Discovering compositional trends in Mars rock targets from ChemCam spectroscopy and remote imaging

Abstract

Onboard the Mars rover "Curiosity", ChemCam contains two instruments that gather geological data in the form of remote micro images (RMI) for geologic context and laserinduced breakdown spectroscopy (LIBS) for chemical composition. By analyzing the geochemical compositional depth trends of rocks, surface layers are identified that provide clues to the past atmospheric and aqueous conditions of the planet. LIBS produces the necessary data of chemical depth profiles with successive laser shots. To quickly identify these surface layers, we fit a Gaussian graphical model (GGM) to LIBS depth profiles on rock targets. The learned GGM is a visual representation of conditional dependencies among the set of shots making for faster identification of targets with interesting depth trends that warrant more in-depth analysis by experts. We show that our learned GGMs reveal information about the compositional trends present in rock targets that match observations made in more focused studies on these same targets. RMI images provide complementary details about the rock surface. Using RMI and LIBS features, we can cluster similar rock targets by the properties of the rock's surface texture and depth profile. We present results that show our machine learning methods can help analyze both the breadth and depth of data collected by ChemCam.