Traditional physics-based approaches to infer sub-surface properties such as fullwaveform inversion or reflectivity inversion are time consuming and computationally expensive. We present a deep-learning technique that eliminates the need for these computationally complex methods by posing the problem as one of domain transfer. Our solution is based on a deep convolutional generative adversarial network and dramatically reduces computation time. Training based on two different types of synthetic data produced a neural network that generates realistic velocity models when applied to a real data set. The system's ability to generalize means it is robust against the inherent occurrence of velocity errors and artifacts in both training and test datasets.

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