Cargando…
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...
Autores principales: | , , , , , , , |
---|---|
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 |
_version_ | 1784640187438989312 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8791461 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT tollenaarveronica unexploredantarcticmeteoritecollectionsitesrevealedthroughmachinelearning AT zekollariharry unexploredantarcticmeteoritecollectionsitesrevealedthroughmachinelearning AT lhermittestef unexploredantarcticmeteoritecollectionsitesrevealedthroughmachinelearning AT taxdavidmj unexploredantarcticmeteoritecollectionsitesrevealedthroughmachinelearning AT debaillevinciane unexploredantarcticmeteoritecollectionsitesrevealedthroughmachinelearning AT goderissteven unexploredantarcticmeteoritecollectionsitesrevealedthroughmachinelearning AT claeysphilippe unexploredantarcticmeteoritecollectionsitesrevealedthroughmachinelearning AT pattynfrank unexploredantarcticmeteoritecollectionsitesrevealedthroughmachinelearning |