Raphael Thys
Seamus Anderson with the meteorite found using drone technology.
Drone technology has allowed researchers from Curtin University to study the potential of mining asteroids as a source of rare elements, following the successful discovery of a meteorite in Western Australia.
The researchers recovered a freshly fallen meteorite after pinpointing its exact location on the Nullarbor Plain in WA, with a new technique that uses a drone to collect footage of the landscape that is then scanned using artificial intelligence.
Curtin’s Space Science and Technology Centre (SSTC) lead researcher and graduate student Seamus Anderson said the find at Kybo Station was a successful demonstration of the new method, which had the potential to greatly increase the number of recovered meteorites, particularly those observed as they fall through the atmosphere.
Anderson said such meteorites, which are tracked by the Desert Fireball Network (DFN) were special because they gave a geologic sample of the particular region of the solar system they originated from, contributing to an overall understanding of the geology of the solar system.
“New solutions such as our drone technique help make investments in space science and the study of meteorites more cost-effective and impactful,” Anderson said.
“Beyond increasing our understanding of the solar system, the study of meteorites is useful for many reasons. For example, meteorites often contain a higher concentration of rare and valuable elements such as cobalt, which is crucial to the construction of modern batteries.
“Also, by gaining a better understanding of how extra-terrestrial material is distributed throughout the solar system, we may one day mine asteroids for precious resources, instead of scrounging for the finite amounts of them on Earth and perhaps harming precious ecosystems in the process.”
Anderson said the recovery involves a camera-fitted drone collecting images of the fall zone, which are transferred to field computer where an algorithm scans each image for meteorites and features that resemble them.
“Although our algorithm was ‘trained’ on data collected from past meteorite searches, we brought with us previously recovered meteorites and imaged them on the ground at the fall site, to create local data with which to further train the algorithm,” Anderson said.
“Meteorite searches usually involve a group of people walking over a large predicted impact area but our new method requires only about one tenth the amount of labour and time, and has a much higher likely success rate, which is evident in the fact we located and recovered the meteorite within four days of being on site at Kybo Station.”
Anderson said other potential applications for the new approach using drones and artificial intelligence include wildlife management and conservation.
“Our model could be easily retrained to detect objects other than meteorites, such as plants and animals,” he said.
The research paper, Successful Recovery of an Observed Meteorite Fall Using Drones and Machine Learning can be found online here.