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White mica: spectrometer design, district-scale mapping, and integration of imaging spectrometer and lidar data
Meyer, John M.
Meyer, John M.
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2024
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Meyer_mines_0052E_316/S2_white_mica_samples.pdf
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Meyer_mines_0052E_316/S2_convolved_spectra.pdf
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2025-11-26
Abstract
Imaging spectroscopy, which applies the principles of reflectance spectroscopy to an image on a pixel-by-pixel basis, has been utilized for decades in a wide variety of fields, providing information regarding the chemical composition of materials measured. This information has allowed researchers to measure subtle differences in materials using ground-based, airborne, and orbital sensors. Combining imaging spectroscopy data with 3-D point cloud data produces a data set containing information on the composition and quantity of materials in an imaged area of interest. This dissertation applies imaging spectroscopy to the study of white mica in the context of mineral exploration and mining. The dissertation comprises three parts: 1) spectrometer design, 2) district-scale mapping, and 3) integration of imaging spectrometer and lidar data.
White micas are common products of hydrothermal alteration associated with mineral deposits. A review of spectral studies conducted at a variety of mineral deposits determined that shifts in the position of the diagnostic 2.2 µm combination feature in white micas of as little as 1 nm can be geologically significant. A sensitivity study was conducted to determine spectrometer characteristics: sampling interval, bandpass, channel center, and signal to noise ratio, that are required to measure the position of the 2.2 µm combination feature with a root mean square error (RMSE) of 1 and 2 nm. It was determined that RMSE was more sensitive to bandpass than sampling interval variation, while channel center position had little effect. Adding Gaussian noise to the test data degraded the RMSE in all spectrometers, but it was determined that fine spectrometers were more sensitive to noise than coarse spectrometers. Results obtained in this contribution can guide the user in the selection of a spectrometer required to achieve a specific RMSE when measuring the position of the white mica 2.2 µm combination feature.
Many spectral studies have been conducted that leverage the position of the white mica 2.2 µm combination feature to infer characteristics of a variety mineral deposit types. In this contribution, we conduct a study in the Battle Mountain mining district in Nevada using the width of the white mica 2.2 µm combination feature to differentiate white mica that formed as the result of magmatic-hydrothermal activity from white mica that is igneous in origin and is a constituent of siliciclastic rocks in the district. Airborne imaging spectroscopy data were processed to generate spectral-based mineral maps. These were compared to aeromagnetic anomaly maps, geologic maps, and deposit-scale studies to ascertain the relationship of white mica chemistry and white mica grain-size with proximity to buried batholiths and associated magmatic-hydrothermal activity. We determined that fine-grained, high-Al content white micas, which we interpreted to be sericite formed as the result of magmatic-hydrothermal activity, were spatially associated with batholiths buried at depth. We also determined that coarse-grained, low-Al white micas, which we interpreted to be igneous in origin and constituents of siliciclastic rocks in the district, were found more distal to buried batholiths and thus not formed as the result of associated magmatic-hydrothermal activity.
To extract information regarding the quantity of materials measured using imaging spectrometers, the 2-D raster data generated may be fused with data containing 3-D geospatial information. Imaging spectrometer collected in Yellowstone National Park were back projected on to point clouds generated with terrestrial laser scanner lidar data using open-source software. The resultant 3-D data cloud was used to interpret an exposed river scarp and to place that scarp in the context of the greater Yellowstone hydrothermal system. Data generated using techniques to fuse and analyze hyperclouds are useful in a variety of mining applications including mapping of outcrops and highwalls and mapping of underground workings. Worker safety is enhanced due to the ability to standoff from dangerous highwalls and unsupported ground while still collecting valuable data regarding the composition and quantity of materials in an area of interest. This contribution can form the basis for future work such as development of real-time, heads-up displays for shovel operators that would provide actionable information on ore sorting and waste identification. This contribution presents the detailed workflows to collect, process, and fuse USGS spectral-based material maps generated from imaging spectroscopy data with point spectrometer, lidar, and geospatial data.
Imaging spectrometers, through direct observation of minerals or the use of spectral analogues, have the potential to be utilized in a wide range of mining applications beyond mineral exploration, such as digital mapping of production drill core, ore grade determination, stockpile management, processing plant feed monitoring, closure, and remediation activities. Fusing 2-D imaging spectrometer data with 3-D point cloud data generates a fused data set containing qualitative and quantitative information of a region of interest. These fused data sets can allow workers to generate 3-D maps of working faces that depict the quantity of various grades of ore, and the presence and quantity of gangue materials. Fused data sets can be used to characterize, and inventory abandoned mine sites, assessing their potential use as a resource and aiding in their remediation.
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