Physics-driven computational techniques, which suffer from ill-posedness and high
computational cost, have long been the standard for solving full-waveform inversion. In this
work, we develop a novel inversion technique that combines physicsdriven models with data-
driven methodologies based on the fully convolutional neural network (FCN) architecture.
We design a cycle-consistency loss to connect two FCN networks that are trained to
incorporate both seismic forward and inverse modeling. To evaluate the performance of our …