  Acoustic scattering is strongly influenced by boundary geometry of objects
over which sound scatters. The present work proposes a method to infer object
geometry from scattering features by training convolutional neural networks.
The training data is generated from a fast numerical solver developed on CUDA.
The complete set of simulations is sampled to generate multiple datasets
containing different amounts of channels and diverse image resolutions. The
robustness of our approach in response to data degradation is evaluated by
comparing the performance of networks trained using the datasets with varying
levels of data degradation. The present work has found that the predictions
made from our models match ground truth with high accuracy. In addition,
accuracy does not degrade when fewer data channels or lower resolutions are
used.
