Recovery of seismic wavefields based on compressive sensing by an l1-norm constrained trust region method and the piecewise random subsampling

By Yanfei Wang, Jingjie Cao, Changchun Yang. Geophysical Journal International, 2011

79 citations recorded by the source corpus.

SUMMARY Due to the influence of variations in landform, geophysical data acquisition is usually subsampled. Reconstruction of the seismic wavefield from subsampled data is an ill-posed inverse problem. Compressive sensing (CS) can be used to recover the original geophysical data from the subsampled data. In this paper, we consider the wavefield reconstruction problem as a CS and propose a piecewise random subsampling scheme based on the wavelet transform. The proposed sampling scheme overcomes the disadvantages of uncontrolled random sampling. In computation, an l1-norm constrained trust region method is developed to solve the CS problem. Numerical results demonstrate that the proposed sampling technique and the trust region approach are robust in solving the ill-posed CS problem and can greatly improve the quality of wavefield recovery.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Geophysics

View Paper: https://www.semanticscholar.org/paper/e7df57478ab4a509bbf8048e9d2634a8653721dc

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In Computational Mechanics

In Computer Vision and Pattern Recognition

In Geophysics

Citations in the corpus

Seismic data restoration via data-driven tight frame (2014, background): “…2003; Liu and Sacchi, 2004; Xu et al., 2005; Abma and Kabir, 2006; Trad, 2009), seislet transform (Fomel and Liu, 2010; Liu and Fomel, 2010), and curvelet transform (Hennenfent and Herrmann, 2008; Herrmann and Hennenfent, 2008; Naghizadeh and Sacchi, 2010; Wang et al., 2011; Shahidi et al., 2013).”