|
|
|
|
|
|
Rapid Estimation of Cobalt Content in Lateritic Cobalt Ores:
a Quantitative Inversion Study of VNIR-SWIR Spectra |
MEI Jia-cheng1, 2, WANG Xue1, 2, ZHANG Hong-rui3*, LIU Lei1, 2* |
1. School of Earth Science and Resources, Chang'an University, Xi'an 710054, China
2. New Energy Minerals and Resources Information Engineering Technology Innovation Center of the Ministry of Natural Resources, Xi'an 710054, China
3. State Key Laboratory of Deep Earth and Mineral Exploration, Institute of Geology, Chinese Academy of Geological Sciences, Beijing 100037, China
|
|
|
Abstract Cobalt is a global strategic mineral resource. Laterite-type cobalt deposits are large and shallow, and are important targets for cobalt exploration. The traditional cobalt exploration process often relies on indoor testing and analysis to determine the level of cobalt content in field outcrops and the degree of its mineralization. The cobalt elemental content testing method often involves a complex sample preparation process. It relies on large-scale high-precision instrumentation, which makes it difficult to meet the demand for rapid testing and exploration. Visible-near infrared and short-wave infrared (VNIR-SWIR) spectroscopy offers the advantages of portability, high efficiency, and non-destructiveness to the samples. It demonstrates excellent applicability to field sample testing scenarios. The measured spectral analysis of the samples shows that 620~810 nm reflects the absorption of cobalt ions and iron ions, 810~1 200 nm reflects the absorption characteristics of Fe2+, 1 350~1 450 and 1 850~2 040 nm reflect the absorption characteristics of —OH and H2O, and 2 140~2 260 and 2 260~2 360 nm reflect the absorption characteristics of Al—OH and Mg—OH, respectively; accordingly, the sensitive wavelength range and the characteristic absorption characteristics of the samples are selected. Accordingly, the range of sensitive bands, the characteristic absorption peak parameters and the ratio of sensitive bands were selected as the spectral combination parameters, and the XGBoost (Extreme Gradient Boosting) regression algorithm was applied to establish the cobalt content quantitative inversion model; based on which, parameter optimization was carried out to obtain the optimal cobalt content quantitative analysis model, and the validation set had the values of R2 0.95, the RMSE was 89.19, the RPD was 4.35, and the model inversion accuracy was high. The histogram of feature importance shows that the sensitive band of cobalt element ranges from 620~810 nm, and the accuracy of the model is significantly improved after increasing the weights of absorption features of minerals closely related to cobalt content (chlorite, serpentine). The above results-demonstrate that the cobalt content of lateritic cobalt ore samples can be accurately estimated based on VNIR-SWIR spectra. The model, which incorporates combined spectral parameters, has the capability of rapidly determining cobalt content in field outcrops, offering significant application value for lateritic cobalt ore exploration.
|
Received: 2025-01-13
Accepted: 2025-04-25
|
|
Corresponding Authors:
ZHANG Hong-rui, LIU Lei
E-mail: 13581620980@126.com; liul@chd.edu.cn
|
|
[1] Savinova E, Evans C, Lèbre , et al. Resources, Conservation and Recycling, 2023, 190: 106855.
[2] Dehaine Q, Tijsseling L T, Glass H J, et al. Minerals Engineering, 2021, 160: 106656.
[3] WANG Hui, FENG Cheng-you, ZHANG Ming-yu(王 辉, 丰成友, 张明玉). Mineral Deposits(矿床地质), 2019, 38(4): 739.
[4] WANG Jing, SHI Xiang-jiang, WANG Shou-cheng, et al(王 京, 石香江, 王寿成, 等). Natural Resource Economies of China(中国国土资源经济), 2019, 32(10): 28.
[5] PutzoluF, Balassone G, Boni M, et al. Ore Geology Reviews, 2018, 97: 21.
[6] Teitler Y, Cathelineau M, Ulrich M, et al. Journal of Geochemical Exploration, 2019, 196: 131.
[7] GAO Ya, DENG Jiang-hong, YANG Xiao-yong, et al(高 雅, 邓江红, 杨晓勇, 等). Geological Review(地质论评), 2022, 68(5): 1839.
[8] FU Wei, NIU Hu-jie, HUANG Xiao-rong, et al(付 伟, 牛虎杰, 黄小荣, 等). Acta Geologica Sinica(地质学报), 2013, 87(6): 832.
[9] Butt C R M, Cluzel D. Elements, 2013, 9(2): 123.
[10] Dalm M, Buxton M W N, Van Ruitenbeek F J A. Minerals Engineering, 2017, 105: 10.
[11] Clark R N, Swayze G A, Livo K E, et al. Journal of Geophysical Research: Planets, 2003, 108(E12): 5131.
[12] Clark R N, Roush T L. Journal of Geophysical Research: Solid Earth, 1984, 89(B7): 6329.
[13] Corrêa Da Costa M A, Carneiro Naleto J L, Perrotta M M. Economic Geology, 2024, 119(5): 1171.
[14] DAI Jing-jing,WANG Deng-hong,LING Tian-yu, et al(代晶晶, 王登红, 令天宇, 等). Remote Sensing Technology and Application(遥感技术与应用), 2019, 34(5): 992.
