Unexplored Antarctic meteorite collection sites revealed through machine learning

Author:

Tollenaar Veronica1ORCID,Zekollari Harry12ORCID,Lhermitte Stef2ORCID,Tax David M.J.3ORCID,Debaille Vinciane4,Goderis Steven5ORCID,Claeys Philippe5ORCID,Pattyn Frank1ORCID

Affiliation:

1. Laboratoire de Glaciologie, Université libre de Bruxelles, Brussels, Belgium.

2. Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, Netherlands.

3. Pattern Recognition Laboratory, Delft University of Technology, Delft, Netherlands.

4. Laboratoire G-Time, Université libre de Bruxelles, Brussels, Belgium.

5. Analytical, Environmental, and Geo-Chemistry, Vrije Universiteit Brussel, Brussels, Belgium.

Abstract

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.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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