In this paper, we present a new discrete undersampling scheme designed to favor wavefield reconstruction by sparsity-promoting inversion with transform elements that are localized in the Fourier domain. Our work is motivated by empirical observations in the seismic community, corroborated by recent results from compressive sampling, which indicate favorable (wavefield) reconstructions from random as opposed to regular undersampling. As predicted by theory, random undersampling renders coherent aliases into harmless incoherent random noise, effectively turning the interpolation problem into a much simpler denoising problem.
A practical requirement of wavefield reconstruction with localized sparsifying transforms is the control on the maximum gap size. Unfortunately, random undersampling does not provide such a control and the main purpose of this paper is to introduce a sampling scheme, coined jittered undersampling, that shares the benefits of random sampling, while offering control on the maximum gap size. Our contribution of jittered sub-Nyquist sampling proofs to be key in the formulation of a versatile wavefield sparsity-promoting recovery scheme that follows the principles of compressive sampling.
After studying the behavior of the jittered-undersampling scheme in the Fourier domain, its performance is studied for curvelet recovery by sparsity-promoting inversion (CRSI). Our findings on synthetic and real seismic data indicate an improvement of several decibels over recovery from regularly-undersampled data for the same amount of data collected.
Deanna Needell and Roman Vershynin just made available a paper entitled:Signal recovery from incomplete and inaccurate measurements via Regularized Orthogonal Matching Pursuit. Roman Vershynin writes the following commentary on it:
This is our second paper with my student Deanna Needell, where we continue to study alternatives to linear programming methods in Compressed Sensing. In our first paper, we developed a greedy algorihtm called ROMP, which can perfectly recover any sparse signal from a small number of linear non-adaptive measurements. In this paper, we prove the stability of ROMP. It seems to be the first greedy algorithm in Compressed Sensing that is provably stable in the sense that the recovery error is proportional to the level of the noise. In particular, the recovery is exact in the absence of noise. Also, one can use ROMP to accurately recover signals, not necessarily sparse. This kind of stability was known for the linear programming methods (a result of Candes, Romberg and Tao), but has been previously unknown for any greedy method (like OMP).
ROMP is available here. Let us note that like the reweighted Lp algorithm of Rick Chartrand and Wotao Yin, reconstruction is performed by a series of iterations solving a Least Squares problem.