### Abstract

Language | English |
---|---|

Article number | 095002 |

Number of pages | 29 |

Journal | Inverse Problems |

Volume | 34 |

Issue number | 9 |

Early online date | 8 Jun 2018 |

DOIs | |

Publication status | Published - 29 Jun 2018 |

### Fingerprint

### Keywords

- ultrasonic non-destructive testing
- imaging of defects
- ultrasonic phased array
- Monte Carlo method
- Bayesian framework

### Cite this

*Inverse Problems*,

*34*(9), [095002]. https://doi.org/10.1088/1361-6420/aaca8f

}

*Inverse Problems*, vol. 34, no. 9, 095002. https://doi.org/10.1088/1361-6420/aaca8f

**A transdimensional Bayesian approach to ultrasonic travel-time tomography for non-destructive testing.** / Tant, K M M; Galetti, E; Mulholland, A J; Curtis, A; Gachagan, A.

Research output: Contribution to journal › Article

TY - JOUR

T1 - A transdimensional Bayesian approach to ultrasonic travel-time tomography for non-destructive testing

AU - Tant, K M M

AU - Galetti, E

AU - Mulholland, A J

AU - Curtis, A

AU - Gachagan, A

PY - 2018/6/29

Y1 - 2018/6/29

N2 - Traditional imaging algorithms within the ultrasonic non-destructive testing community typically assume that the material being inspected is primarily homogeneous, with heterogeneities only at sub-wavelength scales. When the medium is of a more generally heterogeneous nature, this assumption can contribute to the poor detection, sizing and characterisation of any defects. Prior knowledge of the varying velocity fields within the component would allow more accurate imaging of defects, leading to better decisions about how to treat the damaged component. This work endeavours to reconstruct the inhomogeneous velocity fields of random media from simulated ultrasonic phased array data. This is achieved via application of the reversible-jump Markov chain Monte Carlo method: a sampling-based approach within a Bayesian framework. The inverted maps are then used in conjunction with an imaging algorithm to correct for deviations in the wave speed, and the reconstructed flaw images are then used to quantitatively measure the success of this methodology. Using full matrix capture data arising from a finite element simulation of a phased array inspection of a heterogeneous component, a six-fold improvement in flaw location is achieved by taking into account the reconstructed velocity map which exploits almost no \textit{a priori} knowledge of the material's internal structure. Receiver operating characteristic curves are then calculated to demonstrate the enhanced probability of detection achieved when the material map is accounted for.

AB - Traditional imaging algorithms within the ultrasonic non-destructive testing community typically assume that the material being inspected is primarily homogeneous, with heterogeneities only at sub-wavelength scales. When the medium is of a more generally heterogeneous nature, this assumption can contribute to the poor detection, sizing and characterisation of any defects. Prior knowledge of the varying velocity fields within the component would allow more accurate imaging of defects, leading to better decisions about how to treat the damaged component. This work endeavours to reconstruct the inhomogeneous velocity fields of random media from simulated ultrasonic phased array data. This is achieved via application of the reversible-jump Markov chain Monte Carlo method: a sampling-based approach within a Bayesian framework. The inverted maps are then used in conjunction with an imaging algorithm to correct for deviations in the wave speed, and the reconstructed flaw images are then used to quantitatively measure the success of this methodology. Using full matrix capture data arising from a finite element simulation of a phased array inspection of a heterogeneous component, a six-fold improvement in flaw location is achieved by taking into account the reconstructed velocity map which exploits almost no \textit{a priori} knowledge of the material's internal structure. Receiver operating characteristic curves are then calculated to demonstrate the enhanced probability of detection achieved when the material map is accounted for.

KW - ultrasonic non-destructive testing

KW - imaging of defects

KW - ultrasonic phased array

KW - Monte Carlo method

KW - Bayesian framework

UR - http://iopscience.iop.org/journal/0266-5611

U2 - 10.1088/1361-6420/aaca8f

DO - 10.1088/1361-6420/aaca8f

M3 - Article

VL - 34

JO - Inverse Problems

T2 - Inverse Problems

JF - Inverse Problems

SN - 0266-5611

IS - 9

M1 - 095002

ER -