|
|
|
|
|
|
| Study on the Hyperspectral (V-NIR-SWIR) Characteristics and
Applications for Typical Rocks and Ores in the Dalucao
Carbonatite-Related REE Deposit, Western Sichuan, China |
| GUO Dong-xu, SHI Wei-xin*, GAO Qing-nan, HUI Guang-ji, ZHANG Hong, REN Xiao-sa, YUAN Chun-yu, ZHAO Long-xian, LIU Jun-yuan |
Cores and Samples Center of Natural Resources, Langfang 065201，China
|
|
|
|
|
Abstract Hyperspectral technology has great appeal due to its environmental-friendly, rapid, and non-destructive determination for material composition and improvement of exploration efficiency in recent years. The qualitative identification of rare earth elements (REE) and their compounds, rare earth minerals, and the quantitative inversion for REE have been systematically studied through hyperspectral technology . However, the hyperspectral quantitative inversion study of rare earth concentrations for carbonatite-related REE deposits is relatively limited. This paper will collect the spectral reflectance data of representative well rocks and ores in a carbonatite-related REE deposit named Dalucao using HyLogger through hyperspectral (V-NIR-SWIR) technology. Meanwhile, the mineral assemblages of rocks and ores were observed through an orthogonal polarizing microscope, and the contents of rare earth elements were measured through the whole rock trace experiment. Then, the characteristics of hyperspectral reflectance for different types of rocks and ores were summarized systematically, and the quantitative inversion for REE was set up through regression analysis in the Dalucao REE deposit. The following conclusions can be obtained. The typical absorptions (in 511, 523, 580, 676, 740, 798, 865, 890, 1 093, and 1 252 nm), as well as the parameters of relative absorption depths (Dep511≥0.002, Dep523≥0.002, Dep580≥0.002, Dep676≥0.002, Dep740≥0.004, Dep798≥0.006, Dep865≥0.004, Dep890≥0.002, Dep1093≥0.003, Dep1252≥0.002) etc. for ores are the significant information of mineralization for exploration. These relative absorption depths have strong correlations with the concentrations of REE. The multiple linear regression inversion models have been established between these relative absorption depths and the contents of 15 kinds of REE and total REE, including Y, respectively, along with high accuracy with R2(0.759~0.987), which demonstrates that they have good predictions for the contents of REE. The results suggest that it has a wide prospect for rapid, non-destructive, cost-efficient determination of REE in carbonatite-related REE -deposits based on the visible, near-infrared, and short-wave infrared (V-NIR-SWIR) spectroscopy technology. Meanwhile, it shows a great possibility for the deep area, margin area, regional deposit exploration, limitation of the orebody, and mineral resource forecast in this type of deposit.
|
|
Received: 2024-02-07
Accepted: 2024-07-15
|
|
|
|
Corresponding Authors:
SHI Wei-xin
E-mail: shiweixincugb@163.com
|
|
[1] WANG Deng-hong, ZHAO Zhi, YU Yang, et al(王登红, 赵 芝, 于 扬, 等). Rock and Mineral Analysis(岩矿测试), 2013, 32(5): 796.
[2] FAN Hong-rui, NIU He-cai, LI Xiao-chun, et al(范宏瑞, 牛贺才, 李晓春, 等). Chinese Science Bulletin(科学通报), 2020, 65(33): 3778.
[3] Song W, Xu C, Smith M P, et al. Geology, 2018, 46: 323.
[4] Liu Y, Hou Z, Zhang R, et al. Economic Geology，2019，114(4): 719.
[5] Zheng X, Liu Y, Smith M P, et al. Journal of Petrology, 2023, 64(6): 037.
[6] WANG Deng-hong, WANG Rui-jiang, LI Jian-kang, et al(王登红, 王瑞江, 李建康, 等). Geology in China(中国地质), 2013, 40(2): 361.
[7] Weng Z, Jowitt S M, Mudd G M, et al. Economic Geology, 2015, 110: 1925.
