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Title: Acoustic full-waveform inversion in geophysical and medical imaging
Author: Calderon Agudo, Oscar
ISNI:       0000 0004 7427 7846
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2018
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Imaging methods are employed in multiple scientific disciplines to obtain properties of targets that are otherwise invisible to the human eye, such as for imaging the earth’s interior or the human body. In geophysics, the full-waveform inversion (FWI) method is often used to recover high-quality high-fidelity models of the subsurface by recording seismic data in the field, and modelling wave propagation with a wave equation. But the visco-elastic nature of the subsurface is typically ignored, and the acoustic approximation of the wave equation is utilised to reduce the compute costs. This results in less well-resolved and inaccurate models, as accurate physics of wave propagation is not considered. On the contrary, the application of FWI to medical imaging is still in its infancy, especially in 3D. Instead, ray-based tomographic methods are used, which do not ac- count for the physics of finite-frequency wave propagation, leading to poorer recovered models of physical properties. Here, I first propose a method to address elastic and viscous effects in FWI of seismic data, which is based on the use of matching filters and modelling elastic or viscous wave propagation prior to the inversion, while using the acoustic wave equation during the inversion. This represents a considerable time reduction over full elastic or viscous inversions. I show that the proposed method leads to more accurate and detailed P-wave velocity subsurface models, both on synthetic and field datasets. Then, I investigate the feasibility of 3D FWI for breast cancer diagnosis and analyse, for the first time, its implementation to obtain 3D images of the human brain with ultrasound through the skull. The results demonstrate FWI is now ready to be applied to these datasets, and could result in more accurate images and faster and safer acquisitions than with current methods.
Supervisor: Morgan, Joanna ; Warner, Michael Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral