Identifying Minerals on Mars Through VNIR and Mid-IR Spectral Deconvolution based on the Martian Meteorites

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3662 Meteorites, 3665 Mineral Occurrences And Deposits, 3934 Optical, Infrared, And Raman Spectroscopy, 5464 Remote Sensing, 6225 Mars

Scientific paper

Coordinated VNIR and mid-IR spectral analyses of the mineralogy of Mars are important in order to fully understand the composition of the surface. We have been refining and testing the Modified Gaussian Model (MGM) developed by Sunshine et al. (1990) on VNIR martian meteorite spectra. This technique enables detection of minerals based on the electronic absorptions in the spectra. Deconvolutions of thermal emission spectrometer (TES) spectra of the same samples are also underway based on spectral features due to vibrational absorptions in the minerals. The martian meteorites included in the study are ALH 84001, EETA 79001, Los Angeles, Dar al Gani 670, and NWA 1068. These samples contain primarily pyroxenes ranging from orthopyroxene to pigeonite to augite, feldspar (and maskelynite), fayalitic and fosteritic olivine, silica, and glass. We are comparing the deconvolution results of the two spectral regions with each other and with the meteorite petrology from other studies. Combining spectral analyses of Martian meteorite chips and powders enables characterisation of spectral bands for remote detection of potential source regions for meteorite-like rocks on the surface of Mars. Although some surface regions have been identified that exhibit the spectral properties of Martian meteorites, these make up only a small fraction of the surface (Hamilton et al., 2003). Deconvolving the spectra of the meteorites down to the minerals present in these rocks also enables spectral searches of one or more mineral components in the Martian spectra without looking for the whole meteorite spectral signature on the surface. We will be applying these techniques to the Imager for Mars Pathfinder (IMP) extended visible-region spectra, the imaging spectrometer for Mars (ISM), and TES datasets. Hamilton, V. E. et al. 2003, MAPS, in press. Sunshine, J. M. et al. 1990, JGR, 95, 6955.

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