  In the Oil and Gas industry, estimating a subsurface velocity field is an
essential step in seismic processing, reservoir characterization, and
hydrocarbon volume calculation. Full-waveform inversion (FWI) velocity modeling
is an iterative advanced technique that provides an accurate and detailed
velocity field model, although at a very high computational cost due to the
physics-based numerical simulations required at each FWI iteration. In this
study, we propose a method of generating velocity field models, as detailed as
those obtained through FWI, using a conditional generative adversarial network
(cGAN) with multiple inputs. The primary motivation of this approach is to
circumvent the extremely high cost of full-waveform inversion velocity
modeling. Real-world data were used to train and test the proposed network
architecture, and three evaluation metrics (percent error, structural
similarity index measure, and visual analysis) were adopted as quality
criteria. Based on these metrics, the results evaluated upon the test set
suggest that the GAN was able to accurately match real FWI generated outputs,
enabling it to extract from input data the main geological structures and
lateral velocity variations. Experimental results indicate that the proposed
method, when deployed, has the potential to increase the speed of geophysical
reservoir characterization processes, saving on time and computational
resources.
