Overview of the Lunar In Situ Resource Utilization Techniques for Future Lunar Missions

Along with the rapid development of space technology,extraterrestrial exploration has gradually tended to further-distanced and longer-termed planet exploration.As the first step of an attempt for humans to build a perpetual planet base,building a lunar base by in situ resource utilization(ISRU)will drastically reduce the reliance of supplies from Earth.Lunar resources including mineral resources,water/ice resources,volatiles,and solar energy will contribute to the establishment of a lunar base for long-term life support and scientific exploration missions,although we must consider the challenges from high vacuum,low gravity,extreme temperature conditions,etc.This article provides a comprehensive review of the past developing processes of ISRU and the latest progress of several ISRU technologies,including in situ water access,in situ oxygen production,in situ construction and manufacture,in situ energy utilization,and in situ life support and plant cultivation on the Moon.Despite being able to provide some material and energy supplies for lunar base construction and scientific exploration,the ISRU technologies need continuous validation and upgrade to satisfy the higher requirements from further lunar exploration missions.Ultimately,a 3-step development plan for lunar ISRU technologies in the next decade is proposed,which consists of providing technological solutions,conducting technical verification on payloads,and carrying out in situ experiments,with the ultimate aim of establishing a permanent lunar station and carrying out long-term lunar surface scientific activities.The overview of ISRU techniques and our suggestions will provide potential guidance for China’s future lunar exploration missions.

Application of cluster analysis to geochemical compositional data for identifying ore-related geochemical anomalies

Cluster analysis is a well-known technique that is used to analyze various types of data. In this study, cluster analysis is applied to geochemical data that describe 1444 stream sediment samples collected in northwestern Xinjiang with a sample spacing of approximately 2 km. Three algorithms （the hierarchical, k-means, and fuzzy cmeans algorithms） and six data transformation methods （the z-score standardization, ZST; the logarithmic transformation, LT; the additive log-ratio transformation, ALT; the centered log-ratio transformation, CLT; the isometric log-ratio transformation, ILT; and no transformation, NT） are compared in terms of their effects on the cluster analysis of the geochemical compositional data. The study shows that, on the one hand, the ZST does not affect the results of columnor variable-based （R-type） cluster analysis, whereas the other methods, including the LT, the ALT, and the CLT, have substantial effects on the results. On the other hand, the results of the row- or observation-based （Q-type） cluster analysis obtained from the geochemical data after applying NT and the ZST are relatively poor. However, we derive some improved results from the geochemical data after applying the CLT, the ILT, the LT, and the ALT. Moreover, the k-means and fuzzy cmeans clustering algorithms are more reliable than the hierarchical algorithm when they are used to cluster the geochemical data. We apply cluster analysis to the geochemical data to explore for Au deposits within the study area, and we obtain a good correlation between the results retrieved by combining the CLT or the ILT with the k-means or fuzzy c-means algorithms and the potential zones of Au mineralization. Therefore, we suggest that the combination of the CLT or the ILT with the k-means or fuzzy c-means algorithms is an effective tool to identify potential zones of mineralization from geochemical data.

Estimation of copper concentration of rocks using hyperspectral technology

Rock geochemical information is important for mineral exploration and provides a theoretical basis for the rapid delineation of hidden minerals. Remote sensing technology provides the possibility of rapid and large-scale extraction of geochemical information from the earth’s surface. This study analyzed the relationship between copper concentration and rock spectra by first collecting 222 rock samples, and then measuring the copper concentration of rock samples in the laboratory and reflectance spectra using an ASD FieldSpec3 portable spectrometer. It finally established quantitative relationships between the original spectra, first-order derivative spectra and second-order derivative spectra and copper concentration, respectively, using the partial least squares support vector machine method (PLS-SVM). The results show that 1) The estimation accuracy of using second-order derivatives spectra as input parameters to establish a model for estimating copper concentration is the highest, and the determined coefficient (R2) between the predicted value and real value reaches 0.54. 2) When the copper concentration is less than 80 mg/kg, the inversion model of copper concentration established using PLS-SVM obtains a good result. The R2 between the predicted copper concentration and the real copper concentration reached 0.70248. When the copper concentration is greater than 80 mg/kg, the inversion model of copper concentration established using partial least squares (PLS) obtains a good result. The R2 between the predicted copper concentration and the real copper concentration reached 0.49. The R2 between real copper concentration and copper predicted by the method of piecewise separate modeling reaches 0.816.Therefore, the method of segmental modeling has great potential to improve the accuracy of copper concentration inversion.