Spectral Reflectance Characteristics of Kimberlite Indicator Minerals and Other Selected Mineral Grains

High chromium content in kimberlite indicator minerals such as pyrope garnet and diopside is often correlated with the presence of diamonds. In this study, kimberlite indicator minerals were examined using visible light reflectance spectroscopy to determine if chromium content can be correlated with spectral absorption features. The depth of absorption features in the visible spectral region were correlated with the molecular percentage of chromium and other first series transition metal elements obtained by electron microprobe data. In the visible part of the spectrum, chromium is evident by 3 absorption features in the pyrope reflectance spectrum; one isolated and narrow feature at the wavelength 689 nm was used to correlate with the chromium mol %. The isolation of this feature in the pyrope spectra is advantageous since it is not directly affected by other proximal absorption bands that could be caused by other transition metals. Analysis of the feature indicates that as grain volume increases the depth of the absorption feature will also increase. Clustering grain volumes into fractions yields better correlation between absorption depth and mol % chromium. Other types of garnet (almandine, grossular, spessartine) and kimberlite indicator minerals (olivine, diopside, chromite, ilmenite) were analyzed to determine if other absorption features could be used to predict the proportion of specific transition metal elements. Diopside in particular illustrates the same isolated chromium absorption feature as pyrope and may indicate mol percent but needs further study with larger sample sets.

Hyperspectral Mineral Identification using SVM and SOM

Remote sensing techniques involving hyperspectral imagery have applications in a number of sciences that study some aspects of the surface of the planet. The analysis of hyperspectral images is complex because of the large amount of information involved and the noise within that data. Investigating images with regard to identify minerals, rocks, vegetation and other materials is an application of hyperspectral remote sensing in the earth sciences. This thesis evaluates the performance of two classification and clustering techniques on hyperspectral images for mineral identification. Support Vector Machines (SVM) and Self-Organizing Maps (SOM) are applied as classification and clustering techniques, respectively. Principal Component Analysis (PCA) is used to prepare the data to be analyzed. The purpose of using PCA is to reduce the amount of data that needs to be processed by identifying the most important components within the data. A well-studied dataset from Cuprite, Nevada and a dataset of more complex data from Baffin Island were used to assess the performance of these techniques. The main goal of this research study is to evaluate the advantage of training a classifier based on a small amount of data compared to an unsupervised method. Determining the effect of feature extraction on the accuracy of the clustering and classification method is another goal of this research. This thesis concludes that using PCA increases the learning accuracy, and especially so in classification. SVM classifies Cuprite data with a high precision and the SOM challenges SVM on datasets with high level of noise (like Baffin Island).