High-Precision Deblending of 3-D Simultaneous Source Data Based on Prior Information Constraint

How to effectively remove blending noise is the key to the success of simultaneous source acquisition. The traditional deblending of 3-D simultaneous source data is based on the energy difference between blending noise and useful signals in the sparse domain and removes blending noise independently of each 2-D partition of 3-D simultaneous source data using a sparse inversion algorithm. However, these 3-D deblending techniques do not fully utilize a priori information derived from the locations of the spare coefficients from different partitions, especially when highly redundant curvelet domain sparsity is used. In this letter, we propose to use the priori support information to improve the deblending performance of 3-D simultaneous source data by using a 2-D curvelet transform. First, based on the correlations between the support sets of the significant curvelet coefficients of adjacent partitions, a weighted operator is constructed in the curvelet domain using the prior support sets provided by the previous partition that has removed blending noise. Then, incorporate it into the iterative shrinkage thresholding algorithm (ISTA) to delineate the main energy distribution range of the useful signals, and define a sequence of 2-D curvelet-based deblending problems that exploit 3-D correlations exhibited by seismic wavefields without using the highly redundant 3-D curvelet transform. To test the performance of our proposed method, we compared deblending from synthetic and field artificially blended data. These examples demonstrated that our method is superior to the traditional iterative algorithm in terms of deblending performance, and computational memory requirements.