Iterative deblending of simultaneous-source data using smoothed singular spectrum analysis

Simultaneous-source acquisition helps the field crews obtain a faster seismic data recording by allowing multiple sources to be fired simultaneously. However, the simultaneous source ignition causes strong interferences in the recorded data, which greatly affects the subsequent seismic data processing and imaging workflows. An effective separation of simultaneous sources is considered as the key in successfully utilizing the simultaneous-source acquisition technology. In this paper, we propose a novel smoothed singular spectrum analysis (SSA) approach to remove blending noise in an iterative inversion manner. Compared with the traditional SSA approach, the smoothed SSA approach applies a Gaussian smoothing operator onto the Hankel matrix in the frequency domain, and can attenuate more blending noise than the traditional SSA method. We use both synthetic and field data examples to demonstrate the successful performance of the proposed method. (C) 2018 Published by Elsevier B.V.