¹Max Collinet,¹Ana-Catalina Plesa,²Timothy L. Grove,¹Sabrina Schwinger,^3,1Thomas Ruedas,¹Doris Breuer
Journal of Geophysical Research (Planets) (In Press) Link to Article [https://doi.org/10.1029/2021JE006985]
¹German Aerospace Center (DLR), Institute of Planetary Research, Rutherfordstraße 2, 12 489 Berlin Germany
²Massachusetts Institute of Technology, Department of Earth, Atmospheric and Planetary Sciences, 77 Massachusetts Avenue, MA, 02 139 USA
³Museum für Naturkunde Berlin, Impact and Meteorite Research, Invalidenstraße 43, 10 115 Berlin Germany
Published by arrangement with John Wiley & Sons

Martian basalts identified by rover in-situ analyses and the study of meteorites represent a direct link to the melting process in the planet’s interior and can be used to reconstruct the composition of the mantle and estimate its temperature. Experimentally calibrated numerical models are powerful tools to systematically search for the mantle compositions and melting conditions that can produce melts similar to primary basalts. However, currently available models are not suitable for modeling the melting of FeO-rich peridotites. In this study, we present experiments performed at 1.0 and 2.4–2.6 GPa on a primitive Martian mantle with various P2O5 contents. We use the new experiments together with a comprehensive database of previous melting experiments to calibrate a new model called MAGMARS. This model can reproduce the experimental melt compositions more accurately than Gibbs free energy minimization software (e.g. pMELTS) and can simulate near-fractional polybaric melting of various mantle sources. In addition, we provide an updated thermobarometer that can estimate the P–T melting conditions of primary melts and can be used as a prior step to constrain the input parameters of the MAGMARS melting model. We apply MAGMARS to estimate the source composition of the Adirondack-class basalts and find that melting a depleted mantle, at 2.3–1.7 GPa (Tp=1390±40°C) can best explain their bulk composition and K2O/Na2O ratio. MAGMARS represents a fast and accurate alternative to calculate the composition of the Martian primary melts and can be used as a stand-alone package or integrated with geodynamical models or other independent modeling software.

¹L.B.Breitenfeld et al. (>10)
Journal of Geophysical research (Planets) (In Press) Link to Article [https://doi.org/10.1029/2021JE007035]
¹Department of Geosciences, Stony Brook University, Stony Brook, NY, USA
Published by arrangement with John Wiley & Sons

Planetary surfaces can be complex mixtures of coarse and fine particles that exhibit linear and nonlinear mixing behaviors at mid-infrared (MIR) wavelengths. Machine learning multivariate analysis can estimate modal mineralogy of mixtures and is favorable because it does not assume linear mixing across wavelengths. We used partial least squares (PLS) and least absolute shrinkage and selection operator (lasso), two types of machine learning, to build MIR spectral models to determine the surface mineralogy of the asteroid (101955) Bennu using OSIRIS-REx Thermal Emission Spectrometer (OTES) data. We find that PLS models outperform lasso models. The cross-validated root-mean-square error of our final PLS models (consisting of 317 unique spectra of samples derived from 13 analog mineral samples and eight meteorites) range from ∼4–13 vol% depending on the mineral group. PLS predictions in vol% of Bennu’s average global composition are 78% phyllosilicate, 9% olivine, 11% carbonates, and 6% magnetite. Pyroxene is not predicted for the global average spectrum, though it has been detected in small amounts on Bennu. These mineral abundances confirm previous findings that the composition of Bennu is consistent with CI/CM chondrites with high degrees of aqueous alteration. The predicted mineralogy of two previously identified OTES spectral types vary minimally from the global average. In agreement with previous work, we interpret OTES spectral differences as primarily caused by relative abundances of fine particulates rather than major compositional variations.