For many regions on Mars the surface composition and its geological history has been debated in the literature. Because of the limited surface coverage of in‐situ measurements, either new data, or new processing methodologies are required to get a better understanding of the Martian geology. This paper presents the results of a multi‐variate, unsupervised, analysis on under‐utilized CRISM (Compact Reconnaissance Imaging Spectrometer) multispectral mapping mode dataset for surface type analysis. The devised summary products of Pelkey et al. [2007] and Viviano‐Beck et al. [2014] are averaged for the CRISM ~5*5^o mosaic grids and used to analyze the variability in this dataset. The averaged summary product values are studied using correlation coefficients calculated between summary products, as well as the correlation with dust coverage and elevation. The degree of correlation is used to interpret the summary products for the global distribution of mafic and secondary minerals and the effect of external factors such as ice, atmosphere and dust coverage. With unsupervised clustering all grid pixels are classified based on the spectral variability. These clusters are plotted as global maps and interpreted for geological variations in the CRISM data. Some clusters spatially correspond with previously recognized compositional distinct regions Northern Lowlands, Southern Highlands, Meridiani Planum, Syrtis Major, and Nili Fossae. Several other clusters are described here for the first time such as Solis Planum, Ophir Planum and Hellas Basin. These regions cover known geological units but the interpretation of the spectral variability is uncertain whether it relates to geological or external factors.

Although the composition of chlorite group minerals represents a known proxy for conditions in various geological environments, few comparative studies of chlorites from different geological environments have been carried out. In this study, we compare chlorites from a hydrothermal system with those formed under low-grade metamorphic conditions. Both sets of samples were collected from the Pilbara Craton, Western Australia. Near-infrared (NIR) spectroscopy was used to determine if spectral differences record compositional differences between chlorites from the two geological environments. The spectra showed a significant difference in the Mg-OH absorption wavelength, near 2350 nm, with the hydrothermal group showing longer absorption wavelengths than the metamorphic one. A comparison of the spectral data with geochemical analyses showed a relation between the absorption wavelength and the magnesium-to-iron ratio (magnesium number) of chlorite, as well as the bulk rock composition. Metamorphic rocks have a higher magnesium-to-iron ratio than the hydrothermal ones, predominantly explained by differences in the degree of metasomatism. In the hydrothermal system, mass transfer changes the bulk rock composition while for the metamorphic samples the original bulk chemistry determines the current composition of the rock.

Both the mineralogy and geochemistry of coal mine waste presents environmental and social challenges while simultaneously offering the potential source for recovery of metals, including critical raw materials (CRMs). Assessing these challenges and opportunities requires effective waste management strategies and comprehensive material characterization. This study deals with the integration of analytical data obtained from various portable sensor technologies. Infrared reflection spectroscopy (covering a wide wavelength range of 0.4 to 15 µm), and geochemical x-ray fluorescence (XRF) were utilized to differentiate between samples belonging to various geological lithologies and quantify elements of interest. Therefore, we developed a methodological framework that encompasses data integration and machine learning techniques. The model developed using the infrared data predicts the Sr concentration with a model accuracy of R² = 0.77 for the testing dataset; however, the model performances decreased for predicting other elements such as Pb, Zn, Y, and Th. Despite these limitations, the approach demonstrates better performance in discriminating materials based on both mineralogical and geochemical compositions. Overall, the developed methodology, enables rapid and in-situ determination of coal mine waste composition, providing insights into waste composition that are directly linked to potential environmental impact, and the possible recovery of economically valuable metals.