Deblending of Simultaneous-Source Seismic Data Using Bregman Iterative Shaping

Deblending plays an important role in preparing high-quality seismic data from modern blended simultaneous-source seismic data. The conventional methods become problematic when the random ambient noise becomes extremely strong and the inversion iteratively fits the random noise instead of the signal and blending interference. In this article, an improved method is proposed to separate blended seismic data. We treat the deblending problem as a regularization problem under the frame of compressed sensing (CS), and propose a new nonlinear Bregman iterative shaping (BIS) algorithm to solve the minimization problem. Based on the principle of CS, we transform the simultaneous-source data to the seislet-domain for inversion and separation, and verify the effectiveness and superiority of the proposed method by two theoretical model data and an actual marine streamer blended data. Compared with the traditional iterative shaping (TIS) algorithm, BIS adds data residuals to the observed data (the blended data) in each iteration, so that the error between the calculated data after each iteration and the main source signal in the known blended data is the smallest, and helps to recover the weaker information in the seismic data and obtain higher precision separation results. BIS uses a fixed soft threshold, which speeds up the convergence speed. In addition, BIS can effectively improve the separation accuracy when the given sparse domain threshold is not suitable.