We explore the feasibility of a deep learning approach for tomography by comparing it with the current velocity prediction techniques used in the industry. This is accomplished through quantitative and qualitative comparisons of velocity models predicted by a Machine Learning (ML) system and those of two variations of full-waveform inversion (FWI). Additionally, we compare the computational aspects of the two approaches. The results show that the ML-based reconstructed models are competitive to the FWI-produced models in terms selected metrics, and widely less expensive to compute.


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