Unexplored Antarctic meteorite collection sites revealed through machine learning
Meteorites provide a unique view into the origin and evolution of the Solar System. Antarctica is the most productive region for recovering meteorites, where these extraterrestrial rocks concentrate at meteorite stranding zones. To date, meteorite-bearing blue ice areas are mostly identified by sere...
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Online Access: | http://dx.doi.org/10.1126/sciadv.abj8138 https://www.science.org/doi/pdf/10.1126/sciadv.abj8138 |
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craaas:10.1126/sciadv.abj8138 2024-06-23T07:47:16+00:00 Unexplored Antarctic meteorite collection sites revealed through machine learning Tollenaar, Veronica Zekollari, Harry Lhermitte, Stef Tax, David M.J. Debaille, Vinciane Goderis, Steven Claeys, Philippe Pattyn, Frank 2022 http://dx.doi.org/10.1126/sciadv.abj8138 https://www.science.org/doi/pdf/10.1126/sciadv.abj8138 en eng American Association for the Advancement of Science (AAAS) Science Advances volume 8, issue 4 ISSN 2375-2548 journal-article 2022 craaas https://doi.org/10.1126/sciadv.abj8138 2024-05-24T12:53:26Z Meteorites provide a unique view into the origin and evolution of the Solar System. Antarctica is the most productive region for recovering meteorites, where these extraterrestrial rocks concentrate at meteorite stranding zones. To date, meteorite-bearing blue ice areas are mostly identified by serendipity and through costly reconnaissance missions. Here, we identify meteorite-rich areas by combining state-of-the-art datasets in a machine learning algorithm and provide continent-wide estimates of the probability to find meteorites at any given location. The resulting set of ca. 600 meteorite stranding zones, with an estimated accuracy of over 80%, reveals the existence of unexplored zones, some of which are located close to research stations. Our analyses suggest that less than 15% of all meteorites at the surface of the Antarctic ice sheet have been recovered to date. The data-driven approach will greatly facilitate the quest to collect the remaining meteorites in a coordinated and cost-effective manner. Article in Journal/Newspaper Antarc* Antarctic Antarctica Ice Sheet AAAS Resource Center (American Association for the Advancement of Science) Antarctic The Antarctic Science Advances 8 4 |
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Open Polar |
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AAAS Resource Center (American Association for the Advancement of Science) |
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craaas |
language |
English |
description |
Meteorites provide a unique view into the origin and evolution of the Solar System. Antarctica is the most productive region for recovering meteorites, where these extraterrestrial rocks concentrate at meteorite stranding zones. To date, meteorite-bearing blue ice areas are mostly identified by serendipity and through costly reconnaissance missions. Here, we identify meteorite-rich areas by combining state-of-the-art datasets in a machine learning algorithm and provide continent-wide estimates of the probability to find meteorites at any given location. The resulting set of ca. 600 meteorite stranding zones, with an estimated accuracy of over 80%, reveals the existence of unexplored zones, some of which are located close to research stations. Our analyses suggest that less than 15% of all meteorites at the surface of the Antarctic ice sheet have been recovered to date. The data-driven approach will greatly facilitate the quest to collect the remaining meteorites in a coordinated and cost-effective manner. |
format |
Article in Journal/Newspaper |
author |
Tollenaar, Veronica Zekollari, Harry Lhermitte, Stef Tax, David M.J. Debaille, Vinciane Goderis, Steven Claeys, Philippe Pattyn, Frank |
spellingShingle |
Tollenaar, Veronica Zekollari, Harry Lhermitte, Stef Tax, David M.J. Debaille, Vinciane Goderis, Steven Claeys, Philippe Pattyn, Frank Unexplored Antarctic meteorite collection sites revealed through machine learning |
author_facet |
Tollenaar, Veronica Zekollari, Harry Lhermitte, Stef Tax, David M.J. Debaille, Vinciane Goderis, Steven Claeys, Philippe Pattyn, Frank |
author_sort |
Tollenaar, Veronica |
title |
Unexplored Antarctic meteorite collection sites revealed through machine learning |
title_short |
Unexplored Antarctic meteorite collection sites revealed through machine learning |
title_full |
Unexplored Antarctic meteorite collection sites revealed through machine learning |
title_fullStr |
Unexplored Antarctic meteorite collection sites revealed through machine learning |
title_full_unstemmed |
Unexplored Antarctic meteorite collection sites revealed through machine learning |
title_sort |
unexplored antarctic meteorite collection sites revealed through machine learning |
publisher |
American Association for the Advancement of Science (AAAS) |
publishDate |
2022 |
url |
http://dx.doi.org/10.1126/sciadv.abj8138 https://www.science.org/doi/pdf/10.1126/sciadv.abj8138 |
geographic |
Antarctic The Antarctic |
geographic_facet |
Antarctic The Antarctic |
genre |
Antarc* Antarctic Antarctica Ice Sheet |
genre_facet |
Antarc* Antarctic Antarctica Ice Sheet |
op_source |
Science Advances volume 8, issue 4 ISSN 2375-2548 |
op_doi |
https://doi.org/10.1126/sciadv.abj8138 |
container_title |
Science Advances |
container_volume |
8 |
container_issue |
4 |
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1802651360246628352 |