Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography

We study the feasibility of data based machine learning applied to ultrasound tomography to estimate water-saturated porous material parameters. In this work, the data to train the neural networks is simulated by solving wave propagation in coupled poroviscoelastic-viscoelastic-acoustic media. As the forward model, we consider a high-order discontinuous Galerkin method while deep convolutional neural networks are used to solve the parameter estimation problem. In the numerical experiment, we estimate the material porosity and tortuosity while the remaining parameters which are of less interest are successfully marginalized in the neural networks-based inversion. Computational examples confirms the feasibility and accuracy of this approach

On the scattering of longitudinal elastic waves from axisymmetric defects in coated pipes

This is the post-print version of the final paper published in Journal of Sound and Vibration. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Viscoelastic coatings are widely used to protect pipelines from their surrounding environment. These coatings are known to attenuate ultrasonic waves guided along the pipe walls, which may limit the range of a pulse/echo based inspection technique that seeks to detect defects in a pipeline. This article aims to investigate the attenuation of longitudinal modes in a coated pipe by comparing predicted and measured values for the reflection coefficient of an axisymmetric defect in a pipe coated with bitumen. This extends recent work undertaken by the authors for torsional modes, and also provides an independent investigation into the validity of those values proposed by the authors for the shear properties of bitumen, based on a comparison between prediction and experiment for torsional modes. Predictions are generated using a numerical mode matching approach for axially uniform defects, and a hybrid finite element based method for non-uniform defects. Values for the shear and longitudinal properties of bitumen are investigated and it is shown that the shear properties of the viscoelastic material play a dominant role in the propagation of longitudinal modes in a coated pipeline. Moreover, by using the shear values obtained from experiments on torsional modes, it is shown that good agreement between prediction and measurement for uniform and non-uniform defects may also be obtained for the longitudinal L(0,2) mode. This provides further validation for the shear bulk acoustic properties proposed for bitumen in the low ultrasonic frequency range, although in order to apply this methodology in general it is demonstrated that one must measure independently the reflection coefficient of both the torsional T(0,1) and the longitudinal L(0,2) mode before arriving at values for the shear properties of a viscoelastic material

Characterising poroelastic materials in the ultrasonic range - A Bayesian approach

Acoustic fields scattered by poroelastic materials contain key information about the materials' pore structure and elastic properties. Therefore, such materials are often characterised with inverse methods that use acoustic measurements. However, it has been shown that results from many existing inverse characterisation methods agree poorly. One reason is that inverse methods are typically sensitive to even small uncertainties in a measurement setup, but these uncertainties are difficult to model and hence often neglected. In this paper, we study characterising poroelastic materials in the Bayesian framework, where measurement uncertainties can be taken into account, and which allows us to quantify uncertainty in the results. Using the finite element method, we simulate measurements where ultrasonic waves are incident on a water-saturated poroelastic material in normal and oblique angles. We consider uncertainties in the incidence angle and level of measurement noise, and then explore the solution of the Bayesian inverse problem, the posterior density, with an adaptive parallel tempering Markov chain Monte Carlo algorithm. Results show that both the elastic and pore structure parameters can be feasibly estimated from ultrasonic measurements.Comment: Published in JSV. https://doi.org/10.1016/j.jsv.2019.05.02

