High Resolution Deconvolution of Ultrasonic Traces

  • C. A. Zala
  • I. Barrodale
  • K. I. McRae
Part of the NATO ASI Series book series (volume 44)


Ultrasonic inspection techniques are of considerable importance in the nondestructive evaluation of laminar and composite materials. In the detection and localization of flaws, it is frequently desirable to enhance the resolution of the raw B-scan data. In this report we describe the principles and performance of procedures for improving the temporal resolution of the data. The methods are one-dimensional and are applied to successive traces (A-scans).

Two techniques for high resolution deconvolution are investigated. They are based on the assumption that the underlying reflection series is sparse, i.e., contains relatively few nonzero spikes. The first method is a simple spectral extrapolation procedure. Here the Fourier transform of the deconvolved data will consist of a small number of complex exponentials, for which autoregressive filter coefficients (in a region of high SNR) may be computed using the Burg algorithm [1]. This filter may then be used to extrapolate the transform outside this high SNR region, thereby achieving a broadband result. The second method is a recently developed deconvolution technique based on curve-fitting in the L1 (least absolute values) norm. In this procedure the reflection series is built up one-at-a-time until a specified maximum number of spikes is reached or a desired fit to the trace is achieved.

These and other processing schemes were incorporated into an integrated software system for simulation and analysis, developed for VAX/VMS computers. Currently we are developing a fast B-scan processing system, making use of a Compaq 386 microcomputer and a TMS 32020 signal processing card; this system is designed to yield high resolution B-scan images in real time.


Spike Train Autoregressive Coefficient Extrapolation Procedure Inverse Filter Spike Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • C. A. Zala
    • 1
  • I. Barrodale
    • 1
  • K. I. McRae
    • 2
  1. 1.Barrodale Computing ServicesVictoriaCanada
  2. 2.FMODefence Research Establishment PacificVictoriaCanada

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