Seismic imaging is the best possible technology to uncover the Earth's subsurface structures, whereas remote sensing is an indispensable tool for observing and monitoring the Earth's surface. Under CoE RAISE, we are pursuing the advancement of machine learning (ML) approaches across both fronts, aiming, towards the end of the project, to integrate both technologies in a synergistic framework where the outputs of remote sensing inform and guide those of seismic imaging. Seismic imaging is used for the discovery of new energy resources including geothermal reservoirs with a high level of confidence. These, however, often rely on computationally expensive simulations and it is desirable to explore the potential of ML methodologies to be used to at least partly replace some of the associated computationally demanding components. In this work, we have explored a number of candidate generative neural network architectures for seismic wave modelling with the aim of integrating them in an inversion process, during which the subsurface features are uncovered from measurements recorded at the surface of the earth.