¹Maxim Isachenkov,¹Svyatoslav Chugunov,²Zoe Landsman,¹Iskander Akhatov,²Anna Metke,¹Andrey Tikhonov,¹Igor Shishkovsky
Icarus (in Press) Link to Article [https://doi.org/10.1016/j.icarus.2021.114873]
¹Skolkovo Institute of Science and Technology, Center for Design, Manufacturing, and Materials, 30 Bolshoy boulevard, bld, 1121205 Moscow, Russian Federation
²CLASS Exolith Lab, Florida Space Institute, 12354 Research Parkway, Orlando, FL 32826, United States of America
Copyright Elsevier

Lunar regolith is the most critical material for the in-situ resource utilization in the crewed Moon exploration missions. This natural material can be utilized for the additive manufacturing of concrete or ceramic parts on the Moon’s surface to support permanent human presence on the surface of Earth’s natural satellite. Due to the scarcity of regolith on Earth, its simulants are used in lab research to prepare the technology for Moon missions. The present study is devoted to the characterization of lunar regolith simulant material, recently developed by the University of Central Florida, that is considered as a suitable material for regolith-focused additive manufacturing technologies. This paper describes the characterization of the LHS-1 and LMS-1 simulants using XRF, XRD, SEM, EDX, DTA, TGA, UV/Vis/NIR spectroscopy, and Laser diffractometry methods to provide data on their mineral, chemical, and fractional composition, as well as, on their morphology and optical properties. The results were compared to the data of the previously developed simulants and the original lunar samples delivered by Apollo and Luna missions. It was found that LHS-1 and LMS-1 simulants well mimic the primary properties of the original lunar regolith and can be potentially used for ISRU research tasks.

¹Emanuele Plebani,²Bethany L.Ehlmann,^2,3Ellen K.Leask,⁴Valerie K.Fox,¹M. Murat Dundar
Icarus (in Press) Link to Article [https://doi.org/10.1016/j.icarus.2021.114849]
¹Computer and Information Sciences Department, Indiana University – Purdue University, Indianapolis, 46202, IN, USA
²Div. of Geological & Planetary Sciences, California Institute of Technology, Pasadena, 91125, CA, USA
³John Hopkins University Applied Physics Laboratory, Laurel, 20723, MD, USA
⁴Department of Earth and Environmental Sciences, University of Minnesota, Minneapolis, 55455, MN, USA
Copyright Elsevier

Hyperspectral images collected by remote sensing have played a significant role in the discovery of aqueous alteration minerals, which in turn have important implications for our understanding of the changing habitability on Mars. Traditional spectral analyses based on summary parameters have been helpful in converting hyperspectral cubes into readily visualizable three channel maps highlighting high-level mineral composition of the Martian terrain. These maps have been used as a starting point in the search for specific mineral phases in images. Although the amount of labor needed to verify the presence of a mineral phase in an image is quite limited for phases that emerge with high abundance, manual processing becomes laborious when the task involves determining the spatial extent of detected phases or identifying small outcrops of secondary phases that appear in only a few pixels within an image. Thanks to extensive use of remote sensing data and rover expeditions, significant domain knowledge has accumulated over the years about mineral composition of several regions of interest on Mars, which allow us to collect reliable labeled data required to train machine learning algorithms. In this study we demonstrate the utility of machine learning in two essential tasks for hyperspectral data analysis: nonlinear noise removal and mineral classification. We develop a simple yet effective hierarchical Bayesian model for estimating distributions of spectral patterns and extensively validate this model for mineral classification on several test images. Our results demonstrate that machine learning can be highly effective in exposing tiny outcrops of specific phases in orbital data that are not uncovered by traditional spectral analysis. We package implemented scripts, documentation illustrating use cases, and pixel-scale training data collected from dozens of well-characterized images into a new toolkit. We hope that this new toolkit will provide advanced and effective processing tools and improve community’s ability to map compositional units in remote sensing data quickly, accurately, and at scale.

^1,2A.Khan,³P.A.Sossi,³C.Liebske,⁴A.Rivoldini,¹D.Giardini
Earth and Planetary Science Letters 578, 117330 Link to Article [https://doi.org/10.1016/j.epsl.2021.117330]
¹Institute of Geophysics, ETH Zürich, Zürich, Switzerland
²Physik Institut, University of Zürich, Zürich, Switzerland
³Institute of Geochemistry and Petrology, ETH Zürich, Zürich, Switzerland
⁴Royal Observatory of Belgium, Brussels, Belgium
Copyright Elsevier

Constraints on the composition of Mars principally derive from chemical analyses of a set of Martian meteorites that rely either on determinations of their refractory element abundances or isotopic compositions. Both approaches, however, lead to models of Mars that are unable to self-consistently explain major element chemistry and match its observed geophysical properties, unless ad hoc adjustments to key parameters, namely, bulk Fe/Si ratio, core composition, and/or core size are made. Here, we combine geophysical observations, including high-quality seismic data acquired with the InSight mission, with a cosmochemical model to constrain the composition of Mars. We find that the FeO content of Mars’ mantle is 13.7±0.4 wt%, corresponding to a Mg# of 0.81±0.01. Because of the lower FeO content of the mantle, compared with previous estimates, we obtain a higher mean core density of 6150±46 kg/m³ than predicted by recent seismic observations, yet our estimate for the core radius remains consistent around 1840±10 km, corresponding to a core mass fraction of 0.250±0.005. Relying on cosmochemical constraints, volatile element behaviour, and planetary building blocks that match geophysical and isotopic signatures of Martian meteorites, we find that the liquid core is made up of 88.4±3.9 wt% Fe-Ni-Co with light elements making up the rest. To match the mean core density constraint, we predict, based on experimentally-determined thermodynamic solution models, a light element abundance in the range of ≈9 wt% S, ⩾3 wt% C, ⩽2.5 wt% O, and ⩽0.5 wt% H, supporting the notion of a volatile-rich Mars. To accumulate sufficient amounts of these volatile elements, Mars must have formed before the nebular gas dispersed and/or, relative to Earth, accreted a higher proportion of planetesimals from the outer protoplanetary disk where volatiles condensed more readily.