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 3D simultaneous source data is based on the energy difference between blending noise and useful signals in the sparse domain, and remove blending noise independently from each 2D partition of 3D simultaneous source data using sparse inversion algorithm. However, this 3D deblending techniques do not fully utilize a priori information derived from the locations of the spares coefficients from different partitions, especially when highly redundant curvelet domain sparsity is used. In this paper, we propose to use the priori support information to improve the deblending performance of 3D simultaneous source data by using 2D curvelet transform. Firstly, 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 to delineate the main energy distribution range of the useful signals, and define a sequence of 2D curvelet-based deblending problems that exploit 3D correlations exhibited by seismic wavefields without using the highly redundant 3D 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.