Machine learning for recognizing minerals from multispectral data.

Machine Learning (ML) has found several applications in spectroscopy, including recognizing minerals and estimating elemental composition. ML algorithms have been widely used on datasets from individual spectroscopy methods such as vibrational Raman scattering, reflective Visible-Near Infrared (VNIR), and Laser-Induced Breakdown Spectroscopy (LIBS). We firstly reviewed and tested several ML approaches to mineral classification from the existing literature, and identified a novel approach for using Deep Learning algorithms for mineral classification from Raman spectra, that outperform previous state-of-the-art methods.

The PANGAEA mineralogical database.

Future human missions to the surface of the Moon and Mars will involve scientific exploration requiring new support tools to enable rapid and high quality science decision-making. Here, we describe the PANGAEA (Planetary ANalogue Geological and Astrobiological Exercise for Astronauts) Mineralogical Database developed by ESA (European Space Agency): a catalog of petrographic and spectroscopic information on all currently known minerals identified on the Moon, Mars, and associated with meteorites. The catalog also includes minerals found in the analog field sites used for ESA's geology and astrobiology training course PANGAEA, to broaden the database coverage.