Preconditioned Least-squares reverse-time migration with well-constrain regularization

Liu, Mengli; Huang, Jianping; Li, Zhenchun; Li, Chuang; Ren, Yingjun

doi:10.1190/igc2017-262

Cited by 1 publication

(1 citation statement)

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“…Both RLSRTM and LSRTM are performed iteratively using the preconditioned conjugate-gradient algorithm (Nemeth et al 1999 ). Two preconditioners, illumination compensation (Plessix and Mulder 2004 ; Li et al 2016b ) and high-pass filtering (Li et al 2016b ), are employed to improve the migration results.…”

Section: Methodsmentioning

confidence: 99%

Regularized least-squares migration of simultaneous-source seismic data with adaptive singular spectrum analysis

Huang

et al. 2016

Pet. Sci.

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Simultaneous-source acquisition has been recognized as an economic and efficient acquisition method, but the direct imaging of the simultaneous-source data produces migration artifacts because of the interference of adjacent sources. To overcome this problem, we propose the regularized least-squares reverse time migration method (RLSRTM) using the singular spectrum analysis technique that imposes sparseness constraints on the inverted model. Additionally, the difference spectrum theory of singular values is presented so that RLSRTM can be implemented adaptively to eliminate the migration artifacts. With numerical tests on a flat layer model and a Marmousi model, we validate the superior imaging quality, efficiency and convergence of RLSRTM compared with LSRTM when dealing with simultaneous-source data, incomplete data and noisy data.

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