Resolution Enhancement in Ultrasonic TOFD Imaging by Combining Sparse Deconvolution and Synthetic Aperture Focusing Technique (Sparse-SAFT)

Abstract

The shallow subsurface defects are difficult to be identified and quantified by ultrasonic time-of-flight diffraction (TOFD) due to the low resolution induced by pulse width and beam spreading. In this paper, Sparse-SAFT is proposed to improve the time resolution and lateral resolution in TOFD imaging by combining sparse deconvolution and synthetic aperture focusing technique (SAFT). The mathematical model in the frequency domain is established based on the l1 and l2 norm constraints, and the optimization problem is solved for enhancing time resolution. On this basis, SAFT is employed to improve lateral resolution by delay-and-sum beamforming. The simulated and experimental results indicate that the lateral wave and tip-diffracted waves can be decoupled with Sparse-SAFT. The shallow subsurface defects with a height of 3.0 mm at the depth of 3.0 mm were detected quantitatively, and the relative measurement errors of flaw heights and depths were no more than 10.3%. Compared to conventional SAFT, the time resolution and lateral resolution are enhanced by 72.5 and 56% with Sparse-SAFT, respectively. Finally, the proposed method is also suitable for improving resolution to detect the defects beyond dead zone.

Three-Dimensional Ultrasonic Reverse-Time Migration Imaging of Submarine Pipeline Nondestructive Testing in Cylindrical Coordinates

“…Then, by using ultrasonic imaging methods, defects in submarine pipelines can be visualized in an intuitive way [19][20][21]. The traditional ultrasonic methods of pipeline inspection include the synthetic aperture focusing technique (SAFT) [22][23][24], time-of-flight diffraction (TOFD) [25][26][27], and the total focusing method (TFM) [28,29]. Those methods have their own advantages and disadvantages.…”

Impact force localization and reconstruction via ADMM-based sparse regularization method

“…Traditional methods are usually incapable of dealing with these problems, especially for identifying time history and localization simultaneously [15]. As an eye-catching regularization method, sparse regularization(SR) explores the inherent sparsity of impact forces in the joint time-space domain [16,17,18], making it very popular in fields such as fault diagnosis [19] and image processing [20]. Under such strong constraints brought by sparse priors, SR can achieve the force reconstruction and impact localization at the same time, even in under-determined circumstances [21].…”