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