Abstract
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.
Highlights
A fundamental factor considered in seismic data acquisition is efficiency
Motivated by the excellent denoising performance of singular spectrum analysis (SSA) (Sacchi 2009; Oropeza and Sacchi 2010, 2011; Huang et al 2014), we propose to incorporate a regularization term using SSA into leastsquares reverse time migration (LSRTM) that eliminates migration artifacts caused by simultaneous-source data, incomplete data and noisy data
Even for a large size Hankel matrix, such as three-dimensional cases, it has been proven that dividing the data into small cubes and adopting the randomized singular value decomposition (RSVD) to perform the SVD can significantly improve the computational efficiency (Rokhlin et al 2009; Oropeza and Sacchi 2010, 2011)
Summary
A fundamental factor considered in seismic data acquisition is efficiency. Simultaneous-source acquisition uses simultaneous shooting of two or more sources, resulting in the advantages of high efficiency and allowing denser. Within angle-domain common-image gathers, Kuehl and Sacchi (2003) propose to use a smoothing operator along the ray parameter axis to suppress migration artifacts. This approach can be implemented with structure-preserving constraints to improve the migration results (Wang and Sacchi 2009). Motivated by the excellent denoising performance of singular spectrum analysis (SSA) (Sacchi 2009; Oropeza and Sacchi 2010, 2011; Huang et al 2014), we propose to incorporate a regularization term using SSA into leastsquares reverse time migration (LSRTM) that eliminates migration artifacts caused by simultaneous-source data, incomplete data and noisy data. The numerical tests demonstrate the validity and superiority of the proposed method
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