Mineral Content Estimation of Lunar Soil of Lunar Highland and Lunar Mare Based on Diagnostic Spectral Characteristic and Partial Least Squares Method
Source abstract
Abstract
Extraction of mineral and rock information of lunar regolith is of far-reaching significance to the study of material composition, geological structure and historical evolution of lunar regolith. Visible and near-infrared spectra can reflect mineral composition information, and can be used to extract mineral composition and distribution characteristics of lunar regolith. In this paper, the LSCC (Lunar Soil Characterization Consortium) data of lunar regolith is taken as the research object. The partial least squares (PLS) regression model is used to estimate the spectra of lunar regolith measured in RELAB laboratory of Brown University. The mineral contents of plagioclase, pyroxene, olivine, ilmenite, agglutinate and volcanic glass in lunar regolith have been optimized and retrieved. The LSCC spectra of lunar regolith have been pre-processed by multivariate scattering correction (MSC), which highlight the spectral features of lunar regolith. The optimal number of principal components has been selected by cross-validation test. The PLS regression have been established for samples from lunar highland and lunar mare respectively. Two-thirds of samples have been randomly selected as experimental group to establish the prediction relationship between the spectra of lunar regolith and mineral content. The remaining one-third of samples have been used as verification group to further validate the prediction relationship. The results show that the partial least squares regression model has high accuracy and good stability. It is of theoretical and practical significance to optimize the inversion of mineral content in lunar regolith using spectral data of lunar regolith.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Marine and environmental studies · Geochemistry and Geologic Mapping · Isotope Analysis in Ecology
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Geosciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
Hebei Bureau of Coal Geological Exploration, Shijiazhuang 072750, China
School of Geosciences, University of Edinburgh, King’s Buildings, James Hutton Rd., Edinburgh EH9 3FE, UK
Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100021, China
References (26)
Show source-provided references
1. Thermal infrared spectroscopy and partial least squares regression to determine mineral modes of granitoid rocks10.1029/2011GC004004 ↗
2. Estimating soil salinity in Pingluo County of China using QuickBird data and soil reflectance spectra
3. Automated lithological mapping using airborne hyperspectral thermal infrared data: A case study from Anchorage Island, Antarctica10.1016/j.rse.2016.01.022 ↗
4. The beneficiation of lunar regolith for space resource utilisation: A review10.1016/j.pss.2020.104879 ↗
5. Radiative transfer modeling for quantifying lunar surface minerals, particle size, and submicroscopic metallic Fe
6. Lunar mare soils: Space weathering and the major effects of surface-correlated nanophase Fe10.1029/2000JE001402 ↗
7. Spectroscopy of rocks and minerals, and principles of spectroscopy
8. Burns, R.G. (1993). Mineralogical Applications of Crystal Field Theory, Cambridge University Press.10.1017/CBO9780511524899 ↗
9. Quantifying lunar soil composition with partial least squares modeling of reflectance
10. Taylor, L.A., Pieters, C.M., Morris, R.V., McKay, D.S., Pachen, A., and Wentworth, S. (1999, January 15–29). Integration of the chemical and mineralogical characteristics of lunar soils with reflectance spectroscopy. Proceedings of the Lunar and Planetary Science Conference, 30th Annual Lunar and Planetary Science Conference, Houston, TX, USA.
11. Mineralogical characterization of lunar highland soils
12. Mineralogical and chemical characterization of lunar highland soils: Insights into the space weathering of soils on airless bodies10.1029/2009JE003427 ↗
13. Quantitative Analysis of Cotton Content in Cotton-terylene Textile by Near Infrared Technique
14. Analysis of Lead in Soil with Partial Least Square Regression (PLS) Method and Field PortableX-Ray Fluorescence (FPXRF) Analyzer
15. Prediction of Acetamiprid Residues by Fluorescence Spectroscopy Based on PLS
16. Pattern recognition: Finding and using regularities in multivariate data
17. Hu, D.G., and Zhang, R.C. (1990). Multivariate Data Analysis Method—Pure Algebraic Processing, Nankai University Press.
18. Zhou, P., Zhao, Z., Wei, G., and Huo, H.Y. (2022). Two Simulated Spectral Databases of Lunar Regolith: Method, Validation, and Application. Remote Sens., 14.10.3390/rs14020277 ↗
19. Study on prediction model of seam gas content based partial least squares regression
20. Partial Least Squares Based Band Selection for Hyperspectral Imagery
21. Near Infrared Spectral Analysis Based on Partial Least Squares
22. On the mean accuracy of statistical pattern recognizers10.1109/TIT.1968.1054102 ↗
23. Research on Spectral Pretreatment Methods for Quantitative Analysis of Wet Gluten in Flour by Near Infrared Spectroscopy
24. Chu, X.L. (2011). Molecular Spectroscopy Analytical Echnology Combined with Chemomentrics and Its Applications, Chemical Industry Press.
25. Determination of Crystallinity in Neosinocalamus affinins Based on Near Infrared Spectroscopy and PLS Methods
26. Inversion of Geochemical Compositions of Basalts Based on Field Measured Spectra