^1,2,3Timothy K. Johnsen,^1,2,4Virginia C. Gulick
American Mineralogist 110, 685-698 Link to Article [https://doi.org/10.2138/am-2023-9072]
¹Planetary Systems Branch, NASA Ames Research Center, MS 239-20, Moffett Field, California 94035, U.S.A.
²SETI Institute, 339 Bernardo Avenue, Suite 200, Mountain View, California 94043, U.S.A.
³Computational Science Research Center, San Diego State University, 5500 Campanile Drive, San Diego, California 92182, U.S.A.
⁴Department of Planetary Sciences, Lunar and Planetary Lab, University of Arizona, 1629 E. University Boulevard, Tucson, Arizona 85721, U.S.A.
Copyright The Mineralogical Society of America

Planetary surface missions have greatly benefitted from intelligent systems capable of semi-autonomous navigation and surveying. However, instruments onboard these missions are not similarly equipped with automated science analysis classifiers onboard rovers, which can further improve scientific yield and autonomy. Here, we present both single- and multi-mineral autonomous classifiers integrated using the results from a co-registered dual-band Raman spectrometer. This instrument consecutively irradiates the same spot size on the same sample using two excitation lasers of different wavelengths (532 and 785 nm). We identify the presence of mineral groups: pyroxene, olivine, potassium feldspar, quartz, mica, gypsum, and plagioclase, in 191 rocks. These minerals are among the major rock-forming mineral groups, so their presence or absence within a sample is key for understanding rock composition and the environment in which it formed. We present machine learning methods used to train classifiers and leverage the multiple modalities of the dual-band Raman spectrometer. When testing on a novel sample set for single-mineral classification, we show accuracy scores up to 100% (varying by mineral), with a total classification rate (all minerals) of 91%. When testing on a novel set of samples for multi-mineral classification, we show accuracy scores up to 96%, with a total classification rate of 73%. We end with several hypothesis tests demonstrating that dual-band Raman spectroscopy is more robust and improves the scientific yield for mineral classification over single-band spectroscopy, especially when combined with our multimodal neural network.