With the rapid development of deep learning technologies, data-driven methods have become one of the main research focuses in geophysical inversion. Applications of various neural network architectures to the inversion of seismic, electromagnetic, gravity and other types of data confirm the potential of these methods in real-time parameter estimation without dependence on the starting subsurface model. At the same time, deep learning methods require large training datasets which are often difficult to acquire. In this paper, we present a generator of 2D subsurface models based on deep generative adversarial networks. Several networks are trained separately on realistic density and stratigraphy models to reach a sufficient degree of accuracy in generation of new highly detailed and varied models in real-time. This allows for creation of large synthetic training datasets in a cost-effective manner, thus facilitating the development of better deep learning algorithms for real-time inversion and interpretation.