[8] Zhu X, Liu Y, Hou Z. Geology, 2024, 52(2): 105.
[9] SONG Wen-lei, XU Cheng, WANG Lin-jun, et al(宋文磊, 许 成, 王林均, 等). Acta Scientiarum Naturalium Universitatis Pekinensis(北京大学学报：自然科学版), 2013, 49(4): 725.
[10] 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.
[11] Cheng G, Zhang H, Li H, et al. Journal of Earth Science, 2023, 34(4): 1068.
[12] van der Meer F D, van der Werff H M A, van Ruitenbeek F J A, et al. International Journal of Applied Earth Observation and Geoinformation, 2012, 14: 112.
[13] DAI Jing-jing, ZHAO Long-xian, JIANG Qi, et al(代晶晶, 赵龙贤, 姜 琪, 等). Acta Geologica Sinica(地质学报), 2020, 94(8): 2520.
[14] Binnemans K. Chemical Reviews, 2007, 107: 2592.
[15] Ardanova L I, Get'man E I, Loboda S N, et al. Inorganic Chemistry, 2010, 49: 10687.
[16] DAI Jing-jing, WANG Deng-hong, CHEN Zheng-hui, et al(代晶晶, 王登红, 陈郑辉, 等). Acta Geoscientica Sinica(地球学报), 2017, 38(4): 523.
[17] CHENG Gong, LI Jia-xuan, WANG Chao-peng, et al(成 功, 李嘉璇, 王朝鹏, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(5): 1571.
[18] Batsanov S S, Derbeneva S S, Batsanova L R. Journal of Applied Spectroscopy, 1969, 10(2): 240.
[19] Dai J, Wang D, Wang R, et al. Journal of Applied Remote Sensing, 2013, 7: 073513.
[20] Möller V, Williams-Jones A E. Journal of Geochemical Exploration, 2018，188: 194.
[21] 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.
[22] Dai J, Wang D, Chen Z. Journal of Rare Earth, 2021，39: 1300.
[23] Tan W, Qin X, Liu J, et al. Applied Clay Sciences, 2021, 215: 106320.
[24] Tan W, Qin X R, Liu J C, et al. Economic Geology, 2022, 117(2): 495.
[25] Kopačková-Strnadová V, Rapprich V, McLemore V, et al. International Journal of Applied Earth Observations and Geoinformation, 2021, 103: 102423.
[26] LIU Yan, CHEN Chao, SHU Xiao-chao, et al(刘 琰, 陈 超, 舒小超, 等). Acta Petrologica Sinica(岩石学报), 2017, 33(7): 1978.
[27] Turner D J, Rivard B, Groat L A. American Mineralogist. 2014, 99: 1335.
[28] Laukamp C, Rodger A, LeGras M，et al. Minerals, 2021, 11(4): 347.
[29] QIN Xiao-rong, YAO Yu-zeng, HE Hong-ping, et al(秦效荣, 姚玉增, 何宏平, 等). Geochimica(地球化学), 2020, 49(4): 422.
