长兴陨石于1964年10月17日降落在上海市长兴岛。本文利用上海天文馆馆藏的第一手样品,在重新界定其分类的基础上,研究了长兴陨石经历的热变质和流体交代作用,并探讨了其中富铝球粒(简称ARC)的成因。长兴陨石是一块H5型普通球粒陨石,金...长兴陨石于1964年10月17日降落在上海市长兴岛。本文利用上海天文馆馆藏的第一手样品,在重新界定其分类的基础上,研究了长兴陨石经历的热变质和流体交代作用,并探讨了其中富铝球粒(简称ARC)的成因。长兴陨石是一块H5型普通球粒陨石,金属相丰度高达15.1vol%,橄榄石和辉石的成分均一(Fa_(17.9±0.6),Fs_(16.1±0.8),相对平均偏差PMD<5%),基质经历了明显重结晶。长兴陨石的热变质过程伴随着流体交代作用,导致了斜长石的钠长石化、球粒中微孔结构和氧化物微晶的形成、以及氯磷灰石的形成等。与前人的报道不同,长兴陨石中斜长石的成分并不均一(An_(72-9)),说明H群母体上流体交代反应的条件很可能与L和LL群母体并无显著差异。长兴陨石中共发现12个富铝球粒,平均全岩Al_(2)O_(3)含量为14.8%,平均直径(0.30±0.17mm)与富镁铁质球粒(简称FMC,0.41±0.16mm)接近;全岩SiO_(2)、FeO和CaO含量与普通球粒陨石的II型FMC相似,Ca/Al质量比(0.18~0.58)低于宇宙值(1.10;Ahrens and Von Michaelis,1968)。ARC中橄榄石、辉石的TiO_(2)和Al_(2)O_(3)含量以及斜长石的An指数均明显高于FMC。岩相学、矿物学和全岩化学结果表明长兴陨石中的ARC形成于与FMC相似的高温过程,同一类型球粒陨石中的ARC和FMC很可能形成于原行星盘中的相同或相近区域。ARC的前体物质相对于FMC富集Al、Ti等难熔元素可能是太阳星云中气-固交换反应的结果。展开更多
Lunar impact crater detection is crucial for lunar surface studies and spacecraft landing missions,yet deep learning still struggles with accurately detecting small craters,especially when relying on incomplete catalo...Lunar impact crater detection is crucial for lunar surface studies and spacecraft landing missions,yet deep learning still struggles with accurately detecting small craters,especially when relying on incomplete catalogs.In this work,we integrate Digital Elevation Model(DEM)data to construct a high-quality dataset enriched with slope information,enabling a detailed analysis of crater features and effectively improving detection performance in complex terrains and low-contrast areas.Based on this foundation,we propose a novel two-stage detection network,MSFNet,which leverages multi-scale adaptive feature fusion and multisize ROI pooling to enhance the recognition of craters across various scales.Experimental results demonstrate that MSFNet achieves an F1 score of 74.8%on Test Region1 and a recall rate of 87%for craters with diameters larger than 2 km.Moreover,it shows exceptional performance in detecting sub-kilometer craters by successfully identifying a large number of high-confidence,previously unlabeled targets with a low false detection rate confirmed through manual review.This approach offers an efficient and reliable deep learning solution for lunar impact crater detection.展开更多
文摘长兴陨石于1964年10月17日降落在上海市长兴岛。本文利用上海天文馆馆藏的第一手样品,在重新界定其分类的基础上,研究了长兴陨石经历的热变质和流体交代作用,并探讨了其中富铝球粒(简称ARC)的成因。长兴陨石是一块H5型普通球粒陨石,金属相丰度高达15.1vol%,橄榄石和辉石的成分均一(Fa_(17.9±0.6),Fs_(16.1±0.8),相对平均偏差PMD<5%),基质经历了明显重结晶。长兴陨石的热变质过程伴随着流体交代作用,导致了斜长石的钠长石化、球粒中微孔结构和氧化物微晶的形成、以及氯磷灰石的形成等。与前人的报道不同,长兴陨石中斜长石的成分并不均一(An_(72-9)),说明H群母体上流体交代反应的条件很可能与L和LL群母体并无显著差异。长兴陨石中共发现12个富铝球粒,平均全岩Al_(2)O_(3)含量为14.8%,平均直径(0.30±0.17mm)与富镁铁质球粒(简称FMC,0.41±0.16mm)接近;全岩SiO_(2)、FeO和CaO含量与普通球粒陨石的II型FMC相似,Ca/Al质量比(0.18~0.58)低于宇宙值(1.10;Ahrens and Von Michaelis,1968)。ARC中橄榄石、辉石的TiO_(2)和Al_(2)O_(3)含量以及斜长石的An指数均明显高于FMC。岩相学、矿物学和全岩化学结果表明长兴陨石中的ARC形成于与FMC相似的高温过程,同一类型球粒陨石中的ARC和FMC很可能形成于原行星盘中的相同或相近区域。ARC的前体物质相对于FMC富集Al、Ti等难熔元素可能是太阳星云中气-固交换反应的结果。
基金National Natural Science Foundation of China(12103020,12363009)Natural Science Foundation of Jiangxi Province(20224BAB211011)+1 种基金Open Project Program of State Key Laboratory of Lunar and Planetary Sciences(Macao University of Science and Technology)(Macao FDCT grant No.002/2024/SKL)Youth Talent Project of Science and Technology Plan of Ganzhou(2022CXRC9191,2023CYZ26970)。
文摘Lunar impact crater detection is crucial for lunar surface studies and spacecraft landing missions,yet deep learning still struggles with accurately detecting small craters,especially when relying on incomplete catalogs.In this work,we integrate Digital Elevation Model(DEM)data to construct a high-quality dataset enriched with slope information,enabling a detailed analysis of crater features and effectively improving detection performance in complex terrains and low-contrast areas.Based on this foundation,we propose a novel two-stage detection network,MSFNet,which leverages multi-scale adaptive feature fusion and multisize ROI pooling to enhance the recognition of craters across various scales.Experimental results demonstrate that MSFNet achieves an F1 score of 74.8%on Test Region1 and a recall rate of 87%for craters with diameters larger than 2 km.Moreover,it shows exceptional performance in detecting sub-kilometer craters by successfully identifying a large number of high-confidence,previously unlabeled targets with a low false detection rate confirmed through manual review.This approach offers an efficient and reliable deep learning solution for lunar impact crater detection.
