Sorting carbonate clay-type lithium ores using a deep learning model with adaptive spectral-texture feature fusion

Abstract

Carbonate clay-type lithium ore holds significant potential due to its extensive reserves, broad distribution, and relatively easy extraction. However, it presents significant beneficiation challenges due to its coexistence with karst-type bauxite, which often results in mixed, low-lithium ores. For the first time, spectral-texture features derived from hyperspectral imaging (HSI) are jointly modeled with a deep learning framework to explore the feasibility of pre-sorting carbonate clay-type lithium ores. Initially, the spectral responses reflecting mineral composition and the texture features characterizing structural differences were analyzed to evaluate the feasibility of using HSI for ore sorting. Furthermore, the influence of band selection, data standardization, and water absorption regions on pre-sorting performance was systematically investigated through comparative analysis of multiple dataset configurations. Two classification schemes, primary ore types classification and multi grade classification, were employed to assess sorting accuracy and identify key influencing factors. The proposed model, A ² ST-OSNet, achieves excellent results in both ore localization and classification through staged data input with varying dimensions and a modular design. Results revealed that joint modeling of spectral and texture features enables efficient and accurate pre-sorting, whereas models relying solely on either spectral or texture features were insufficient, as discriminative information for ore sorting is jointly determined by mineral composition and structural characteristics. Moreover, refined feature extraction and fusion strategies, including spectral-texture feature selection and attention mechanisms, proved critical in enhancing classification performance. The proposed approach offers valuable technical support for ore beneficiation and tailings reutilization, contributing to sustainable resource utilization and providing an effective solution for the efficient recycling of carbonate clay-type lithium ores.

Keywords

Lithium / Ore sorting / Hyperspectral imaging (HSI) / Deep learning framework / Spectral-spatial classification / Mining circular economy

CRediT authorship contribution statement

Qunjia Zhang: Writing - review & editing, Writing - original draft, Software, Methodology, Funding acquisition, Conceptualization. Jiacheng Mei: Validation. Lujun Lin: Software. Lei Liu: Writing - review & editing, Resources, Data curation. Hanjie Wen: Funding acquisition, Data curation. Le Wang: Software.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported by the National Key Research and Development Program of China (Grant No. 2024YFC2909905); and the Natural Science Basic Research Program of Shaanxi Province (Grant Nos. 2023-JC-ZD-18, 2024SF-YBXM-570).

References

[1] Bai, F., Fan, M., Yang, H., Dong, L., 2021. Fast recognition using convolutional neural network for the coal particle density range based on images captured under multiple light sources. Int. J. Min. Sci. Technol. 31, 1053-1061. https://doi.org/10.1016/j.ijmst.2021.09.004.

[2] Barnewold, L., Lottermoser, B.G., 2020. Identification of digital technologies and digitalisation trends in the mining industry. Int. J. Min. Sci. Technol. 30, 747-757. https://doi.org/10.1016/j.ijmst.2020.07.003.

[3] Benson, T.R., Coble, M.A., Rytuba, J.J., Mahood, G.A., 2017. Lithium enrichment in intracontinental rhyolite magmas leads to Li deposits in caldera basins. Nat. Commun. 8, 270. https://doi.org/10.1038/s41467-017-00234-y.

[4] Bonifazi, G., Capobianco, G., Serranti, S., 2023. Fast and effective classification of plastic waste by pushbroom hyperspectral sensor coupled with hierarchical modelling and variable selection. Resour. Conserv. Recycl. 197, 107068. https://doi.org/10.1016/j.resconrec.2023.107068.

[5] Booysen, R., Lorenz, S., Thiele, S.T., Fuchsloch, W.C., Marais, T., Nex, P.A.M., Gloaguen, R., 2022. Accurate hyperspectral imaging of mineralised outcrops: an example from lithium-bearing pegmatites at Uis, Namibia. Remote Sens. Environ. 269, 112790. https://doi.org/10.1016/j.rse.2021.112790.

[6] Bowell, R.J., Lagos, L., de los Hoyos, C.R., Declercq, J., 2020. Classification and characteristics of natural lithium resources. Elements 16, 259-264.

[7] Brouwer, P., 2006. Theory of XRF. PANalytical BV, Almelo, Netherlands.

[8] Cao, F., Liu, L., Li, L., 2023. Short-wave infrared photodetector. Mater. Today 62, 327-349.

[9] Castor, S.B., Henry, C.D., 2020. Lithium-rich claystone in the McDermitt Caldera, Nevada, USA: Geologic, mineralogical, and geochemical characteristics and possible origin. Minerals 10, 68. https://doi.org/10.3390/min10010068.

[10] Chen, C., Ma, Y., Ren, G., 2020. Hyperspectral classification using deep belief networks based on conjugate gradient update and pixel-centric spectral block features. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 4060-4069. https://doi.org/10.1109/JSTARS.2020.3008825.

