The use of deep learning models as priors for compressive sensing tasks presents new potential for inexpensive seismic data acquisition. Conventional recovery usually suffers from undesired artifacts, such as oversmoothing, and high computational cost. Generative adversarial networks (GANs) offer promising alternative approaches that can improve quality and reveal finer details. An appropriately designed Wasserstein GAN trained on several historical surveys and capable of learning the statistical properties of the seismic wavelet's architecture is proposed. The efficiency and precision of this model at compressive sensing are validated in three steps. First, the existence of a sparse representation with different compression rates for seismic surveys is studied. Then, nonuniform samplings are studied using the proposed methodology. Finally, a recommendation is proposed for a nonuniform seismic survey grid based on the evaluation of reconstructed seismic images and metrics. The primary goal of the proposed deep learning model is to provide the foundations of an optimal design for seismic acquisition without a loss in imaging quality. Along these lines, a compressive sensing design of a nonuniform grid over an asset in the Gulf of Mexico, versus a traditional seismic survey grid that collects data uniformly every few feet, is suggested, leveraging the proposed method.