代達羅斯

2023年7月14日

薩馬可能Sidik, Eos阿波羅11號拍攝了這張照片的月球代達羅斯1969年火山口。來源:美國國家航空航天局(NASA)公共領域月球表麵講述了太陽係內部。每一個隕石撞到葉子的印記,這些火山口持有的記錄事件發生在月球上在過去的40億年。但記錄很難讀懂。年齡和空間密度的月球隕石坑是解碼的關鍵指標的影響曆史,但分析這些屬性可能是耗時的,並且有時需要把樣品帶回地球。j·h·費爾韋瑟和他的同事們,在地球和空間科學發表的一篇文章中,機器學習可以快速而簡單的方法來改善我們的理解月球隕石坑。通過訓練算法對超過50000的圖片以前描述的隕石坑,研究人員能夠估計更多的年齡和密度的月球眾多標誌。首先,機器學習算法的估計實質上不同於其他研究人員已經派生。但隨著人工管理,費爾韋瑟和他的同事們能夠把他們的自動化火山口年齡的估計和密度符合先前的估計。照明條件提出了一個問題。 If craters were partially shaded by rocks or located on unevenly lit slopes, the algorithm had trouble analyzing them accurately. Excluding such craters improved the accuracy. The presence of rocks or buried craters also led the algorithm to overestimate crater ages by 10%–45%, but it could determine very accurate ages for young lunar surfaces and impact craters once rocks, buried craters, and other unwanted objects were removed from the images. The researchers caution that although machine learning can provide a wealth of information about the moon's surface, the algorithms still require careful oversight. More information: J. H. Fairweather et al, Lunar Surface Model Age Derivation: Comparisons Between Automatic and Human Crater Counting Using LRO‐NAC and Kaguya TC Images, Earth and Space Science (2023). DOI: 10.1029/2023EA002865 Journal information: