Mapping Planetary Surface Ages at Ultimate Resolutions with Machine Learning: The Moon
| dc.contributor.author | Fairweather, John Hugh | |
| dc.contributor.supervisor | Gretchen Benedix | en_US |
| dc.contributor.supervisor | Anthony Lagain | en_US |
| dc.date.accessioned | 2024-05-27T01:46:56Z | |
| dc.date.available | 2024-05-27T01:46:56Z | |
| dc.date.issued | 2023 | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/95165 | |
| dc.description.abstract |
The density of impact craters upon a terrestrial surface can give an accurate estimate of the surface's formation age. The Moon has hundreds of millions of impact craters scattered across its surface. Through the power of machine learning, we can automatically count those craters to date any surface on the Moon! | en_US |
| dc.publisher | Curtin University | en_US |
| dc.title | Mapping Planetary Surface Ages at Ultimate Resolutions with Machine Learning: The Moon | en_US |
| dc.type | Thesis | en_US |
| dcterms.educationLevel | PhD | en_US |
| curtin.department | School of Earth and Planetary Sciences | en_US |
| curtin.accessStatus | Open access | en_US |
| curtin.faculty | Science and Engineering | en_US |
| curtin.contributor.orcid | Fairweather, John Hugh [0000-0002-3854-6311] | en_US |