To separate seismic interference (SI) noise while ensuring high signal fidelity, we have developed a deep neural network (DNN)-based workflow applied to common-shot gathers (CSGs). In our design, a small subset of the entire to-be-processed data set is first processed by a conventional algorithm to obtain an estimate of the SI noise (from now on called the SI noise model). By manually blending the SI noise model with SI-free CSGs and a set of simulated random noise, we obtain training inputs for the DNN. The SI-free CSGs can be either real SI-free CSGs from the survey or SI-attenuated CSGs produced in parallel with the SI noise model from the conventional algorithm depending on the specific project. To enhance the DNN’s output signal fidelity, adjacent shots on both sides of the to-be-processed shot are used as additional channels of the input. We train the DNN to output the SI noise into one channel and the SI-free shot along with the intact random noise into another. Once trained, the DNN can be applied to the entire data set contaminated by the same types of SI in the training process, producing results efficiently. For demonstration, we applied our DNN-based workflow to 3D seismic field data acquired from the northern Viking Graben of the North Sea and compared it with a conventional algorithm. The studied area has a challenging SI contamination problem with no sail lines free from SI noise during the acquisition. The comparison finds that our DNN-based workflow outperformed the conventional algorithm in processing quality with less noise residual and better signal preservation. This validates its feasibility and value for real processing projects.@en