Lunar volcanic domes are essential windows into the local magmatic activities on the Moon. Classification of domes is a useful way to figure out the relationship between dome appearances and formation processes. Previous studies of dome classification were manually or semi-automatically carried out either qualitatively or quantitively. We applied an unsupervised machine-learning method to domes that are annularly or radially distributed around Gardner, a unique central-vent volcano located in the northern part of the Mare Tranquillitatis. High-resolution lunar imaging and spectral data were used to extract morphometric and spectral properties of domes in both the Gardner volcano and its surrounding region in the Mare Tranquillitatis. An integrated robust Fuzzy C-Means clustering algorithm was performed on 120 combinations of five morphometric (diameter, area, height, surface volume, and slope) and two elemental features (FeO and TiO₂ contents) to find the optimum combination. Rheological features of domes and their dike formation parameters were calculated for dome-forming lava explanations. Results show that diameter, area, surface volume, and slope are the selected optimum features for dome clustering. 54 studied domes can be grouped into four dome clusters (DC1 to DC4). DC1 domes are relatively small, steep, and close to the Gardner volcano, with forming lavas of high viscosities and low effusion rates, representing the latest Eratosthenian dome formation stage of the Gardner volcano. Domes of DC2 to DC4 are relatively large, smooth, and widely distributed, with forming lavas of low viscosities and high effusion rates, representing magmatic activities varying from Imbrian to Eratosthenian in the northern Mare Tranquillitatis. The integrated algorithm provides a new and independent way to figure out the representative properties of lunar domes and helps us further clarify the relationship between dome clusters and local magma activities of the Moon.

中文翻译：

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月球加德纳地区穹顶上的无监督机器学习：对月亮上的穹顶分类和局部岩浆活动的影响

月球火山圆顶是进入月球局部岩浆活动的重要窗口。圆顶的分类是找出圆顶外观与形成过程之间关系的有用方法。以前对圆顶分类的研究是定性或定量地手动或半自动进行的。我们将无监督的机器学习方法应用于围绕Gardner环形或径向分布的圆顶，该圆顶是位于Mare Tranquillitatis北部的独特中央通风口火山。高分辨率的月球成像和光谱数据被用于提取Gardner火山及其母马Tranquillitatis周围地区圆顶的形态和光谱特性。₂内容）以找到最佳组合。计算圆顶的流变特性及其堤防参数，以解释形成圆顶的熔岩。结果表明，直径，面积，表面体积和坡度是圆顶聚类的最佳选择。54个已研究的球型摄像机可以分为四个球型集群（DC1至DC4）。DC1穹顶相对较小，陡峭并靠近Gardner火山，形成高粘度和低渗出率的熔岩，代表了Gardner火山最新的埃拉托森式圆顶形成阶段。DC2到DC4的穹顶相对较大，光滑且分布广泛，形成了低粘度和高渗出率的熔岩，代表了北马雷安克拉利蒂斯地区英布纪至埃拉托森时期的岩浆活动。