Sparse solutions of linear systems of equations are essential in many applications of seismic data processing. These systems arise in many denoising algorithms, such as those that use Radon transforms. We have developed a robust matching pursuit (RMP) algorithm for the retrieval of sparse Radon domain coefficients. The algorithm is robust to outliers and, hence, is applicable for seismic data deblending. The classic matching pursuit (MP) algorithm is often adopted to approximate data by a small number of basis functions. It performs effectively for data contaminated with well-behaved, typically Gaussian, random noise. However, MP tends to identify the wrong basis functions when the data are contaminated by erratic noise such as source interference encountered in common-receiver and common-channel gathers of simultaneous source surveys. Incorporating an lp space inner product into the MP algorithm significantly increases its robustness to erratic signals. Deblending experiments with synthetic and field data examples indicate a significant signal-to-noise ratio improvement when one adopts a Radon denoiser computed via our RMP solver. We determine in detail the steps required to implement our lp space RMP algorithm when the basis functions are not given in an explicit form, as is the case with the time-domain Radon transform.

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