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dc.contributor.authorLiang, Jiabin
dc.contributor.supervisorBoris Gurevichen_US
dc.contributor.supervisorMaxim Lebedeven_US

Modelling elastic properties from micro-CT images of rocks is essential for geophysical characterisation of the subsurface. This is achieved through an advanced physics-based multi-mineral image segmentation workflow, which is then automated using machine learning. The effects of intergranular contacts that are below the micro-CT resolution are modelled by a workflow that extracts their elastic properties from rock microstructure and ultrasonic measurements. I also developed a workflow that successfully detects pressure-induced deformation in micro-CT images.

dc.publisherCurtin Universityen_US
dc.titleModelling Elastic Properties of Clastic Rocks from Microtomographic Images Using Multi-Mineral Segmentation and Machine Learningen_US
curtin.departmentWASM: Minerals, Energy and Chemical Engineeringen_US
curtin.accessStatusFulltext not availableen_US
curtin.facultyScience and Engineeringen_US
curtin.contributor.orcidLiang, Jiabin [0000-0002-0105-193X]en_US

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