Separation of simultaneous source data via iterative rank reduction
Jinkun Cheng, Mauricio D. Sacchi · 2013
In this paper, we report an inversion algorithm based on singular spectrum analysis (SSA) that is capable of suppressing the interferences generated by simultaneous source acquisition. We derive an iterative scheme that adopts the projected gradient method to solve the source separation problem. The projection operator is the SSA rank reduction filter that suppresses incoherent noise in the frequency-space domain. Convergence of this algorithm can be achieved with appropriate choice of step size and an initial starting point. We use synthetic examples simulated with a data set from the Gulf of Mexico to illustrate this method.