[11] Chhapariya, K., Buddhiraju, K.M., Kumar, A., 2024. A deep spectral-spatial residual attention network for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 17, 15393-15406. https://doi.org/10.1109/JSTARS.2024.3355071.

[12] Clark, R.N., Roush, T.L., 1984. Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications. J. Geophys. Res. 89, 6329-6340. https://doi.org/10.1029/JB089iB07p06329.

[13] Clark, R.N., Swayze, G.A., Livo, K.E., Kokaly, R.F., Sutley, S.J., Dalton, J.B., McDougal, R. R., Gent, C.A., 2003. Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems. J. Geophys. Res. 108, 2002JE001847. https://doi.org/10.1029/2002JE001847.

[14] Cui, Y., Hanjie, W., WenXiu, Y., Chongguang, L., Shengjiang, D., KunYue, L., Fei, X., Jihua, Y., 2022. Study on the occurrence state and enrichment mechanism of lithium in lithium-rich clay rock series of the Daoshitou Formation of lower Permian in Central Yunnan. Acta Petrol. Sin. 38, 2080-2094. https://doi.org/10.18654/1000-0569/2022.07.16 (in Chinese with English abstract).

[15] Dai, S., Sun, F., Wang, L., Wang, L., Zhang, R., Guo, H., Xing, Y., Gui, X., 2023. A new method for pre-enrichment of gallium and lithium based on mode of occurrence in coal gangue from Antaibao surface mine, Shanxi Province, China. J. Clean. Prod. 425, 138968. https://doi.org/10.1016/j.jclepro.2023.138968.

[16] Fu, C., Du, B., Zhang, L., 2024. ReSC-net: Hyperspectral image classification based on attention-enhanced residual module and spatial-channel attention. IEEE Trans. Geosci. Remote Sens. 62, 1-15. https://doi.org/10.1109/TGRS.2024.3402364.

[17] Gaft, M., Reisfeld, R., Panczer, G., 2015. Minerals radiometric sorting and online process control. In: Modern Luminescence Spectroscopy of Minerals and Materials. Spring, Cham, pp. 499-575. DOI: 10.1007/978-3-319-24765-6_8

[18] Gallot-Duval, D., Quéré C., De Vito, E., Sirven, J.-B., 2024. Depth profile analysis and high-resolution surface mapping of lithium isotopes in solids using laser-induced breakdown spectroscopy (LIBS). Spectrochimica Acta Part B: Atomic Spectroscopy 215, 106920. https://doi.org/10.1016/j.sab.2024.106920.

[19] Gomez, C., Adeline, K., Bacha, S., Driessen, B., Gorretta, N., Lagacherie, P., Roger, J.M., Briottet, X., 2018. Sensitivity of clay content prediction to spectral configuration of VNIR/SWIR imaging data, from multispectral to hyperspectral scenarios. Remote Sens. Environ. 204, 18-30. https://doi.org/10.1016/j.rse.2017.10.047.

[20] Gu, J., Huang, Z., Fan, H., Jin, Z., Yan, Z., Zhang, J., 2013. Mineralogy, geochemistry, and genesis of lateritic bauxite deposits in the Wuchuan-Zheng’an-Daozhen area, Northern Guizhou Province, China. J. Geochem. Explor. 130, 44-59.

[21] Harmon, R.S., 2024. Laser-induced breakdown spectroscopy in mineral exploration and ore processing. Minerals 14, 731. https://doi.org/10.3390/min14070731.

[22] He, K., Zhang, X., Ren, S., Sun, J., 2016a. Identity Mappings in Deep Residual Networks, in: Leibe B., Matas J., Sebe N., Welling M. (Eds.), Computer, Vision - ECCV 2016, Lecture Notes in Computer Science. Springer International Publishing, Cham, pp. 630-645. DOI: 10.1007/978-3-319-46493-0_38

[23] He, K., Zhang, X., Ren, S., Sun, J., 2016b. In: Deep Residual Learning for Image Recognition. IEEE, Las Vegas, NV, USA, pp. 770-778. https://doi.org/10.1109/CVPR.2016.90.

[24] Imani, M., Ghassemian, H., 2020. An overview on spectral and spatial information fusion for hyperspectral image classification: current trends and challenges. Inf. Fusion 59, 59-83.

[25] IPCC, 2011. Renewable Energy Sources and Climate Change Mitigation. https://www.ipcc.ch/report/renewable-energy-sources-and-climate-change-mitigation/ (accessed 23 July 2024).

[26] Jabari, O., Ayalew, Y., Motshegwa, T., 2021. Semi-Automated X-ray Transmission Image Annotation Using Data-efficient Convolutional Neural Networks and Cooperative Machine Learning,in:Proceedings of the 2021 5th International Conference on Video and Image Processing. ACM, Hayward CA USA, pp. 205-214. https://doi.org/10.1145/3511176.3511207.

[27] Jemwa, G.T., Aldrich, C., 2012. Estimating size fraction categories of coal particles on conveyor belts using image texture modeling methods. Expert Syst. Appl. 39, 7947-7960. https://doi.org/10.1016/j.eswa.2012.01.104.

