Simultaneous Sources Separation via an Iterative Rank-Increasing Method

Abstract

Simultaneous sources acquisition attracts intensive attention from both academia and industry due to its greatly improved efficiency in acquiring high-density seismic data. Unfortunately, its merits are compromised by the strong interference noise between adjacent shots. In this letter, we propose a stepwise rank-increasing (RI) method to estimate the crosstalk noise in simultaneous sources acquisition. The proposed algorithm assumes that an ideal common offset gather (COG) can be represented via a low-rank matrix in the time-space domain. The coherent signals are estimated from low-rank decomposition and transformed to the crosstalk noise by employing a priori information about random dithering code, and then the blending noise is subtracted from the blended data. By increasing the rank of coherent signals step-by-step, the crosstalk noise can be gradually estimated with high accuracy. In this letter, singular value decomposition is utilized to increase the rank of COG data. Applications on synthetic and field data sets demonstrate the better performance of the proposed RI method not only by more effectively suppressing noise but also by accelerating the convergence rate.