Automated Mineralogical Analysis of <250 µm Heavy Minerals in Till: Method Development and Case Study

• Main Author: Lougheed, Donald
• Other Authors: Layton-Matthews, Daniel, McClenaghan, M. Beth, Leybourne, Matthew, Geological Sciences and Geological Engineering
• Format: Thesis
• Language: English
• Published: 2022
• Subjects: Geology; Mineral exploration; Applied geochemistry; Drift exploration; Indicator minerals; Heavy mineral concentrates; Automated mineralogy; Canada; Nunavut
• Online Access: http://hdl.handle.net/1974/30011

Exploration under glacial sediment cover is a necessary part of modern mineral exploration in Canada. Traditional indicator methods use visual examination to identify mineral grains in the 250 to 2000 µm fraction of till heavy-mineral concentrates (HMC). This study tests automated mineralogical methods using scanning electron microscopy to identify indicator minerals in the fine (<250 µm) HMC fraction of till. Automated mineralogy of polished grains from the fine HMC enables rapid data collection (10,000–300,000 grains/sample). Samples collected near three deposits were used to test this method: 4 from the upper amphibolite Izok Lake volcanogenic massive sulfide deposit, Nunavut, 5 from the Sisson granite-hosted W-Mo deposit, New Brunswick, and 4 from the Triple B kimberlite, Ontario. The less than 250 µm HMC fraction of till samples collected down-ice of each deposit contain ore and alteration minerals typical of their deposit type. Sulfide minerals occur mainly as inclusions in oxidation-resistant minerals, including minerals previously identified in the metamorphic alteration halo of each deposit, and are found to occur farther down-ice than the grains identified visually in the greater than 250 µm HMC fraction. Minerals that are traditionally difficult to identify using visual identification are easily discriminated by composition. The classic suite of kimberlite indicator suite is fully identified, and energy dispersive X-ray spectroscopy enables discrimination of compositional sub-populations without the use of more expensive/time consuming targeted chemical analysis techniques. This project developed a workflow for analyzing <250 µm HMC with automated mineralogy that is reproducible and relatable both within and between studies. The method described expands the detectable footprint for certain indicator minerals, identifies new indicator minerals, and enhances the information on the mineralogical composition of till samples that can be collected from till samples. PhD