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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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Autores principales: Tollenaar, Veronica, Zekollari, Harry, Lhermitte, Stef, Tax, David M.J., Debaille, Vinciane, Goderis, Steven, Claeys, Philippe, Pattyn, Frank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791461/
https://www.ncbi.nlm.nih.gov/pubmed/35080966
http://dx.doi.org/10.1126/sciadv.abj8138
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author Tollenaar, Veronica
Zekollari, Harry
Lhermitte, Stef
Tax, David M.J.
Debaille, Vinciane
Goderis, Steven
Claeys, Philippe
Pattyn, Frank
author_facet Tollenaar, Veronica
Zekollari, Harry
Lhermitte, Stef
Tax, David M.J.
Debaille, Vinciane
Goderis, Steven
Claeys, Philippe
Pattyn, Frank
author_sort Tollenaar, Veronica
collection PubMed
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.
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spelling pubmed-87914612022-02-08 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 Sci Adv Earth, Environmental, Ecological, and Space Sciences 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. American Association for the Advancement of Science 2022-01-26 /pmc/articles/PMC8791461/ /pubmed/35080966 http://dx.doi.org/10.1126/sciadv.abj8138 Text en Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY). https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Earth, Environmental, Ecological, and Space Sciences
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
title 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_short Unexplored Antarctic meteorite collection sites revealed through machine learning
title_sort unexplored antarctic meteorite collection sites revealed through machine learning
topic Earth, Environmental, Ecological, and Space Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791461/
https://www.ncbi.nlm.nih.gov/pubmed/35080966
http://dx.doi.org/10.1126/sciadv.abj8138
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