|
| [1] |
SHEN Guan-ting1, RAO Ke-yi1, FANG Rui-xin1, ZHANG Xue-min1, WU Zhao-cong1, 2*. A Method for Classifying Stains and Defects on Mobile Phone Cover Glass Based on Hyperspectral Line Scan Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(03): 616-622. |
| [2] |
Suyala Qiqige1*, ZHANG Zhen-xin2, LI Zhuo-ling1, FAN Ming-shou2, JIA Li-guo2, ZHAO Jin-hua1. Quantitative Monitoring Models of Potato Leaf Water Content in Tuber Formation Period Based on Hyperspectral Characteristic Parameters[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(03): 774-783. |
| [3] |
GONG Xue-liang, LI Yu*, JIA Shu-han, ZHAO Quan-hua, WANG Li-ying. Tensor-Based Dictionary Learning Sparse Representation Classification for Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(03): 798-807. |
| [4] |
ZHAO Xin1, 4, SHI Yu-na1, LIU Yi-tong1, JIANG Hong-zhe2, CHU Xuan3, ZHAO Zhi-lei1, 4, WANG Bao-jun1, 4*, CHEN Han1. Key Feature Analysis in Identification and Authenticity of Ziziphi Spinosae Semen by Using Hyperspectral Images Based on 1DCNN and PLSDA[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(03): 869-877. |
| [5] |
ZHANG Fu1, WANG Meng-yao1, YAN Bao-ping1, ZHANG Fang-yuan1, YUAN Ye1, ZHANG Ya-kun1, FU San-ling2*. Hyperspectral Imaging Combined With ELM for Eggs Variety
Identification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(03): 836-841. |
| [6] |
ZHANG Yu-qing1, 2, ZHAO Qi-chao1, 2*, LIU Qi-yue1, 2, FANG Hong-ji1, 3, HAN Wen-long1, 4, CHEN Wen-yue1, 2. Inversion Model of Hyperspectral Water Quality Parameters Based on FOD and Optimal Spectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(03): 842-851. |
| [7] |
FANG Ao, YIN Yong*, YU Hui-chun, YUAN Yun-xia. Hyperspectral Method for Tracing the Origin of Hawthorn Using the Weighted Combination Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(02): 584-590. |
| [8] |
LI Wei-qi1, WANG Yi-fan1, YU Yue1, LIU Jie1, 2, 3*. Establishment and Optimization of the Hyperspectral Detection Model for Soluble Solids Content in Fortunella Margarita[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(02): 492-500. |
| [9] |
WANG Ming1, 2, HE Hong-juan1, 2, WANG Bao-rui1, 2*, AI Yu-hua1, 2, WANG Yue1, 2. A Generalized Combustion Characteristic Prediction Model Based on Flame Hyperspectral Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(02): 532-541. |
| [10] |
ZHANG Ran1, 2, JIN Wei1, 2, MU Ying1, YU Bing-wen2, BAI Yi-wen2, SHAO Yi-bo1, 2, PING Jin-liang3*, SONG Peng-tao3, HE Xiang-yi3, LIU Fei3, FU Lin-lin3. Transformer-Based Method for Segmentation of Gastric Cancer
Microscopic Hyperspectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(02): 551-557. |
| [11] |
WU Chang-yu1, DAI Jing-jing1, 2*, SONG Yang1, 2, CHEN Wei1, 2, LIU Zhi-bo1, LIU Hong-cheng3, BAI Long-yang1. Study on Core Hyperspectral Imaging and Its Significance in Exploration: A Case Study of Bangpu Large Polymetallic Deposit in Tibet[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(01): 38-44. |
| [12] |
JU Lei1, YU Jie1, WU Yan-miao2, LI Li2, LU Tian3, DING Ya-ping2, SHU Ru-xin1*. Comparative Study of Hyperspectral Preprocessing Methods and Multiple Models in Classification and Discrimination[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(01): 125-132. |
| [13] |
WU Meng-hong1, 2, DOU Sen1, LIN Nan2, JIANG Ran-zhe3, CHEN Si2, LI Jia-xuan2, FU Jia-wei2, MEI Xian-jun2. Hyperspectral Estimation of Soil Organic Matter Based on FOD-sCARS and Machine Learning Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(01): 204-212. |
| [14] |
TANG Bin1, HE Yu-long1, TANG Huan2*, LONG Zou-rong1, WANG Jian-xu1*, TAN Bo-wen2, QIN Dan2, LUO Xi-ling1, ZHAO Ming-fu1. Attention Mechanism Based Hyperspectral Image Dimensionality Reduction for Mold Spot Recognition in Paper Artifacts[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(01): 246-255. |
| [15] |
ZHANG Chao1, 2, 3, WU Xuan1, 3, YANG Ke-ming4*, QI Fan-yu1, 3, XIA Tian5. Exploration of Spectral Characteristics of Crop Leaves Under Cu2+ Pollution[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(01): 264-271. |
|
|
|
|
