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

Abstract

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 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 methodology is demonstrated through two case studies: reconstructing a delamination defect in a sandwich composite and reconstructing a high-resolution image of a highly heterogeneous reinforced concrete beam. While the delamination defect was not fully reconstructed, the results indicate that combining cross-correlation misfit measures with total variation regularization and Sigmoid parameter transformation results in significantly better reconstructed images of composites and identifying sharp discontinuities in multilayered and heterogeneous structures.