Variational Bayesian Inversion Approaches for Ultrasonic Tomography

Ultrasonic non-destructive evaluation (UNDE) is vital for assessing the structural integrity of safety-critical infrastructure. However, accurate defect detection and characterisation using UNDE is particularly challenging in complex materials like austenitic steel welds, as the heterogeneous and locally anisotropic grain structures distort wave paths, causing traditional imaging methods based on homogeneous and isotropic assumptions to fail. In this project we develop a probabilistic framework to reconstruct spatially varying elastic tensor information from ultrasonic travel-time data using stochastic Stein Variational Gradient Descent. Unlike prior approaches, our method relaxes assumptions of uniformity of material properties across the domain and considers uncertainty induced by limited prior knowledge. We show that travel-time data alone cannot fully constrain high-dimensional domains, and accurate imaging requires informed priors on a series of anisotropy parameters we use as a proxy for the stiffness tensor – scale, strength, and orientation.

For more information about the project contact Dr Katy Tant  (katy.tant@glasgow.ac.uk), Senior Lecturer in System Power & Energy at the James Watt School of Engineering at the University of Glasgow or James Ludlam (james.ludlam@strath.ac.uk), PhD candidate at the Department of Mathematics and Statistics at the University of Strathclyde.

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