Seismic reverse-time migration in viscoelastic media

Seismic images are key to exploration seismology. They help identify structures in the subsurface and locate potential reservoirs. However, seismic images suffer from the problem of low resolution caused by the viscoelasticity of the medium. The viscoelasticity of the media is caused by the combination of fractured solid rock and fluids, such as water, oil and gas. This viscoelasticity of the medium causes attenuation of seismic waves, which includes energy absorption and velocity dispersion. These two attenuation effects significantly change the seismic data, and thus the seismic imaging. The aim of this thesis is to deepen the understanding of seismic wave propagation in attenuating media and to further investigate the method for high-resolution seismic imaging. My work, presented in this dissertation, comprises the following three parts. First, the determination of the viscoelastic parameters in the generalised viscoelastic wave equation. The viscoelasticity of subsurface media is succinctly represented in the generalised wave equation by a fractional temporal derivative. This generalised viscoelastic wave equation is characterised by the viscoelastic parameter and the viscoelastic velocity, but these parameters are not well formulated and therefore unfavourable for seismic implementation. The causality and stability of the generalised wave equation are proved by deriving the rate-of-relaxation function. On this basis, the viscoelastic parameter is formulated based on the constant Q model, and the viscoelastic velocity is formulated in terms of the reference velocity and the viscoelastic parameter. These two formulations adequately represent the viscoelastic effect in seismic wave propagation. Second, the development of a fractional spatial derivatives wave equation with a spatial filter. This development aims to effectively and efficiently solve the generalised viscoelastic wave equation with fractional temporal derivative, which is numerically challenging. I have transferred the fractional temporal derivative into fractional spatial derivatives, which can be solved using the pseudo-spectral implementation. However, this method is inaccurate in heterogeneous media. I introduced a spatial filter to correct the simulation error caused by the averaging in this implementation. The numerical test shows that the proposed spatial filter can significantly improve the accuracy of the seismic simulation and maintain high efficiency. Moreover, the proposed wave equation with fractional spatial derivatives is applied to compensate for the attenuation effects in reverse-time migration. This allows the dispersion correction and energy compensation to be performed simultaneously, which improves the resolution of the migration results. Finally, the development of reverse-time migration using biaxial wavefield decomposition to reduce migration artefacts and further improve the resolution of seismic images. In reverse-time migration, the cross-correlation of unphysical waves leads to large artefacts. By decomposing the wavefield both horizontally and vertically, and selecting only the causal waves for cross-correlation, the artefacts are greatly reduced, and the delicate structures can be identified. This decomposition method is also suitable for reverse-time migration with attenuation compensation. The migration results show that the resolution of the final seismic image is significantly improved, compared to conventional reverse-time migration.Open Acces

Magnetic Resonance Elastography of the Brain: from Phantom to Mouse to Man

The overall objective of this study is to develop magnetic resonance elastography: MRE) imaging to better understand brain deformation, brain tissue mechanical properties, and brain-skull interaction in vivo. The findings of this study provide parameters for numerical models of human head biomechanics, as well as data for validation of these models. Numerical simulations offer enormous potential to the study of traumatic brain injury: TBI) and may also contribute to the development of prophylactic devices for high-risk subjects: e.g., military personnel, first-responders, and athletes). Current numerical models have not been adequately parameterized or validated and their predictions remain controversial. This dissertation describes three kinds of MRE experiments, conducted in phantom: physical model), mouse, and man. Phantom studies provide a means to experimentally confirm the accuracy of MRE estimates of viscoelastic parameters in relatively simple materials and geometries. Studies in the mouse provide insight into the dispersive nature of brain tissue mechanical properties at frequencies beyond those that can be measured in humans. Studies in human subjects provide direct measurements of the human brain\u27s response to dynamic extracranial loads, including skull-brain energy transmission and viscoelastic properties

Dissipative Dynamics of Polymer Phononic Materials

Phononic materials are artificial composites with unprecedented abilities to control acoustic waves in solids. Their performance is mainly governed by their architecture, determining frequency ranges in which wave propagation is inhibited. However, the dynamics of phononic materials also depends on the mechanical and material properties of their constituents. In the case of viscoelastic constituents, such as most polymers, it is challenging to correctly predict the actual dynamic behavior of real phononic structures. Existing studies on this topic either lack experimental evidence or are limited to specific materials and architectures in restricted frequency ranges. A general framework is developed and employed to characterize the dynamics of polymer phononic materials with different architectures made of both thermoset and thermoplastic polymers, presenting qualitatively different viscoelastic behaviors. Through a comparison of experimental results with numerical predictions, the reliability of commonly used elastic and viscoelastic material models is evaluated in broad frequency ranges. Correlations between viscous effects and the two main band-gap formation mechanisms in phononic materials are revealed, and experimentally verified guidelines on how to correctly predict their dissipative response are proposed in a computationally efficient way. Overall, this work provides comprehensive guidelines for the extension of phononics modeling to applications involving dissipative viscoelastic materials.</p

Forward Modelling and Inversion of the Ultrasonic Wave Propagation Through a Homogeneous and Porous Rock

The aim of my work is to estimate viscoelastic parameters of rock samples from waveforms of ultrasonic waves propagating through these samples. To this end, I develop an automated Python modules in Finite Element Modelling software Abaqus, and tailored it specifically for a controlled transmission experiment using ultrasonic source and receiver. The approach is verified using test Aluminium samples, and then applied to real rocks to estimate ultrasonic attenuation using Prony formulation of viscoelasticity