Sept 10 (Reuters) - IBM (IBM.N) and NASA on Thursday released an open-source AI model designed to help scientists analyze decades of lunar observation data and support plans for a sustained human presence on the Moon.

9 月 10 日（路透社）—— IBM (IBM.N) 與 NASA 於週四發佈了一款開源人工智能模型，旨在幫助科學家分析數十年的月球觀測數據，併為人類在月球長期存在的計劃提供支持。

The NASA-IBM Lunar Foundation Model is a publicly available AI tool designed to study the Moon. It was trained on more than 30 layers of data collected by nine instruments on four NASA missions, including the Lunar Reconnaissance Orbiter.

“NASA-IBM 月球基礎模型” 是一款面向公眾的人工智能工具，專為研究月球而設計。它基於來自四次 NASA 任務（包括月球勘測軌道飛行器任務）中九個儀器收集的 30 多層數據進行訓練。

The model joins IBM and NASA’s Prithvi family of open foundation models, which span geospatial, weather and other applications.

該模型加入了 IBM 與 NASA 的 “Prithvi” 系列開源基礎模型，該系列涵蓋地理空間、氣象及其他應用領域。

It can help researchers identify potential ice deposits on the Moon’s permanently shadowed regions, map craters to select safe landing sites and study volcanic features — tasks that have traditionally required scientists to sift through maps and images manually or rely on lower-resolution machine-learning tools.

它可以幫助研究人員識別月球永久陰影區潛在的冰沉積物，繪製隕石坑地圖以選擇安全的着陸點，並研究火山特徵——這些任務傳統上需要科學家手動篩選地圖和圖像，或依賴分辨率較低的機器學習工具。

In benchmark tests, the model identified key features on the lunar surface up to 23% more accurately than widely used methods, NASA and IBM said.

NASA 和 IBM 表示，在基準測試中，該模型識別月球表面關鍵特徵的準確率比廣泛使用的方法高出 23%。

Lunar ice is of particular interest to space agencies because it indicates the presence of water and oxygen, resources considered essential for a future Moon base and for producing rocket fuel for missions to Mars.

月球冰層受到各航天機構的高度關注，因為它表明存在水和氧氣，這些資源被視為未來月球基地以及為火星任務生產火箭燃料的必要條件。

NASA’s Artemis program plans to return astronauts to the Moon in 2028, testing new technology for a sustained lunar presence and future Mars missions.

NASA 的阿爾忒彌斯計劃（Artemis program）打算於 2028 年將宇航員送回月球，測試新技術以實現長期的月球存在併為未來的火星任務做準備。

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