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| The Inversion of Muscovite Content Based on Spectral Absorption
Characteristics of Rocks |
| ZHAO Jian-ming, YANG Chang-bao, HAN Li-guo*, ZHU Meng-yao |
College of Geoexploration Science and Technology，Jilin University，Changchun 130026，China
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Abstract Rock is composed of various minerals, and there is a close relationship between the reflectance spectral absorption characteristics and mineral content. The spectral absorption characteristics of mineral spectra at specific bands are one of the important indicators for the quantitative estimation of content. This paper takes muscovite as the research object, analysing the rock spectrum’s spectral absorption characteristics near 2.2 μm and muscovite content. Moreover, uses Savitzky-Golay smoothing filter and Continuum Removal method to process the spectral reflectance of rock, and then extracts the spectral absorption characteristic parameters (absorption depth D, absorption width W, absorption area A ), and analyzes the correlation between the absorption characteristics of rock spectrum near 2.2 μm and muscovite content. In this paper, the statistical model was established by a single absorption feature, and the Partial Least Squares (PLS) and Multilayer Perceptron (MLP) models were established by multi-dimensional absorption feature. The muscovite content and spectral absorption characteristic parameters in rocks were analyzed, and a non-linear representation method for predicting mineral content in rocks was proposed. The results show that the spectral absorption characteristics of rock spectrum near 2.2 μm, the correlation between absorption depth and muscovite content among the highest. In the statistical model based on single absorption characteristics, the quadratic curve model has the best fitting effect on the absorption depth. R2 is 0.935 0, RMSE is 0.063 0. The absorption depth of the rock spectrum changes with the abundance of muscovite. The higher the muscovite content in rock, the greater the value of rock spectral absorption depth. The PLS model based on multidimensional spectral absorption characteristics was more effective than the MLP model. The R2 was 0.947 7 higher than 0.901 2 for MLP, and the RMSE was 0.002 7 lower than 0.005 1 for MLP. On the whole, the multidimensional model is better than the single-dimension model, and the PLS model has the best inversion ability. The model has the characteristics of a small amount of calculation and high precision in predicting muscovite content. Analyzing the spectral absorption characteristics of rocks at the diagnostic characteristics provides a theoretical reference for the quantitative inversion of the content of mineral components. It provides a fast, efficient, and convenient method for the monitoring and evaluating mineral resources.
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Received: 2021-11-17
Accepted: 2022-03-28
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Corresponding Authors:
HAN Li-guo
E-mail: 68854058@qq.com
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[1] Notesco G, Ogen Y, Ben-Dor E. Remote Sensing, 2016, 8: 318.
[2] Deng K, Zhao H, Li N, et al. Remote Sensing Letters, 2021, 12(5): 449.
[3] Fox N, Parbhakar-Fox A, Moltzen J, et al. Minerals Engineering, 2017, 107: 63.
[4] Kumar H, Rajawat A S. International Journalof Applied Earth Observation and Geoinformation, 2020, 88: 102084.
[5] Kopačková Veronika, Koucká Lucie. Remote Sensing, 2017, 9(10): 1006.
[6] Christian M, Christian R, Nina B, et al. Remote Sensing, 2016, 8(2): 127.
[7] Jing T, Yu Z C, Jmca B, et al. Ore Geology Reviews, 2019, 106: 134.
[8] Ruitenbeek F JAV, Bakker W H, Werff H, et al. Planetary & Space Science, 2014, 101(10): 108.
[9] Hecker C, Ruitenbeek F, Werff H, et al. IEEE Geoscience and Remote Sensing Magazine, 2019, 7(2): 51.
[10] Radim, Va?át, Radka, et al. Geoderma, 2017, 298: 46.
[11] Sebai, Abdelaziz, Mezned, et al. Journal of Applied Remote Sensing, 2017, 11(1): 016021.
[12] Zhang M, Zhang Q, Wang L, et al. Remote Sensing Technology and Application, 2019, 34(2): 313.
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