Model Age Derivation of Large Martian Impact Craters, Using Automatic Crater Counting Methods
Determining when an impact crater formed is a complex and tedious task. However, this knowledge is crucial to understanding the geological history of planetary bodies and, more specifically, gives information on erosion rate measurements, meteorite ejection location, impact flux evolution and the lo...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
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Wiley-Blackwell
2021
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| Online Access: | http://purl.org/au-research/grants/arc/DP170102972 http://hdl.handle.net/20.500.11937/82726 |
| _version_ | 1848764538888912896 |
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| author | Lagain, Anthony Servis, Konstantinos Benedix, Gretchen Norman, Christopher Anderson, Seamus Bland, Philip |
| author_facet | Lagain, Anthony Servis, Konstantinos Benedix, Gretchen Norman, Christopher Anderson, Seamus Bland, Philip |
| author_sort | Lagain, Anthony |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Determining when an impact crater formed is a complex and tedious task. However, this knowledge is crucial to understanding the geological history of planetary bodies and, more specifically, gives information on erosion rate measurements, meteorite ejection location, impact flux evolution and the loss of a magnetic field. The derivation of an individual crater's age is currently performed through manual counting. Because crater size scales as a power law, this method is limited to small (and/or young) surface areas and, in the case of the derivation of crater emplacement age, to a small set of impact craters. Here, we used a Crater Detection Algorithm, specifically retrained to detect small impact craters on large‐ and high‐resolution imagery data set to solve this issue. We applied it to a global, 5 m/pixel resolution mosaic of Mars. Here, we test the use of this data set to date 10 large impact craters. We developed a cluster analysis tool in order to distinguish potential secondary crater clusters from the primary crater population. We then use this, filtered, crater population to date each large impact crater and evaluate our results against literature ages. We found that automated counting filtered through clustering analysis produced similar model ages to manual counts. This technique can now be expanded to much wider crater dating surveys, and by extension to any other kind of Martian surface. We anticipate that this new tool will considerably expand our knowledge of the geological events that have shaped the surface of Mars, their timing and duration. |
| first_indexed | 2025-11-14T11:20:57Z |
| format | Journal Article |
| id | curtin-20.500.11937-82726 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:20:57Z |
| publishDate | 2021 |
| publisher | Wiley-Blackwell |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-827262021-03-17T07:00:34Z Model Age Derivation of Large Martian Impact Craters, Using Automatic Crater Counting Methods Lagain, Anthony Servis, Konstantinos Benedix, Gretchen Norman, Christopher Anderson, Seamus Bland, Philip Determining when an impact crater formed is a complex and tedious task. However, this knowledge is crucial to understanding the geological history of planetary bodies and, more specifically, gives information on erosion rate measurements, meteorite ejection location, impact flux evolution and the loss of a magnetic field. The derivation of an individual crater's age is currently performed through manual counting. Because crater size scales as a power law, this method is limited to small (and/or young) surface areas and, in the case of the derivation of crater emplacement age, to a small set of impact craters. Here, we used a Crater Detection Algorithm, specifically retrained to detect small impact craters on large‐ and high‐resolution imagery data set to solve this issue. We applied it to a global, 5 m/pixel resolution mosaic of Mars. Here, we test the use of this data set to date 10 large impact craters. We developed a cluster analysis tool in order to distinguish potential secondary crater clusters from the primary crater population. We then use this, filtered, crater population to date each large impact crater and evaluate our results against literature ages. We found that automated counting filtered through clustering analysis produced similar model ages to manual counts. This technique can now be expanded to much wider crater dating surveys, and by extension to any other kind of Martian surface. We anticipate that this new tool will considerably expand our knowledge of the geological events that have shaped the surface of Mars, their timing and duration. 2021 Journal Article http://hdl.handle.net/20.500.11937/82726 10.1029/2020EA001598 http://purl.org/au-research/grants/arc/DP170102972 http://purl.org/au-research/grants/arc/FT170100024 http://creativecommons.org/licenses/by/4.0/ Wiley-Blackwell fulltext |
| spellingShingle | Lagain, Anthony Servis, Konstantinos Benedix, Gretchen Norman, Christopher Anderson, Seamus Bland, Philip Model Age Derivation of Large Martian Impact Craters, Using Automatic Crater Counting Methods |
| title | Model Age Derivation of Large Martian Impact Craters, Using Automatic Crater Counting Methods |
| title_full | Model Age Derivation of Large Martian Impact Craters, Using Automatic Crater Counting Methods |
| title_fullStr | Model Age Derivation of Large Martian Impact Craters, Using Automatic Crater Counting Methods |
| title_full_unstemmed | Model Age Derivation of Large Martian Impact Craters, Using Automatic Crater Counting Methods |
| title_short | Model Age Derivation of Large Martian Impact Craters, Using Automatic Crater Counting Methods |
| title_sort | model age derivation of large martian impact craters, using automatic crater counting methods |
| url | http://purl.org/au-research/grants/arc/DP170102972 http://purl.org/au-research/grants/arc/DP170102972 http://hdl.handle.net/20.500.11937/82726 |