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 with significantly comparable techniques: a dictionary-learning based deblending method; an end-to-end deblending convolution neutral network (CNN).
Bibliographical noteKAUST Repository Item: Exported on 2022-09-14
Acknowledgements: This work was supported by the National Key R&D Program of China under Grant 2021YFA0716904, the National Natural Science Foundation of China under Grant 41974131 and Grant 41774135, and the China Scholarship Council. GPU computation has been made available thanks to the GPU Grant Program by NVIDIA Corporation, to which the authors express deep gratitude.
ASJC Scopus subject areas
- Geotechnical Engineering and Engineering Geology
- Electrical and Electronic Engineering