Successful Recovery of an Observed Meteorite Fall Using Drones and Machine Learning
| Autor: | Seamus L. Anderson, Martin C. Towner, John Fairweather, Philip A. Bland, Hadrien A. R. Devillepoix, Eleanor K. Sansom, Martin Cupák, Patrick M. Shober, Gretchen K. Benedix |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Earth and Planetary Astrophysics (astro-ph.EP)
FOS: Computer and information sciences Physics - Geophysics Computer Science - Machine Learning Physics - Space Physics Space and Planetary Science FOS: Physical sciences Astronomy and Astrophysics Space Physics (physics.space-ph) Machine Learning (cs.LG) Geophysics (physics.geo-ph) Astrophysics - Earth and Planetary Astrophysics |
| Popis: | We report the first-time recovery of a fresh meteorite fall using a drone and a machine learning algorithm. A fireball on the 1st April 2021 was observed over Western Australia by the Desert Fireball Network, for which a fall area was calculated for the predicted surviving mass. A search team arrived on site and surveyed 5.1 km2 area over a 4-day period. A convolutional neural network, trained on previously-recovered meteorites with fusion crusts, processed the images on our field computer after each flight. meteorite candidates identified by the algorithm were sorted by team members using two user interfaces to eliminate false positives. Surviving candidates were revisited with a smaller drone, and imaged in higher resolution, before being eliminated or finally being visited in-person. The 70 g meteorite was recovered within 50 m of the calculated fall line using, demonstrating the effectiveness of this methodology which will facilitate the efficient collection of many more observed meteorite falls. 4 Figures, 1 Table, 10 pages |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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