Current Techniques and Applications of Mineral Chemistry to Mineral Exploration; Examples from Glaciated Terrain: A Review

This paper provides a summary of traditional, current, and developing exploration techniques using indicator minerals derived from glacial sediments, with a focus on Canadian case studies. The 0.25 to 2.0 mm fraction of heavy mineral concentrates (HMC) from surficial sediments is typically used for indicator mineral surveys, with the finer (0.25–0.50 mm) fraction used as the default grain size for heavy mineral concentrate studies due to the ease of concentration and separation and subsequent mineralogical identification. Similarly, commonly used indicator minerals (e.g., Kimberlite Indicator Minerals—KIMs) are well known because of ease of optical identification and their ability to survive glacial transport. Herein, we review the last 15 years of the rapidly growing application of Automated Mineralogy (e.g., MLA, QEMSCAN, TIMA, etc) to indicator mineral studies of several ore deposit types, including Ni-Cu-PGE, Volcanogenic Massive Sulfides, and a variety of porphyry systems and glacial sediments down ice of these deposits. These studies have expanded the indicator mineral species that can be applied to mineral exploration and decreased the size of the grains examined down to ~10 microns. Chemical and isotopic fertility indexes developed for bedrock can now be applied to indicator mineral grains in glacial sediments and these methods will influence the next generation of indicator mineral studies.

Universal image segmentation for optical identification of 2D materials

Machine learning methods are changing the way data is analyzed. One of the most powerful and widespread applications of these techniques is in image segmentation wherein disparate objects of a digital image are partitioned and classified. Here we present an image segmentation program incorporating a series of unsupervised clustering algorithms for the automatic thickness identification of two-dimensional materials from digital optical microscopy images. The program identifies mono- and few-layer flakes of a variety of materials on both opaque and transparent substrates with a pixel accuracy of roughly 95%. Contrasting with previous attempts, application generality is achieved through preservation and analysis of all three digital color channels and Gaussian mixture model fits to arbitrarily shaped data clusters. Our results provide a facile implementation of data clustering for the universal, automatic identification of two-dimensional materials exfoliated onto any substrate.