USGS2022
Over the past ~30 years, hyperspectral remote sensing of chemical variations in white mica have proven to be useful for ore deposit studies in a range of deposit types. To better understand mineral deposits and to guide spectrometer design, this contribution reviews relevant papers from the fields of remote sensing, spectroscopy, and geology that have utilized spectral changes caused by chemical variation in white micas. This contribution reviews spectral studies conducted at the following types of mineral deposits: base metal sulfide, epithermal, porphyry, sedimentary rock hosted gold deposits, orogenic gold, iron oxide copper gold, and unconformity-related uranium. The structure, chemical composition, and spectral features of white micas, in this contribution defined as muscovite, paragonite, celadonite, phengite, illite, and sericite, are given. Reviewed laboratory spectral studies determined that shifts in the position of the white mica 2200 nm combination feature of 1 nm correspond to a change in Al oct content of approximately ±1.05%. Many of the reviewed spectral studies indicated that a shift in the position of the white mica 2200 nm combination feature of 1 nm was geologically significant. A sensitivity analysis of spectrometer characteristics; bandpass, sampling interval, and channel position, is conducted using spectra of 19 white micas with deep absorption features to determine minimum characteristics required to accurately measure a shift in the position of the white mica 2200 nm combination feature. It was determined that a sampling interval < 16.3 nm and bandpass <17.5 nm are needed to achieve a root mean square error (RMSE) of 2 nm, whereas a sampling interval < 8.8 nm and bandpass <9.8 nm are needed to achieve a RMSE of 1 nm. For comparison, commonly used imaging spectrometers HyMap, AVIRIS-Classic, SpecTIR®'s AisaFENIX 1K, and HySpex tm SWIR 384 have 2.1, 1.2, 0.96, and 0.95 nm RMSE in determining the position of the 2200 nm white mica combination feature, respectively. An additional sensitivity analysis is conducted to determine the effect of signal to noise ratio (SNR) on the RMSE of the position of the white mica 2200 nm combination feature, using spectra of 18 white micas with deep absorption features. For a spectrometer with sampling interval and bandpass of 1 nm, we estimate that RMSEs of 1 and 1.5 nm are achievable with spectra having a minimum SNR of approximately 246 and 64, respectively. For a spectrometer with sampling interval and bandpass of 5 nm, we estimate that RMSEs of 1 and 1.5 nm are attainable with spectra having a minimum SNR of approximately 431 and 84, respectively. When using a spectrometer with a sampling interval 8.8 nm and a bandpass of 9.8 nm, a RMSE of 1 is only achievable with convolved, noiseless reference spectra. For the 8.8_9.8 nm spectrometer, spectra with SNR of 250 and 100 result in RMSE of 1.1 and 1.3, respectively. Therefore, fine spectral resolution characteristics achieve RMSEs better than 1 nm for high SNR spectra while spectrometers with coarse spectral resolution have larger RMSE, perform well with noisy data, and are useful for white mica studies if RMSE of 1.1 to 1.5 nm is acceptable.
Remote Sensing of Environment↗