Seismic trace interpolation based on the principle of reciprocity using a conditional generative adversarial network (cGAN)

Abstract Seismic trace interpolation is a common step in data processing used to fill in missing traces and regularize acquisition grids. In the ocean bottom node (OBN) acquisition, it is common practice to design seismic surveys with many sources and fewer receivers. However, it results in a loss of lateral resolution in the shot-gather domain, although it increases the lateral resolution in the receiver-gather domain. In this paper, we propose to utilize the information from the receiver-gather domain to enhance the lateral resolution in the shot-gather domain, based on the principle of reciprocity. Explicitly, we use receiver gathers to train a conditional generative adversarial network (cGAN), called Pix2Pix, and obtain a generative interpolator, which can be used in the shot-gather domain. We present an iterative workflow to perform seismic data interpolation in the shot-gather domain and illustrate the workflow using a synthetic 2D data model consisting of densely populated sources and sparse receivers. We also present a field OBN data example from a Brazilian pre-salt area. The workflow can be used to increase lateral resolution or even complete the geometry of a monitor acquisition to match the baseline. In contrast to conventional methods, this iterative GAN interpolation does not lead to artefacts in the case of larger filling gaps.