Bayesian modelling for determining material properties

Anil Kumar, Rohit Kumar Shrivastava, Kumar Hemant Singh

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Abstract

    Sound wave propagation in materials has been used extensively for nondestructive testing of materials, studying the internal structure of Earth or for oil/gas/mineral exploration. The propagation characteristics have been exploited and studied using signal processing tools for analyzing the material properties or for examining the sub-surface features. The signal processing tools can be innovated in accordance with the characteristics of the sound wave propagation in the media for improvement of the signal to noise ratio. A synthetic model of one dimensional chain of spherical particles is used to generate space time responses when impulse moves along the chain, it gives information about the longitudinal wave propagation (P-wave). The space time responses obtained from the synthetic model are used by the Bayesian inference technique to obtain the properties of the media (particle size/mass distribution of the chain). Finally, the importance of Bayesian inference technique as a signal processing tool is discussed upon.

    Original languageEnglish
    Title of host publication2018 International Conference on Recent Innovations in Electrical, Electronics and Communication Engineering, ICRIEECE 2018
    PublisherIEEE
    Pages1557-1563
    Number of pages7
    ISBN (Electronic)9781538659946
    DOIs
    Publication statusPublished - 27 Feb 2020
    EventInternational Conference on Recent Innovations in Electrical, Electronics and Communication Engineering, ICRIEECE 2018 - Bhubaneswar, India
    Duration: 27 Jul 201828 Jul 2018

    Conference

    ConferenceInternational Conference on Recent Innovations in Electrical, Electronics and Communication Engineering, ICRIEECE 2018
    Abbreviated titleICRIEECE 2018
    CountryIndia
    CityBhubaneswar
    Period27/07/1828/07/18

    Keywords

    • Bayesian modelling
    • Disorder parameter
    • Markov chain Monte Carlo
    • Probabilistic programming

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