Iterative deblending of simultaneous-source data using a coherency-pass shaping operator

Simultaneous-source acquisition helps greatly boost an economic saving, while it brings an unprecedented challenge of removing the crosstalk interference in the recorded seismic data. In this paper, we propose a novel iterative method to separate the simultaneous source data based on a coherency-pass shaping operator. The coherency-pass filter is used to constrain the model, that is, the unblended data to be estimated, in the shaping regularization framework. In the simultaneous source survey, the incoherent interference from adjacent shots greatly increases the rank of the frequency domain Hankel matrix that is formed from the blended record. Thus, the method based on rank reduction is capable of separating the blended record to some extent. However, the shortcoming is that it may cause residual noise when there is strong blending interference. We propose to cascade the rank reduction and thresholding operators to deal with this issue. In the initial iterations, we adopt a small rank to severely separate the blended interference and a large thresholding value as strong constraints to remove the residual noise in the time domain. In the later iterations, since more and more events have been recovered, we weaken the constraint by increasing the rank and shrinking the threshold to recover weak events and to guarantee the convergence. In this way, the combined rank reduction and thresholding strategy acts as a coherency-pass filter, which only passes the coherent high-amplitude component after rank reduction instead of passing both signal and noise in traditional rank reduction based approaches. Two synthetic examples are tested to demonstrate the performance of the proposed method. In addition, the application on two field data sets common receiver gathers and stacked profiles) further validate the effectiveness of the proposed method.