Predictability can give information about the "health" of the material.
Photo by Ant Rozetsky on Unsplash
What is it about?
In this paper, we present a novel and completely different approach to the problem of scattering material characterization: measuring the degree of predictability of the time series. Measuring predictability can provide information of the signal strength of the deterministic component of the time series in relation to the whole time series acquired. This relationship can provide information about coherent reflections in material grains with respect to the rest of incoherent noises that typically appear in non-destructive testing using ultrasonics. This is a non-parametric technique commonly used in chaos theory that does not require making any kind of assumptions about attenuation profiles. In highly scattering media (low SNR), it has been shown theoretically that the degree of predictability allows material characterization. The experimental results obtained in this work with 32 cement probes of 4 different porosities demonstrate the ability of this technique to do classification. It has also been shown that, in this particular application, the measurement of predictability can be used as an indicator of the percentages of porosity of the test samples with great accuracy.
Why is it important?
Measuring predictability and caos in ultrasonic scans of materials can give information about the "health" of the material. This is a novel approach that might provide in some situations better results than traditional characterisation by means of attenuation or speed profiles.
The following have contributed to this page: Ramon Miralles