Time-jittered marine acquisition: low-rank v/s sparsity

Time-jittered simultaneous marine acquisition has been recognized as an economic way of improving the spatial sampling, and speedup acquisition, where a single (or multiple) source vessel fires at – jittered source locations and instances in time. It has been shown in the past that this problem can be setup as a – compressed sensing problem, where conventional seismic data is reconstructed from blended data via a sparsity-promoting optimization formulation. While the recovery quality of deblended data is very good, the recovery process is computationally very expensive. In this paper, we present a computationally efficient rank-minimization algorithm to deblend the seismic data. The proposed algorithm is suitable for large-scale seismic data, since it avoids SVD computations and uses a low-rank factorized formulation instead. Results are illustrated with simulations of time-jittered marine acquisition, which translates to jittered source locations for a given speed of the source vessel, for a single source vessel with two airgun arrays.