arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2203.01466astro-ph.EPcs.LGphysics.geo-phphysics.space-ph
收藏

使用无人机和机器学习成功恢复观测到的陨石坠落

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

Curtin University(科廷大学)

Seamus L. Anderson, Martin C. Towner, John Fairweather, Philip A. Bland, Hadrien A. R. Devillepoix, Eleanor K. Sansom, Martin Cupak, Patrick M. Shober, Gretchen K. Benedix

摘要:

我们报告使用无人机和机器学习算法首次恢复新鲜陨石坠落。2021年4月1日,沙漠火球网络在西澳大利亚州上空观测到一个火球,并计算了预测的幸存质量的坠落面积。一支搜索队抵达现场,在4天内调查了5.1平方公里的区域。一个卷积神经网络,在以前回收的带有融合外壳的陨石上训练,在每次飞行后在我们的现场计算机上处理图像。由算法识别的陨石候选者由团队成员使用两个用户界面进行分类,以消除误报。幸存的候选人被一架较小的无人机重新访问,并以更高的分辨率成像,然后被淘汰或最终亲自访问。70克的陨石回收范围内的50米计算的下降线使用,证明了这种方法的有效性,这将有助于有效地收集更多的观察到的陨石下降。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