FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4225712758> ?p ?o ?g. }

• W4225712758 endingPage "113000" @default.
• W4225712758 startingPage "113000" @default.
• W4225712758 abstract "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 contrib ution 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 Aloct 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 HySpextm 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." @default.
• W4225712758 created "2022-05-05" @default.
• W4225712758 creator A5015630268 @default.
• W4225712758 creator A5029715898 @default.
• W4225712758 creator A5059111916 @default.
• W4225712758 date "2022-06-01" @default.
• W4225712758 modified "2023-10-02" @default.
• W4225712758 title "Hyperspectral remote sensing of white mica: A review of imaging and point-based spectrometer studies for mineral resources, with spectrometer design considerations" @default.
• W4225712758 cites W168217325 @default.
• W4225712758 cites W1890075632 @default.
• W4225712758 cites W1964549775 @default.
• W4225712758 cites W1983564289 @default.
• W4225712758 cites W1986530877 @default.
• W4225712758 cites W1988014764 @default.
• W4225712758 cites W1992444449 @default.
• W4225712758 cites W1998818470 @default.
• W4225712758 cites W1999726370 @default.
• W4225712758 cites W2008825280 @default.
• W4225712758 cites W2010797000 @default.
• W4225712758 cites W2012749606 @default.
• W4225712758 cites W2018194343 @default.
• W4225712758 cites W2019038438 @default.
• W4225712758 cites W2022470997 @default.
• W4225712758 cites W2048459112 @default.
• W4225712758 cites W2059579037 @default.
• W4225712758 cites W2067087418 @default.
• W4225712758 cites W2071584308 @default.
• W4225712758 cites W2075901285 @default.
• W4225712758 cites W2076430294 @default.
• W4225712758 cites W2082645751 @default.
• W4225712758 cites W2083639974 @default.
• W4225712758 cites W2083808432 @default.
• W4225712758 cites W2087407047 @default.
• W4225712758 cites W2089752283 @default.
• W4225712758 cites W2099662321 @default.
• W4225712758 cites W2106085599 @default.
• W4225712758 cites W2113062784 @default.
• W4225712758 cites W2122452972 @default.
• W4225712758 cites W2128686953 @default.
• W4225712758 cites W2136518035 @default.
• W4225712758 cites W2137209740 @default.
• W4225712758 cites W2139577861 @default.
• W4225712758 cites W2150182117 @default.
• W4225712758 cites W2153889834 @default.
• W4225712758 cites W2161669723 @default.
• W4225712758 cites W2165394332 @default.
• W4225712758 cites W2330747182 @default.
• W4225712758 cites W2396451144 @default.
• W4225712758 cites W2460188791 @default.
• W4225712758 cites W2527344479 @default.
• W4225712758 cites W2568355616 @default.
• W4225712758 cites W2599803952 @default.
• W4225712758 cites W2748968520 @default.
• W4225712758 cites W2792216949 @default.
• W4225712758 cites W2895942203 @default.
• W4225712758 cites W2945071234 @default.
• W4225712758 cites W2960366821 @default.
• W4225712758 cites W3004157881 @default.
• W4225712758 doi "https://doi.org/10.1016/j.rse.2022.113000" @default.
• W4225712758 hasPublicationYear "2022" @default.
• W4225712758 type Work @default.
• W4225712758 citedByCount "6" @default.
• W4225712758 countsByYear W42257127582022 @default.
• W4225712758 countsByYear W42257127582023 @default.
• W4225712758 crossrefType "journal-article" @default.
• W4225712758 hasAuthorship W4225712758A5015630268 @default.
• W4225712758 hasAuthorship W4225712758A5029715898 @default.
• W4225712758 hasAuthorship W4225712758A5059111916 @default.
• W4225712758 hasBestOaLocation W42257127581 @default.
• W4225712758 hasConcept C120665830 @default.
• W4225712758 hasConcept C121332964 @default.
• W4225712758 hasConcept C127313418 @default.
• W4225712758 hasConcept C151730666 @default.
• W4225712758 hasConcept C159078339 @default.
• W4225712758 hasConcept C183852935 @default.
• W4225712758 hasConcept C191897082 @default.
• W4225712758 hasConcept C192562407 @default.
• W4225712758 hasConcept C199289684 @default.
• W4225712758 hasConcept C2776432453 @default.
• W4225712758 hasConcept C2776581184 @default.
• W4225712758 hasConcept C2778583526 @default.
• W4225712758 hasConcept C2779870107 @default.
• W4225712758 hasConcept C2781038479 @default.
• W4225712758 hasConcept C33390570 @default.
• W4225712758 hasConcept C5457282 @default.
• W4225712758 hasConcept C62649853 @default.
• W4225712758 hasConceptScore W4225712758C120665830 @default.
• W4225712758 hasConceptScore W4225712758C121332964 @default.
• W4225712758 hasConceptScore W4225712758C127313418 @default.
• W4225712758 hasConceptScore W4225712758C151730666 @default.
• W4225712758 hasConceptScore W4225712758C159078339 @default.
• W4225712758 hasConceptScore W4225712758C183852935 @default.
• W4225712758 hasConceptScore W4225712758C191897082 @default.
• W4225712758 hasConceptScore W4225712758C192562407 @default.
• W4225712758 hasConceptScore W4225712758C199289684 @default.
• W4225712758 hasConceptScore W4225712758C2776432453 @default.
• W4225712758 hasConceptScore W4225712758C2776581184 @default.
• W4225712758 hasConceptScore W4225712758C2778583526 @default.

• next