confidence: 99%

Research on the Improved Least Squares Reverse Time Migration Imaging Method

Zhang

Liu

et al. 2021

E3S Web Conf.

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An improved Least Squares Reverse Time Migration (LSRTM) method is proposed in the paper, which can effectively improve convergence speed and imaging accuracy. Firstly, the key techniques in the implementation of LSRTM are discussed. Secondly, a condition factor is introduced in the iteration process of conjugate gradient method. Finally, the imaging effect and performance of the algorithm are analyzed. The experiment results indicate that it can speed up the convergence speed and improve the convergence accuracy, so as to improve the imaging effect. Compared with the conventional LSRTM, the data residual of improved LSRTM can be reduced by about 5%.

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Section: Basic Principles Of Lsrtmmentioning

confidence: 99%

Research on the Improved Least Squares Reverse Time Migration Imaging Method

Zhang

Liu

et al. 2021

E3S Web Conf.

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“…With the development of oil and gas exploration, it poses greater challenges to traditional migration imaging methods for underground structure imaging 1 , 2 . In order to improve imaging accuracy and amplitude fidelity, researchers proposed least squares reverse time migration (LSRTM) 3 , 4 .…”

Section: Introductionmentioning

confidence: 99%

Least squares reverse time migration imaging with illumination preconditioned based on improved PRP conjugate gradients

Zhang,

Li,

Cui

et al. 2023

Sci Rep

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Least squares reverse time migration (LSRTM) imaging is the one of the most accurate methods for migration imaging at present, and Polak–Ribiere–Polyak conjugate gradient (PRPCG) for LSRTM has the good numerical performance but weak convergence, so we construct an optimization factor to improve the iteration direction of the gradient, which can automatically generate a sufficient descent direction. The improved PRPCG (IPRPCG) can reduce the data residual values and speed up the iteration. And the illumination preconditioned (IP) operator is employed to IPRPCG-LSRTM which solves the problem of low resolution due to the insufficient iterative gradient information. In this paper, the experiments show that the imaging results of the proposed method (IPRPCG-IP-LSRTM) is improved greatly in detail characterization and events continuity, the iterative curve converged faster significantly, and the normalized data residual was reduced by 6.55% on average, which improved the accuracy of migration imaging effectively.

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