Robust Baseline Subtraction for Ultrasonic Full Wavefield Analysis

Full wavefield analysis is used to study and characterize the interaction between waves and structural damage. Yet, as wavefields are measured and as damage evolves in a structure, environmental and operational variations can significantly affect wave propagation. Several approaches, including time-stretching and optimal baseline selection methods, can reduce variations, but these methods are often limited to specific effects, are ineffective for large environmental variations, or require an impractical number of prior baseline measurements. This paper presents a robust methodology for subtracting wavefields and isolating wave-damage interactions. The method is based on dictionary learning. It is robust to multiple types of environmental and operational variations and requires only one initial baseline. We learn the dictionary, which describes wave propagation for a particular wavefield, based on multiple frequencies of a baseline wavefield. We then use the dictionary and sparse regression to create new baselines for measurements with different environmental and operational conditions. The new baseline is then subtracted from the new wavefield to isolate damage wavefield.