Model-driven, Wavefield Baseline Subtraction for Damage Visualization using Dictionary Learning

Abstract

Wavefield acquisition is a powerful visualization tool for studying wave propagation. Yet, without a baseline, damage visualization is a challenge because the damage wavefield is usually orders of magnitude weaker than the incident waves. Researchers have created several baseline-free approaches for suppressing incident waves, but these methods often rely on simple assumptions. We address this challenge with a dictionary learning framework that uses simulation data to guide the suppression of incident waves. Dictionary learning produces new baselines with geometric features similar to the simulations as well as velocity and frequency response features similar to the experimental data. We show that our framework can visualize reflections from a circular 2 mm diameter half thickness hole in a 10 cm × 10 cm steel plate without a baseline.