Deblending and Recovery of Incomplete Blended Data via MultiResUnet

Blended acquisition is still open to improve the efficiency of seismic data acquisition. Deblending is an essential procedure to provide separated gathers for subsequent migration and inversion to characterize subsurface medium. Because of missing data, the incompleteness of blended data poses challenges for accurate deblending. Besides, the computational cost increases sharply as the increasing volume of seismic data and traditional deblending algorithms only characterize seismic data in a linear manner. To improve the deblending efficiency and accuracy, deep learning algorithms have drawn much attention to express seismic data in a nonlinear manner via supervised learning as the fast development of Graphic Processing Unit. For field cases with blending noise and sparseness contamination, we design an innovative workflow for deblending and recovery of incomplete blended data via multi-level blending noise assisted multiresolution ResUnet (MultiResUnet). The MultiResUnet combines the advantages of ResNet and Unet to characterize seismic data accurately. The designed network is trained in a supervised manner in the common receiver domain (CRD) for joint deblending and missing shot information reconstruction with multi-level blending noise to mimic the blending noise level during iterative deblending. For the reconstruction of missing receiver information, the training data in the CRD is used again to fine-tune the trained network for the pure reconstruction issue. The fine-tuned MultiResUnet is implemented for the common shot gathers (CSGs) with the projection onto convex sets strategy to reconstruct the information of missing receivers. The reconstruction accuracy can be guaranteed because the training common receiver gathers have similar features with the test CSGs. To process seismic data with different features, the trained network can be regarded as initialization based on transfer learning. Synthetic data and field data numerical examples demonstrate the validity of the proposed innovative workflow to separate blended data and reconstruct the information of missing shots and receivers. Article Highlights Joint deblending and reconstruction of missing shots are iteratively achieved in the common receiver gather based on the designed MultiResUnet with multi-level blending noise The missing receiver information is recovered in the common shot gather based on transfer learning Numerical examples of synthetic data and field data demonstrate the validity of