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Recent breakthroughs in generative models, notably Generative Adversarial Networks (GANs) have unlocked the potential for generating synthetic data in the geosciences. This paper explores the several applications of such models when applied to petrographic, seismic and micropaleontological data. Such models are able to replicate the statistical and visual characteristics of a given dataset, and also allow for modifications and modelling using while exploring the latent space of the given GAN, enabling data augmentation, image editing and also downstream training of models for image classification and segmentation. Addressing key problems in geosciences such as data scarcity, image labelling and modelling.