Enhanced adaptive subtraction method for simultaneous source separation
Zhaojun Liu, Bin Wang, Jim Specht, Jeffery Sposato, Yongbo Zhai · 2014
We have developed an iterative adaptive subtraction method to separate shot blended data. The model of coherent events is determined and alternately adaptively subtracted from the primary and secondary sets of shot data in their respective common offset gathers. We add the residual from a given iteration to the other set of shot data for the next iteration. This iterative process reconstructs the deblended data of both sets of shots simultaneously. We illustrate our method by generating simulated simultaneous source data using the synthetic Marmousi data. The results show little crosstalk remaining after deblending and the migration images are very similar to those of the unblended data.