Full Waveform Inversion of Ultrasonics for High-Resolution Imaging of Composites: Misfit, Regularization, and Parameterization

Abstract This work investigates strategies to enhance full waveform inversion (FWI) of ultrasonic signals for high-resolution imaging and defect detection of composites. Various misfit measures—including L2-norm, L1-norm, cross-correlation, and envelope measures—are compared to assess their effectiveness in improving FWI ultrasonic outcomes. Additionally, different regularization methods, such as Tikhonov (L2-norm), LASSO (L1-norm), and total variation regularization, are examined for stabilizing FWI and enhancing its reconstruction. Parameterization techniques, like transforming model parameters to Sigmoid space, are explored to improve reconstruction accuracy and convergence. The findings suggest that combining cross-correlation functionals with total variation regularization and applying Sigmoid transformation to model parameters is particularly effective for generating high-resolution images of composites and detecting sharp-edged defects in multilayered systems.