基于Geant4模拟的嫦娥七号中子伽玛谱仪月表水含量反演模型
doi: 10.11728/cjss2026.02.2025-0144 cstr: 32142.14.cjss.2025-0144
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陈冠宇 男, 1996年4月出生于黑龙江省哈尔滨市, 现为中国科学院紫金山天文台在读博士研究生.主要研究方向为中子伽马射线探测技术与探测器标定方法. E-mail: chengy@pmo.ac.cn -
马涛 男, 中国科学院紫金山天文台暗物质与空间天文实验室正高级工程师, 主要研究方向空间高能粒子探测技术. E-mail: matao@pmo.ac.cn
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Geant4 Simulation on Lunar Surface Water Content Inversion Using the Chang’E-7 Neutron and Gamma-ray Spectrometer
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摘要: 嫦娥七号计划于2026年发射, 开展月球南极永久阴影区内的水冰探测是其重要的科学目标之一. 基于Geant4工具包构建嫦娥七号有效载荷月球中子伽玛谱仪(LNGS)的精细模型, 建立月表水含量与超热中子计数率之间的定量反演关系. 通过模拟银河宇宙射线(GCR)轰击月表产生的次级中子能谱, 结合中国散裂中子源(CSNS)的束流标定实验验证, 得到如下结果探测器建模的模拟效率与实验数据吻合良好(相对误差<6%); 探测器具备区分不同含水量土壤的能力; 月表水冰质量含量在0.01%～20%范围内时, 超热中子计数率随氢含量增加呈显著下降趋势, 其关系符合修正的Lawrence模型(R2=0.9993). 研究为嫦娥七号在轨数据解译提供了可靠的理论模型, 并为月球资源原位利用的探测技术发展奠定了基础.Abstract: The Chang’E-7 lunar mission, scheduled for launch in 2026, has the primary scientific objective of detecting water-ice deposits within the Permanently Shadowed Regions (PSRs) at the lunar south pole. Understanding the distribution and concentration of lunar water ice is crucial for both fundamental science and future In-situ Resource Utilization (ISRU). In this study, we developed a high-fidelity model of the Chang’E-7 Lunar Neutron and Gamma-ray Spectrometer (LNGS) payload using the Geant4 toolkit (Version 10.07.p02) and established a quantitative inversion relationship between lunar surface water content and epithermal neutron count rates. The LNGS model, constructed by importing a detailed CAD model into Geant4, was rigorously validated against neutron beam calibration experiments conducted at the China Spallation Neutron Source (CSNS) Back-n facility. The results are as follows. The detector model shows excellent agreement with experimental data across the 0.4 eV to 1000 eV energy range, with a relative error of less than 6%, confirming the accuracy of the mass modeling and simulation setup. LNGS exhibits significant capability in discriminating soils with varying water content, as evidenced by both simulation and ground-based validation experiments using layered soil and water samples. Within the water-ice content range of 0.01% to 20%, simulations of galactic cosmic ray (GCR) bombardment and subsequent neutron transport show that the epithermal neutron (400 ～700 keV) count rate decreases significantly with increasing hydrogen abundance. This relationship follows a modified Lawrence model with an exceptional coefficient of determination (R2 = 0.9993). The slight parameter differences compared to the original Lawrence model are attributed to the different simulation tools, lunar regolith composition models, and distinct detector designs and energy responses between LNGS and the Lunar Prospector neutron spectrometer. This study provides a robust theoretical framework and a specific, validated inversion model for interpreting Chang’E-7 orbital neutron data, directly enabling the mapping of hydrogen concentrations from measured count rates. It establishes fundamental technical support for the development of in-situ resource utilization technologies on the Moon and paves the way for high-precision assessment of water ice resources in the lunar polar regions.
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Key words:
- Geant4 /
- Neutron and gamma-ray spectrometer /
- Lunar polar regions /
- Water-ice content
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图 1 嫦娥七号月球中子伽玛谱仪结构(a)及 EJ254晶体尺寸(b)
Figure 1. Schematic of the Chang’E-7 lunar neutron and gamma-ray spectrometer (a), and size of the EJ254 scintillator of a detector unit (b)
图 3 宇宙线质子与α粒子的微分能谱(Φ=650 MV)
Figure 3. Differential energy spectra of cosmic ray protons and α particles(Φ=650 MV)
图 4 中子能量窗口为1～1.258 eV的脉冲幅度谱(已扣除本底)
Figure 4. Pulse amplitude spectra with a neutron energy window of 1～1.258 eV (background subtracted)
图 5 0.4～1000 eV中子模拟效率曲线与实验数据点(a)及实验数据与模拟曲线之间的残差(b)
Figure 5. shows the 0.4～1000 eV neutron simulation efficiency curve with experimental data points (a), and gives the residual plot between the experimental data and the simulated curves (b)
图 6 1层土和2层水情况下的实验现场
Figure 6. Diagram of the experimental site under 1 layer of soil and 2 layers of water
图 7 模拟得到的中子束流穿过不同材料后的能谱
Figure 7. Energy spectra of neutron beams after passing through different materials obtained by simulation
图 8 通过实验与模拟得到的超热中子计数率随水层厚度的变化关系
Figure 8. Relationship between the count rates of epithermal neutrons obtained through experiments and simulations and the thickness of the water layer
图 9 模拟月表不同含水量情况下的中子逃逸能谱
Figure 9. Neutron leakage spectra of the lunar surface under different water contents
图 10 LNGS超热中子计数与水含量之间的关系
Figure 10. Relationship between the LNGS epithermal neutron counts and water contents
表 1 干燥月壤元素组分
Table 1. Dry lunar soil elemental composition
元素名称 含量占比 O 0.41739 Na 0.00292 Mg 0.06162 Al 0.06061 Si 0.19026 K 726.24 ×10–6 Ca 0.07541 Ti 0.05144 Cr 0.00287 Mn 0.00176 Fe 0.13496 Sm 8.3342 ×10–6 Eu 1.8164 ×10–6 Gd 10.997 ×10–6 Th 0.9449 ×10–6 
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表 2 水含量实验数据
Table 2. Science data of the water content experiment
实验 模拟 能量范围/keV 1层土 1 1 12.6～25.1 1层土+1层水 0.3513±0.00466 0.3389±0.039 39.8～63.1 1层土+2层水 0.3103±0.00438 0.3086±0.031 100～199.5
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