M3 Global/Target Datasets Analysis of the Mairan Crater Area: Correlating Surface Composition and Exogenous Hydroxyls for Potential ISRU

M3 GLOBAL/TARGET DATASETS ANALYSIS OF THE MAIRAN CRATER AREA: CORRELATING SURFACE COMPOSITION AND EXOGENOUS HYDROXYLS FOR POTENTIAL ISRU. F. Colaiuta1,2, F. Tosi2, F. Zambon2, 1Department of Physics, University of Rome “La Sapienza”, Piazzale Aldo Moro 2, 00185 Rome, Italy (federico.co[email protected] , [email protected]), 2INAF – Istituto di Astrofisica e Planetologia Spaziali (INAF-IAPS), Via Fosso del Cavaliere 100, I-00133, Rome. Introduction: The study of the Moon's surface mineralogy is a crucial step in preparing for future robotic/human missions to our satellite, as it provides an opportunity to examine the properties of the terrain at targeted landing sites. In this context, the Moon Mineralogy Mapper (M3) [1,2] aboard Chandrayaan-1 has been one of the main imaging spectrometers to operate in lunar orbit, with a spectral range of 0.43-3.00 µm. It played a key role in definitively confirming the presence of water/hydroxyl across the lunar surface, particularly at polar latitudes [4]. The wavelength position and depth of the ~2.8-µm band is the driving parameter for the detection of hydroxylated minerals [3,4,5,6,7]. This study aims to confirm the presence of a thin hydroxyl layer formed by solar wind implantation [5,8] and to identify the overall structure in the Mairan crater area, thereby exploring the potential link between these elements and the overall surface chemical composition. Furthermore, it seeks to evaluate the potential extractability from oxides/hydroxyls of key compounds for lunar return, such as hydrogen and oxygen, for in-situ water utilization and potential propellant production [8,9]. Context: Located at 41°36′N 43°24′W, Crater Mairan settles on an elevated peninsula between Oceanus Procellarum to the west and Mare Imbrium to the east. Notably, data derived from the Gamma Ray Spectrometer (GRS) and Neutron Spectrometer (NS) aboard Lunar Prospector revealed that the area is rich in hydrogen [11], with concentrations comparable to those found at the lunar South Pole. Concurrently, the GRS and NS data also indicate an enrichment of Rare Earth Elements (REEs), evidenced by elevated levels of Thorium and Samarium in the region [10]. Despite these promising results, it is important to note that these instruments provide a combined analysis of both surface and subsurface chemical compositions, thus making it impossible to distinguish between the two layers. To gain deeper insight, analyzing the area using M3 data could help identify potential correlations or anti-correlations in hydrogen content between the two instruments, enabling a more accurate assessment of the abundance and nature of surface hydroxyls, which are crucial for the efficient extraction of resources. However, this analysis cannot be used to examine the abundances of REEs on the surface, due to the limited spectral and spatial resolution of M3. Methods: In our analysis, we utilized M3 data in both global and target modes in their latest release, which includes photometric calibration and thermal removal [12]. The global mode, with a spatial resolution of ~140 m/pixel and a spectral sampling of 20-40 nm [1,2], proves particularly advantageous for mapping spectral indices across broad regions, offering comprehensive spatial coverage. Conversely, the target mode has a higher spatial resolution (~70 m/pixel) and a finer spectral sampling (10 nm), but a reduced coverage. M3 data covered Mairan crater in global mode and its eastern portion also in target mode, which is intriguing for analyzing the abundance and nature of hydroxyls. To enhance the signal-to-noise ratio (SNR) to a level suitable for the application of unmixing models, we implemented a K-Means clustering algorithm. This approach reduces the number of target mode spectra involved in the unmixing process, increasing the accuracy of the methods. The uncertainties associated with these spectra are derived from regions with low reflectance values in the hyperspectral images, where the standard deviation and the number of low reflectance areas help define a Noise Equivalent Spectral Reflectance (NESR). Preliminary spectral analysis: Spectral indices analysis is conducted to explore potential correlations between the surface chemical composition and geological units within this region of interest. The first parameter we use is the 540-nm reflectance, which enables distinguishing between fresh/bright material and dark terrains, typically associated with plagioclase-rich areas and maria, respectively. This index highlights a darker terrain unit in the southern part of the image, corresponding to the edge of Oceanus Procellarum, as well as in the floor of the Mairan crater, suggesting a significant contribution of pyroxene. To analyze the composition and abundance of pyroxene, we computed the band depths/centers at ~1 µm and ~2 µm and mapped them across the entire area using global mode datasets. The