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Title: Quantifying three-dimensional fabric in tills using X-ray microcomputed tomography
Author: Groves, John W. E.
ISNI:       0000 0004 7971 8073
Awarding Body: Queen Mary, University of London
Current Institution: Queen Mary, University of London
Date of Award: 2019
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Deforming subglacial tills have a significant influence on the dynamics of many glaciers and ice sheets; however, due to their inaccessibility and spatial/temporal heterogeneity, laws defining their behaviour and rheology are still contentious. A lack of analytical and theoretical continuity between exposed relict tills, those under active ice and physical and numerical models using artificial analogues is partly responsible for this. Particle fabric, the 3D orientation of individual particles, could provide a quantitative link between such approaches; however, inconsistencies and weaknesses in data collection, presentation and interpretation have led to conflicting laws governing particle dynamics and therefore subglacial till behaviour. X-ray μCT provides 3D volumetric density maps of till samples at μm-scale, allowing for the extraction of true 3D properties for large particle populations (n > 5000). Typically, such investigations have used sorted or artificial sands locked in resin to simplify particle identification; natural sediments however, are compositionally and lithologically heterogenous and require a supervised approach. Machine-learning protocols are presented and tested alongside a novel method which quantifies the best possible representation of particles within a sample. A mean accuracy of 85% is achieved. By applying these protocols to samples taken from a variety of active and relict glacier/ice sheet margins, a large database of particle properties (n > 280k), including orientation, shape, size, 3D position and other experimental metrics has been created. Particle fabrics generated using X-ray μCT are much weaker and subtler than those obtained through other methods; therefore a detailed investigation into presentation, statistical significance and contextual interpretation of fabric data is conducted. The role of particle properties, particularly size and shape is shown to be an important controller of fabric in tills and must be carefully considered. By applying targeted analysis based on these considerations to sheared and liquefied subglacial tills, a set of criteria for distinguishing strain regime is hypothesised. These criteria are subsequently applied to a suite of stacked subglacial tills, revealing the μCT fabric signature associated with variations in depth and deformation style. The implications for rheological theory and the role of fabric data in glacial sedimentology are discussed.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Microcomputed Tomography