Intelligent shot-gather reconstruction using residual learning networks

The minimum distance between adjacent shots is always “larger” than that of adjacent receivers in each shot, and the inconsistency can decrease the performance of migration. Interpolation methods can improve the data consistency, while most methods are suitable for randomly missing cases, and the interpolation difficulty increases sharply for regularly missing cases, especially for the cases with a big gap. As deep learning has a strong self-learning ability in nonlinear characterization, we introduce it into shot gather reconstruction. First, the residual learning networks (ResNets) with better back propagation property is illustrated, and the self-similarity of the common shot and receiver gathers are analyzed theoretically followed by briefly discussing the interpolation issue. Then, the common shot gathers are divided into the training sets to train ResNets and the validation sets to verify the trained ResNets. Finally, the trained ResNets is used to reconstruct the missing shot gathers in the common receiver gather. Three different datasets are used to demonstrate the reconstruction validity of the proposed deep learning strategy. The reconstructed data with better consistency can improve the accuracy of the final reservoir characterization. Presentation Date: Tuesday, October 16, 2018 Start Time: 1:50:00 PM Location: 204B (Anaheim Convention Center) Presentation Type: Oral