Seismic Intelligent Deblending via Plug and Play Method With Blended CSGs Trained Deep CNN Gaussian Denoiser

Deblending can extract good quality seismic data from blended seismic data. Generally, the deblending methods can be categorized as model- and data-driven methods. The model-driven deblending methods usually suffer from a massive computational burden, while the data-driven ones need to implement a forward blending process to construct training samples for network training. To solve these issues, we develop a plug and play (PnP) method that integrates a trained convolutional neural network (CNN) Gaussian denoiser as the prior for seismic intelligent deblending. Specifically, we propose to use acquired blended common shot gathers (CSGs) as training dataset for the CNN Gaussian denoiser training to avoid constructing or collecting any extra data. According to the theory and dedicated designed experiments, this training mode can greatly improve network performance. Then, the trained CNN Gaussian denoiser is plugged into the alternating direction method of multiplier (ADMM) algorithm to solve the deblending problem. Furthermore, based on the
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norm data fidelity term and the special structure of the network architecture, the PnP-ADMM method converges to a fixed point. Experiments on synthetic and field data demonstrate that the presented PnP method with blended CSGs trained deep CNN Gaussian denoiser has superior deblending performances over the dictionary learning and discriminative deblending CNN methods.