LAUSR

Data-driven artificial neural networks (ANNs) demonstrably offer numerous advantages over the conventional deterministic methods in a wide range of geophysical problems. For seismic velocity model building, judiciously trained ANNs offer the possibility of estimating subsurface velocity models; however, there are substantial challenges with effective and efficient network training. Motivated by the multiscale approach commonly used to address full waveform inversion (FWI) nonlinearity challenges, we develop a frequency-stepping velocity model building approach that uses a sequence-to-sequence recurrent neural network (RNN) with built-in long short-term memory (LSTM). The input sequences to the LSTM-RNN consist of the frequency-domain seismic data ordered by frequency from lowest available to highest usable or chosen, while the corresponding output sequences are frequency-dependent smoothed velocity models. We compare the models recovered using the trained RNN to the true models qualitatively and quantitatively. The normalized root mean square (NRMS) misfit between the true and predicted models has a mean of 6%, which confirms that the network recovers highly accurate models.