Successful Recovery of an Observed Meteorite Fall Using Drones and Machine Learning

Abstract

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

References in corpus (3)

• Towards planetesimals: dense chondrule clumps in the protoplanetary nebula
• Determining fireball fates using the $\alpha$-$\beta$ criterion
• Recovery of Meteorites Using an Autonomous Drone and Machine Learning

Cited by in corpus (4)

• Near Earth stream decoherence revisited: the limits of orbital similarity
• Perihelion history and atmospheric survival as primary drivers of the Earth`s meteorite record
• Comparing the dynamics of Jupiter-family Comets and comet-like fireballs
• From orbit to ground: pre-impact meteorite strewn field predictions for imminent impactors and meteorite recovery