Abstract
Seismic deblending is an ill-posed inverse problem that involves counteracting the effect of a blending matrix derived from the shots' position and firing time. In this letter, we propose a seismic deblending method based on so-called deep preconditioners. A convolutional autoencoder (AE) is first trained in a patch-wise fashion to learn an effective sparse representation of the common receiver gathers (CRGs) we aim to reconstruct. Then, the decoder branch of the trained AE is used as a nonlinear preconditioner for the deblending problem. Particularly, to avoid the explicit creation of a training dataset, we suggest to use the common shot gathers (CSGs) of the blended dataset itself to train the AE network, as they are not affected by incoherent blending noise. Numerical examples on synthetic and field datasets demonstrate the effectiveness of the proposed method in comparison to significantly comparable techniques: a dictionary-learning-based deblending method; an end-to-end deblending convolution neutral network (CNN).