Clustering Supported Classification of ChemCam Data From Gale Crater, Mars

Abstract

The Mars Science Laboratory (MSL) rover Curiosity landed successfully in Gale crater on Mars on August 6, 2012. One of the primary mission goals of MSL is to determine whether Mars was ever habitable, for which the Gale crater and in particular its central mountain Aeolis Mons (informally known as Mt. Sharp) was chosen as the science target (Grotzinger et al., 2012). From orbit, it was recognized that the lower layered sedimentary strata of Mt. Sharp contains a record of environmental changes including diverse aqueous conditions over time (Milliken et al., 2010). Curiosity landed at the Bradbury site, northwest of Mt. Sharp, near the base of the alluvial fan of Peace Vallis, and explored the rocks of Aeolis Palus during the first Mars year of the mission. On sol 753, Curiosity reached the geologic transition to the Mt. Sharp group and has continued to climb the mound since then. The ancient habitability could be confirmed at Yellowknife Bay, a location in Aeolis Palus, due to several detections of early diagenesis products, which together suggest the existence of an aqueous environment, implying a higher potential for habitable conditions. (Grotzinger et al., 2014(Grotzinger et al., , 2015.Several scientific instruments belong to Curiosity's payload for the characterization of Gale crater's geological evolution and one of them is the "Chemistry and Camera" (ChemCam) instrument (Maurice et al., 2012;Wiens et al., 2012). ChemCam has the capability to perform rapid multi-elemental analyses of rocks and soils from a distance with the laser-induced breakdown spectroscopy (LIBS) technique. Due to its regular use-almost every sol-ChemCam has collected a very large and unique LIBS data set containing more than 800,000 single shot LIBS spectra. Such a data set is well suited for the application of machine learning algorithms in order to identify similar targets and predict classes of new measured targets. From

An Insight Into Ancient Aeolian Processes and Post‐Noachian Aqueous Alteration in Gale Crater, Mars, Using ChemCam Geochemical Data From the Greenheugh Capping Unit

“…Complete linkages and Euclidean distances were used as these methods provided the best fit for the data set showing the most defined clusters and was successfully used in a previous investigation of the Stimson formation (Bedford, Schwenzer, et al, 2020). Clustering methods have also been successfully employed in other studies of Gale crater sediments (Gasnault et al, 2013(Gasnault et al, , 2019Rammelkamp et al, 2021). Variables were standardized in order to minimize the effect of scale differences.…”

Post-landing major element quantification using SuperCam laser induced breakdown spectroscopy

“…Precision is important in determining whether groups of targets observed by the same instrument can be distinguished from one another [81]. For example, to understand if the rover has entered a new geological formation by testing for a change in the distribution of chemical abundances (e.g., [18,82,83]).…”

Mapping Lunar Swirls with Machine Learning: The Application of Unsupervised and Supervised Image Classification Algorithms in Reiner Gamma and Mare Ingenii

“…Unsupervised learning is often performed as a step prior to supervised classification. Some of the more common unsupervised clustering algorithms include K-means (Asada et al 2010;Anderson & Bell 2013;Collier et al 2020;Rammelkamp et al 2021;Kerner et al 2022), Density-Based Spatial Clustering of Applications with Noise, Gaussian Mixture Models, and Agglomerative Hierarchal Clustering (Sarker 2021).…”