Modelling Elastic Properties of Clastic Rocks from Microtomographic Images Using Multi-Mineral Segmentation and Machine Learning
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 conta...
| Main Author: | |
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| Format: | Thesis |
| Published: |
Curtin University
2022
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| Online Access: | http://hdl.handle.net/20.500.11937/88666 |
| _version_ | 1848765057454833664 |
|---|---|
| author | Liang, Jiabin |
| author_facet | Liang, Jiabin |
| author_sort | Liang, Jiabin |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:29:12Z |
| format | Thesis |
| id | curtin-20.500.11937-88666 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:29:12Z |
| publishDate | 2022 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-886662024-06-18T00:21:15Z Modelling Elastic Properties of Clastic Rocks from Microtomographic Images Using Multi-Mineral Segmentation and Machine Learning Liang, Jiabin 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. 2022 Thesis http://hdl.handle.net/20.500.11937/88666 Curtin University fulltext |
| spellingShingle | Liang, Jiabin Modelling Elastic Properties of Clastic Rocks from Microtomographic Images Using Multi-Mineral Segmentation and Machine Learning |
| title | Modelling Elastic Properties of Clastic Rocks from
Microtomographic Images Using Multi-Mineral Segmentation
and Machine Learning |
| title_full | Modelling Elastic Properties of Clastic Rocks from
Microtomographic Images Using Multi-Mineral Segmentation
and Machine Learning |
| title_fullStr | Modelling Elastic Properties of Clastic Rocks from
Microtomographic Images Using Multi-Mineral Segmentation
and Machine Learning |
| title_full_unstemmed | Modelling Elastic Properties of Clastic Rocks from
Microtomographic Images Using Multi-Mineral Segmentation
and Machine Learning |
| title_short | Modelling Elastic Properties of Clastic Rocks from
Microtomographic Images Using Multi-Mineral Segmentation
and Machine Learning |
| title_sort | modelling elastic properties of clastic rocks from
microtomographic images using multi-mineral segmentation
and machine learning |
| url | http://hdl.handle.net/20.500.11937/88666 |