[15] Yin F, Wu M, Liu L, et al. International Journal of Applied Earth Observation and Geoinformation, 2021, 102: 102420.
[16] Prado E M G, de Souza Filho C R, Carranza E J M. Economic Geology, 2023, 118(8), 1899.
[17] Chen T, Guestrin C. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, 785.
[18] Li Y, Liu Y, Lin H, et al. Scientific Reports, 2023, 13(1): 18061.
[19] Zhang S, Chu G, Cheng J, et al. Ore Geology Reviews, 2020, 122: 103516.
[20] Deng K, Zhao H, Li N, et al. Remote Sensing Letters, 2021, 12(5): 449.
[21] D'Ippolito V, Andreozzi G B, Hlenius U, et al. Physics and Chemistry of Minerals, 2015, 42: 431. |
[1] |
LI Wen-wen, YAN Fang*, LIU Yang-shuo. A Mechanistic Analysis of Terahertz Absorption Peak Formation in
Benzoic Acid and Sorbic Acid Mixtures[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(07): 1848-1856. |
[2] |
TAN Fang-ping1, LU Tong-suo1, 2*. Study of Near-Infrared Fingerprints of Ganoderma Lucidum in Different Growth Environments[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(07): 1968-1978. |
[3] |
HE Shuai, ZHOU Jie, ZHANG Fu-lin, MU Guo-qing*. Moisture Content Online Detection in Fluidized Bed Drying Process Based on Near Infrared Spectroscopy and XGBoost[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(12): 3347-3352. |
[4] |
XU Xiao-dong, ZHANG Hui-min, LIU Jia-le, HAN Lu-jia, YANG Zeng-ling, LIU Xian*. Study on Infrared Spectral Recognition of Microplastics in Fishmeal Based on XGBoost Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1835-1842. |
[5] |
ZHANG Wen-jing1, 2, XUE He-ru1, 2*, JIANG Xin-hua1, 2, LIU Jiang-ping1, 2, HUANG Qing1. An Improved XGBoosting Algorithm Based on Fat Content in Infant Milk Powder Prediction Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1464-1471. |
[6] |
XIAO Bin1, 2, HE Hong-chang1, DOU Shi-qing1*, FAN Dong-lin1, FU Bo-lin1, ZHANG Jie1, XIONG Yuan-kang1, SHI Jin-ke1. A Fine Classification Method of Citrus Fruit Trees Based on UAV
Hyperspectral Images and SULOV_XGBoost Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 548-557. |
[7] |
WANG Lin, WANG Xiang*, ZHOU Chao, WANG Xin-xin, MENG Qing-hui, CHEN Yan-long. Remote Sensing Quantitative Retrieval of Chlorophyll a and Trophic Level Index in Main Seagoing Rivers of Lianyungang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3314-3320. |
[8] |
ZHANG Mei-zhi1, ZHANG Ning1, 2, QIAO Cong1, XU Huang-rong2, GAO Bo2, MENG Qing-yang2, YU Wei-xing2*. High-Efficient and Accurate Testing of Egg Freshness Based on
IPLS-XGBoost Algorithm and VIS-NIR Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1711-1718. |
[9] |
WU Xue1, 2, FENG Wei-wei2, 3, 4*, CAI Zong-qi2, 3, WANG Qing2, 3. Study on Rapid Recognition of Microplastics Based on Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3501-3506. |
[10] |
CAO Yu-qi2, KANG Xu-sheng1, 2*, CHEN Piao-yun2, XIE Chen2, YU Jie2*, HUANG Ping-jie2, HOU Di-bo2, ZHANG Guang-xin2. Research on Discrimination Method of Absorption Peak in Terahertz
Regime[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3058-3062. |
[11] |
YANG Xin1, 2, YUAN Zi-ran1, 2, YE Yin1, 2*, WANG Dao-zhong1, 2, HUA Ke-ke1, 2, GUO Zhi-bin1, 2. Winter Wheat Total Nitrogen Content Estimation Based on UAV
Hyperspectral Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3269-3274. |
[12] |
LI Rui1, LI Bo1*, WANG Xue-wen1, LIU Tao1, LI Lian-jie1,2, FAN Shu-xiang2. A Classification Method of Coal and Gangue Based on XGBoost and
Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2947-2955. |
[13] |
WU Ye-lan1, GUAN Hui-ning1, LIAN Xiao-qin1, YU Chong-chong1, LIAO Yu2, GAO Chao1. Study on Detection Method of Leaves With Various Citrus Pests and
Diseases by Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2397-2402. |
[14] |
WANG Ming-xuan, WANG Qiao-yun*, PIAN Fei-fei, SHAN Peng, LI Zhi-gang, MA Zhen-he. Quantitative Analysis of Diabetic Blood Raman Spectroscopy Based on XGBoost[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1721-1727. |
[15] |
PENG Wu-di1, NING Jia-lian2, CHEN Zhi-li1*, TANG Jin1, LIU Li-xi1, CHEN Lin1. Construction of CS2 Combustion Flame Spectral Radiation Model and Inversion of Characteristic Pollution Product Concentration[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 672-677. |
|
|
|
|