Session: 18: Student poster competition

Paper Number: 111314

111314 - Apex-Shifted Radon Transform-Based Near-Surface Artifact Removal in Total Focusing Methods 

We propose an apex-shifted Radon transform (ASRT)-based technique to mitigate surface wave artifacts (SWAs) in the total focusing method (TFM) for ultrasonic imaging. In reflection mode scans, direct P-waves and surface waves (collectively referred to as direct arrivals) can cause strong near-surface artifacts. These artifacts lower the visual clarity of TFM reconstructions and can obscure damage sites, creating a “dead-zone” near the array that is difficult to image. To mitigate SWAs, an ASRT-based image processing algorithm has been used to mute the direct arrivals in full matrix capture data used for TFM. The ASRT is a mathematical transform at the core of the proposed technique. It targets specific geometry from time-space domain full matrix capture (FMC) data and compresses the corresponding wave packets into point-like regions in the Radon domain. In this new domain, overlapping events in the original domain are represented as sparse discrete regions where undesirable information can be muted without destroying nearby data. First, a demonstration of how the ASRT is used to remove direct arrivals in FMC data is provided. Then, the proposed SWA-mute technique is demonstrated on data generated from spectral element wave propagation simulations and performs comparably to the baseline subtraction approach, which is the ideal case. Finally, the results are validated on the TFM reconstruction of experimental ultrasonic measurements polluted by SWAs and the effectiveness of the technique is discussed.

Presenting Author: Augustine Loshelder The University of Alabama

Presenting Author Biography: Augustine (Gus) Loshelder is a first-year Ph.D. Student in the Lab for Computational Imaging and Smart Structures at The University of Alabama. His research interests include ultrasonic computed tomography, image processing, physics informed machine learning, and deep learning. He originally comes from Madison, Mississippi, and is a National Merit Finalist and an Aerospace Engineering and Mechanics major. He is a recipient of the Graduate Council Fellowship. Outside of school, Gus is an avid musician.