A deep-learning-based compressive-sensing technique for reconstruction of missing seismic traces is introduced. The agility of the proposed approach lies in its ability to perfectly resolve the optimization limitation of conventional algorithms that solve inversion problems. It demonstrates how deep generative adversarial networks, equipped with an appropriate loss function that essentially leverages the distribution of the entire survey, can serve as an alternative approach for tackling compressive-sensing problems with high precision and in a computationally efficient manner. The method can be applied on both prestack and poststack seismic data, allowing for superior imaging quality with well-preconditioned and well-sampled field data, during the processing stage. To validate the robustness of the proposed approach on field data, the extent to which amplitudes and phase variations in original data are faithfully preserved is established, while subsurface consistency is also achieved. Several applications to acquisition and processing, such as decreasing bin size, increasing offset and azimuth sampling, or increasing the fold, can directly and immediately benefit from adopting the proposed technique. Furthermore, interpolation based on generative adversarial networks has been found to produce better-sampled data sets, with stronger regularization and attenuated aliasing phenomenon, while providing greater fidelity on steep-dip events and amplitude-variation-with-offset analysis with migration.