Electronic spectra of fluorides, oxyfluorides, and oxides of rare-earth metals

Бацанов, С. С.; Derbeneva, S. S.; Batsanova, L. R.

doi:10.1007/bf00615368

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…The electronic spectrum of REEs is composed of a series of clear lines that are formed by 4f electron transitions in ultraviolet, visible, and infrared ranges. In addition, the spectral characteristics of studied REEs obviously move to the long-wave range according to the order of fluoride, oxyfluoride, and oxide [35]. Figures 3-5 show the spectra of fluoride, oxy-fluoride, and oxide and indicate the frequency of maximum absorption peaks.…”

Section: Spectral Response For the Contents Of Reesmentioning

confidence: 99%

Inversion Evaluation of Rare Earth Elements in Soil by Visible-Shortwave Infrared Spectroscopy

Huang

et al. 2021

Remote Sensing

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According to historical information, more than 300 metal smelting enterprises have been in the southwest of Xiongan for 300 years; however, these polluting enterprises have been gradually closed with the increased intensity of environmental protection. In the paper, 264 soil samples were collected and analyzed in the range of 400 nm–2500 nm by the spectra vista corporation (SVC), and the spectral noise was smoothed by the Savitzky–Golay filter. In order to enhance the spectral differences and curve shapes, mathematical transformations, such as the standard normal variate (SNV), first-order differential (FD), second-order differential (SD), multiple scattering correction (MSC), and continuum removal (CR), were performed on the data, and the correlation between spectral transformation and contents of REEs was analyzed. Moreover, three machine learning models—partial least-squares (PLS), random forest (RF), back propagation neural network (BPNN)—were used to predict the contents of REEs. Experimental results prove that REEs are combined with spectral active substances, such as organic compounds, clay minerals, and iron oxide, and it is possible to determine the contents of REEs using the reflection spectrum. The R2 between the predicted values and measured contents reached 0.986 by using BPNN after FD transformation. More importantly, the predicted values basically agree with the actual situation for CASI/SASI airborne hyperspectral images, and this is an effective technique to obtain the contents of REEs in soil at the study area.

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Section: Spectral Response For the Contents Of Reesmentioning

confidence: 99%

Inversion Evaluation of Rare Earth Elements in Soil by Visible-Shortwave Infrared Spectroscopy

Huang

et al. 2021

Remote Sensing

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show abstract

“…The spectral absorption characteristics of rare earth elements mainly depend on the F-F electronic transition of REE 3+ . As early as more than 50 years ago, scholars have studied the visible-near-infrared reflection spectral characteristics of rare earth minerals (Adams, 1965;Hunt, 1977), and some other scholars have studied the spectral characteristics of rare earth minerals since then (Batsanov et al, 1969;Dai, 2013;Turner et al, 2014;Boesche et al, 2015). With the development of remote sensing technology, domestic and foreign scholars have applied remote sensing technology to the exploration and research of rare earth minerals, and achieved certain results (Zhang et al, 2012;Zhao et al, 2014;Boesche et al, 2015;Cheng et al, 2019b).…”

Section: Introductionmentioning

confidence: 99%

Research on quantitative inversion of ion adsorption type rare earth ore based on convolutional neural network

et al. 2023

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Rare earth resource is a national strategic resource, which plays an essential role in the field of high technology research and development. In this paper, we aim to use remote sensing quantitative inversion prospecting technology, use surface-to-surface mode, and model inversion and evaluation through convolutional neural network model to achieve a new research method for large-scale, low-cost, rapid and efficient exploration of ion-adsorbed rare earth ore. The results show that the RE2O3 content of samples has significant negative correlation with the second, third and fourth band of GF-2 image, but has no significant correlation with the first band of GF-2 image; the convolution neural network model can be used to reconstruct the RE2O3 content. The content distribution map of RE2O3 obtained by inversion is similar to that of geochemical map, which indicates that the convolution neural network model can be used to invert the RE2O3 content in the sampling area. The quantitative inversion results show that the content distribution characteristics of ion adsorption rare earth ore in the study area are basically consistent with the actual situation; there are two main high anomaly areas in the study area. The high anomaly area I is a known mining area, and the high anomaly area II can be a prospective area of ion adsorption type rare earth deposit. It shows that the remote sensing quantitative inversion prospecting method of ion adsorption type rare earth deposit based on Convolutional Neural Networks (CNN) model is feasible.

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Recent advances in multivariate analysis coupled with chemical analysis for soil surveys: a review

Milinović

Vale²,

Azenha³

2022

J Soils Sediments

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Electronic spectra of fluorides, oxyfluorides, and oxides of rare-earth metals

Cited by 6 publications

References 4 publications

Inversion Evaluation of Rare Earth Elements in Soil by Visible-Shortwave Infrared Spectroscopy

Inversion Evaluation of Rare Earth Elements in Soil by Visible-Shortwave Infrared Spectroscopy

Research on quantitative inversion of ion adsorption type rare earth ore based on convolutional neural network

Recent advances in multivariate analysis coupled with chemical analysis for soil surveys: a review

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