distribution of the results suggests a correlation between pyroxene band depths and geological units (Fig. 1a), as previously indicated by the 540-nm reflectance. At this stage, the reliability of the method is confirmed by comparing our results for the 1-µm band depth and the 2-µm band center with those from previous studies [13], at least in a small area covering the western part of the crater, as the other regions in our datasets have not been thoroughly analyzed. The analysis of the 2.8-µm band depth does not reveal any correlation between hydroxyl abundance and geological units. However, this result may be influenced by the presence of artifacts in the M3 global data, particularly in spectral channels 81 and 84 [6]. These artifacts distort the actual band depth and should ideally be removed before analysis. Nonetheless, the limited number of data points in the global mode datasets makes artifact removal challenging, as it could introduce significant uncertainties. Furthermore, these spikes also affect the 2.8-µm band centers, although the quadratic fitting method used to model the band is less susceptible to this issue. The spectral indices analysis is enhanced by KMeans clustering on target datasets (the eastern part of our region of interest, Fig. 1b), which identifies four distinct types of terrain (Fig. 1c), ranging from high-reflectance to darker areas. The average spectra of these clusters, computed to maximize the SNR, do not exhibit spikes (Fig. 2a), thus allowing a more efficient characterization of the ~2.8-µm band. Furthermore, the identification of a ~1.4-µm band, which is an overtone of the 2.8-µm band (see correlation in Fig. 2b), enables a more accurate analysis of hydroxyl distribution and structure, as the band is affected only partially by the removal of the lunar thermal signal [12]. The presence of this supplementary band provides the opportunity to define a spectral library that accounts for the non-negligible absorption at this wavelength, potentially excluding certain trapping structures (e.g., proton implantation in glasses). Once a spectral library for hydroxylated minerals has been defined, the aim of this work is to implement both linear and non-linear unmixing methods to highlight potential correlations arising between specific geologic features and the spectral features diagnostic of hydroxyl. Initially, lunar simulants and several hydroxylated minerals will be used as endmembers to determine which mineralogy is best suited to reproduce highlands/maria terrains. Subsequently, we will refer to a larger library to assess surface mineralogy with improved accuracy. Potential In-Situ Resource extraction: The aim of this study is to determine the surface chemical composition of the region of interest, to evaluate the potential for resources extraction in the lunar nearside. Identifying oxides/hydroxyls with non-negligible abundances could allow the extraction of breathable oxygen, water, and propellant [9]. Therefore, the objectives of this work are essential for identifying the most effective extraction strategies and the necessary technologies for future missions in this area. Acknowledgments: This abstract derives from the Space It Up project, funded by the Italian Space Agency, ASI, and the Ministry of University and Research, MUR, under contract n. 2024-5-E.0 - CUP n. I53D24000060005. Figure 1 1-µm band depth of M3 ‘global mode’ data from the western area of the Mairan crater (a). RGB from ‘target mode’ data (eastern area) (b), and clusters identified by the K-Means algorithm in ‘target’ data(c). Figure 2 Continuum-removed spectra for the four identified clusters (a). The color scheme is the same of Fig. 1c. Trend of band depths at ~2.8 µm vs. ~1.4 µm (b), for target mode data. The ~1.4-µm and the ~2.8-µm bands are identified by the two red arrows in (a). References: [1] Pieters C. M. et al. (2009), Current Science Vol. 96, No. 4, pp. 500-505. [2] Green R. O. et al. (2011), Journal of Geophysical research Planets, Volume 116, Issue E10. [3] Clark R. et al. (2024) The Planetary Science Journal, Volume 5, Issue 9, id.198, 32 pp. [4] Pieters C. M. et al. (2009) Science, Vol 326, Issue 5952, pp. 568-572. [5] Hibbitts C. A. et al. (2011) Icarus, Volume 213, Issue 1, pp 64-72. [6] Yu L. et al. (2024) Geophysical research letters, Volume 51, Issue 7, e2023GL107499. [7] Sunshine J. M. et al. (2009), Science, Volume 326, Issue 5952, pp. 565-568. [8] McCord T.B. et al. (2011), Journal of Geophysical research Planets, Volume 116, Issue E6. [9] Sargeant H. M. et al. (2021), Planetary and Space Science, Volume 205, 105287. [10] Anand M. et al. (2012), Planetary and Space Science, Volume 74, Issue1, pp 42-28. [11] Prettyman T. H. et al. (2006), Journal of Geophysical research Planets, Volume 111, Issue E12. [12] Lawrence D. J. Et al. (2022), JGR Planets, Volume 127, Issue 7. [13] Clark R. et al. (2011), Journal of Geophysical research, Volume 116, Issue E6. [14] Moriarty III D.P. & Pieters C.M. (2016), Meteorites and Planetary Science, Volume 51, Issue 2.