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I demonstrate that the conventional seismic full-waveform inversion algorithmcan be constructed as a recurrent neural network and so implemented using deeplearning software such as TensorFlow. Applying another deep learning concept,the Adam optimizer with minibatches of data, produces quicker convergencetoward the true wave speed model on a 2D dataset than Stochastic GradientDescent and than the L-BFGS-B optimizer with the cost function and gradientcomputed using the entire training dataset. I also show that the cost functiongradient calculation using reverse-mode automatic differentiation is the sameas that used in the adjoint state method.

Seismic Full-Waveform Inversion Using Deep Learning Tools and Techniques