  This paper introduces novel deep recurrent neural network architectures for
Velocity Model Building (VMB), which is beyond what Araya-Polo et al 2018
pioneered with the Machine Learning-based seismic tomography built with
convolutional non-recurrent neural network. Our investigation includes the
utilization of basic recurrent neural network (RNN) cells, as well as Long
Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells. Performance
evaluation reveals that salt bodies are consistently predicted more accurately
by GRU and LSTM-based architectures, as compared to non-recurrent
architectures. The results take us a step closer to the final goal of a
reliable fully Machine Learning-based tomography from pre-stack data, which
when achieved will reduce the VMB turnaround from weeks to days.
