铅锌矿石稀散元素镉含量的高光谱反演研究

doi:10.3964/j.issn.1000-0593(2023)04-1275-07

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Abstract Rare-dispersed element mineral resources are the key mineral resource in the national economy. The information extraction of content is the basis for potential evaluation and target optimization of mineral resources, but the existing analysis technology of rare-dispersed elements cannot meet the needs of rapid detection and potential evaluation. The analysis technology of rare-dispersed element based on Hyperspectral is a way to solve this problem. Therefore, the Sinongduo-Zexue ore concentration area in Tibet is the study area, and the hyperspectral inversion method and inversion model about the content of rare-dispersed element cadmium (Cd) in lead zinc ore are studied. ASD FieldSpec 3 spectrometer and supporting software are used for spectral data acquisition and pretreatment. Based on spectral feature analysis, multi-type spectral transformations such as first derivative (FD), second derivative (SD), logarithm of the reciprocal (AT), first derivative of logarithm of the reciprocal (AFD), second derivative of logarithm of the reciprocal (ASD) are carried out, the characteristic bands selected by Pearson correlation coefficient (r) are used for the modeling and inversion of random forest (RF), artificial neural network (ANN), support vector machine (SVM), the effect and prediction accuracy of content inversion models are evaluated by the coefficient of determination (R²), and root mean square error (RMSE). The results show that the spectral reflectance of the sample is concentrated in the range of 40%~60%, and the absorption peaks are formed at 1 420, 1 920 and 2 200 nm. The characteristic bands cover the visible and near-infrared bands, and 771~2 051 nm is the optimal range of the characteristic band. SD is the best spectral data dimensionality reduction method, which has selected 15 characteristic bands. ASD has selected 8 characteristic bands, and AFD has selected 2 characteristic bands. FD and AT did not select the characteristic band. In the characteristic band inversion of SD selection, the SD-ANN model (R²=0.884, RMSE=2.679) has the best prediction effect of cadmium content, followed by the SD-SVM model (R²=0.830>0.8, RMSE=1.382), SD-RF model has the worst prediction effect (R²=0.505<0.6). In the characteristic band inversion of ASD selection, the best prediction of cadmium content is the ASD-SVM model (R²=0.857, RMSE=2.198), followed by the ASD-ANN model (R²=0.846, RMSE=2.625). The hyperspectral inversion effect of cadmium (Cd) content is: SD-ANN>ASD-SVM>ASD-ANN>SD-SVM>ASD-RF>SD-RF. The study summarizes the hyperspectral characteristics of cadmium, establishes the hyperspectral inversion method and model of cadmium content, provides a reference for hyperspectral inversion, nondestructive testing and rapid analysis of rare-dispersed elements such as cadmium, and provides theoretical support for the potential evaluation and target optimization of rare-dispersed element mineral resources in the high-altitude exploration area.

Key words：Rare-dispersed element; Cadmium content analysis; Hyperspectral inversion; Visible and near infrared spectroscopy; Lead zinc ore

Received: 2022-02-18 Accepted: 2022-07-05

ZTFLH:

TP79

Corresponding Authors: LIU Yan-song E-mail: liuyansong2012@cdut.edu.cn

Cite this article:

LAI Si-han,LIU Yan-song,LI Cheng-lin, et al. Study on Hyperspectral Inversion of Rare-Dispersed Element Cadmium Content in Lead-Zinc Ores[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1275-1281.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2023)04-1275-07 OR https://www.gpxygpfx.com/EN/Y2023/V43/I04/1275

[1] TAO Yan, HU Rui-zhong, TANG Yong-yong, et al(陶琰, 胡瑞忠, 唐永永, 等). Acta Geologica Sinica(地质学报), 2019, 93(6): 1210.
[2] MAO Jing-wen, YUAN Shun-da, XIE Gui-qing, et al(毛景文, 袁顺达, 谢桂青, 等). Mineral Deposits(矿床地质), 2019, 38(5): 935.
[3] WEN Han-jie, ZHU Chuan-wei, DU Sheng-jiang, et al(温汉捷, 朱传威, 杜胜江, 等). Chinese Science Bulletin(科学通报), 2020, 65(33): 3688.
[4] LI Kai-xuan, LENG Cheng-biao, REN Zhi, et al(李凯旋, 冷成彪, 任志, 等). Acta Mineralogica Sinica(矿物学报), 2021, 41(3): 225.
[5] LI Yun-gang, ZHU Chuan-wei(李云刚, 朱传威). Acta Mineralogica Sinica(矿物学报), 2020, 40(6): 765.
[6] LIU Chang, YANG Zhu-sen, XU Pei-yan, et al(刘畅, 杨竹森, 徐培言, 等). Geoscience(现代地质), 2021, 35(2): 466.
[7] QU Wen-jun, WANG Deng-hong, ZHU Yun, et al(屈文俊, 王登红, 朱云, 等). Acta Geologica Sinica(地质学报), 2019, 93(6): 1514.
[8] HU Yu-si, YE Lin, HUANG Zhi-long, et al(胡宇思, 叶霖, 黄智龙, 等). Acta Petrologica Sinica(岩石学报), 2019, 35(11): 3477.
[9] CAO Fa-sheng, LIU Yan-song, HE Zheng-wei, et al(曹发生, 刘严松, 何政伟, 等). Chinese Journal of Analytical Chemistry(分析化学), 2021, 49(2): 292.
[10] MAO Ya-chun, WEN Jian, FU Yan-hua, et al(毛亚纯, 温健, 付艳华, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2022, 42(1): 68.
[11] Lyu Pengyi, He Li, He Zhengwei, et al. Ore Geology Reviews, 2021, 138: 104359.
[12] CHI Qing-hua(迟清华). Handbook of Applied Geochemical Element Abundance Data(应用地球化学元素丰度数据手册). Beijing: Geological Publishing Press(北京: 地质出版社), 2007. 39.
[13] SHI Chang-yi, LIANG Meng, FENG Bin(史长义, 梁萌, 冯斌). Earth Science(地球科学), 2016, 41(2): 234.
[14] LI Mao-gang, LIANG Jing, YAN Chun-hua, et al(李茂刚, 梁晶, 闫春华, 等). Chinese Journal of Analytical Chemistry(分析化学), 2021, 49(8): 1410.
[15] HU Yang, LI Zi-han, LÜ Tao(胡杨, 李子涵, 吕涛). Laser & Optoelectronics Progress(激光与光电子学进展), 2017, 54(5): 345.