Modelling Elastic Properties of Clastic Rocks from Microtomographic Images Using Multi-Mineral Segmentation and Machine Learning
dc.contributor.author | Liang, Jiabin | |
dc.contributor.supervisor | Boris Gurevich | en_US |
dc.contributor.supervisor | Maxim Lebedev | en_US |
dc.date.accessioned | 2022-05-31T08:29:21Z | |
dc.date.available | 2022-05-31T08:29:21Z | |
dc.date.issued | 2022 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/88666 | |
dc.description.abstract |
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. | en_US |
dc.publisher | Curtin University | en_US |
dc.title | Modelling Elastic Properties of Clastic Rocks from Microtomographic Images Using Multi-Mineral Segmentation and Machine Learning | en_US |
dc.type | Thesis | en_US |
dcterms.educationLevel | PhD | en_US |
curtin.department | WASM: Minerals, Energy and Chemical Engineering | en_US |
curtin.accessStatus | Open access | en_US |
curtin.faculty | Science and Engineering | en_US |
curtin.contributor.orcid | Liang, Jiabin [0000-0002-0105-193X] | en_US |