Abstract: A method and system employing deep learning techniques improves processing speed and spatial resolution of acoustic wavenumber spectroscopy (ASSESS) techniques by performing semantic segmentation on simulated ultrasonic wavefield images of a steady-state, select-tone excitation of a structural or mechanical component. One or more embodiments may employ a convolutional neural network (CNN), pre-trained on openly-available datasets, and trained by transfer learning on an augmented wavefield dataset, to localize and characterize defects or damage from inspection measurements of components.