[28] Kern, M., Tusa, L., Lei ß ner, T., Van Den Boogaart, K.G., Gutzmer, J., 2019. Optimal sensor selection for sensor-based sorting based on automated mineralogy data. J. Clean. Prod. 234, 1144-1152. https://doi.org/10.1016/j.jclepro.2019.06.259.

[29] Kesler, S.E., Gruber, P.W., Medina, P.A., Keoleian, G.A., Everson, M.P., Wallington, T.J., 2012. Global lithium resources: Relative importance of pegmatite, brine and other deposits. Ore Geol. Rev. 48, 55-69. https://doi.org/10.1016/j.oregeorev.2012.05.006.

[30] Kikongi, P., Salvas, J., Gosselin, R., 2017. Curve-fitting regression: improving light element quantification with XRF. X-Ray Spectrom. 46, 347-355.

[31] Kingma, D.P., Ba, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.

[32] Lee, H., Kwon, H., 2017. Going deeper with contextual CNN for hyperspectral image classification. IEEE Trans. Image Process. 26, 4843-4855.

[33] Li, G., Klein, B., Sun, C., Kou, J., 2022. Investigation on influential factors of bulk ore sortability based on fractal modelling. Minerals Eng. 177, 107362. https://doi.org/10.1016/j.mineng.2021.107362.

[34] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P., 2017. Focal loss for dense object detection. In:Proceedings of the IEEE International Conference on Computer Vision, pp. 2980-2988.

[35] Lin, Z., Chen, Y., Zhao, X., Wang, G., 2013. Spectral-spatial classification of hyperspectral image using autoencoders. In: 2013 9th International Conference on Information, Communications & Signal Processing, pp. 1-5. https://doi.org/10.1109/ICICS.2013.6782778.

[36] Ling, K., Wen, H., Han, T., Lu, Z., Cui, Y., Luo, C., Yu, W., 2024. Lithium-rich claystone in Pingguo area, Guangxi, southwest China: precursor kaolinite controls lithium enrichment. Miner. Deposita 59, 329-340. https://doi.org/10.1007/s00126-023-01210-x.

[37] Ling, K., Wen, H., Zhang, Q., Luo, C., Gu, H., Du, S., Yu, W., 2021. Super-enrichment of lithium and niobium in the upper Permian Heshan Formation in Pingguo, Guangxi, China. Sci. China Earth Sci. 64, 753-772.

[38] Ling, K.-Y., Tang, H.-S., Zhang, Z.-W., Wen, H.-J., 2020. Host minerals of Li-Ga-V- rare earth elements in Carboniferous karstic bauxites in southwest China. Ore Geol. Rev. 119, 103325. https://doi.org/10.1016/j.oregeorev.2020.103325.

[39] Ling, K.-Y., Zhu, X.-Q., Tang, H.-S., Du, S.-J., Gu, J., 2018. Geology and geochemistry of the Xiaoshanba bauxite deposit, Central Guizhou Province, SW China: Implications for the behavior of trace and rare earth elements. J. Geochem. Explor. 190, 170-186.

[40] Luo, X., He, K., Zhang, Y., He, P., Zhang, Y., 2022. A review of intelligent ore sorting technology and equipment development. Int. J. Miner. Metall. Mater. 29, 1647-1655. https://doi.org/10.1007/s12613-022-2477-5.

[41] Maier, G., Gruna, R., Längle, T., Beyerer, J., 2024. A survey of the state of the art in sensor-based sorting technology and research. IEEE Access 12, 6473-6493. https://doi.org/10.1109/ACCESS.2024.3350987.

[42] Mehta, P., Populwar, V., Shinde, N., Dhebe, D., More, P., 2025. Laser induced breakdown spectroscopy: a promising analytical technique. Ind. J. Pharm. Edu. Res 59, 30-36. https://doi.org/10.5530/ijper.20254665.

[43] Meshram, P., Pandey, B.D., Mankhand, T.R., 2014. Extraction of lithium from primary and secondary sources by pre-treatment, leaching and separation: a comprehensive review. Hydrometallurgy 150, 192-208. https://doi.org/10.1016/j.hydromet.2014.10.012.

[44] Nie, C., Jiang, J., Deng, J., Li, K., Jia, L., Sun, T., 2023. Predicting TFe content and sorting iron ores from hyperspectral image by variational mode decomposition-based spectral feature. J. Clean. Prod. 429, 139629. https://doi.org/10.1016/j.jclepro.2023.139629.

[45] Nikonow, W., Rammlmair, D., Meima, J.A., Schodlok, M.C., 2019. Advanced mineral characterization and petrographic analysis by l -EDXRF, LIBS, HSI and hyperspectral data merging. Mineral. Petrol. 113, 417-431.

[46] Paoletti, M.E., Haut, J.M., Plaza, J., Plaza, A., 2019. Deep learning classifiers for hyperspectral imaging: a review. ISPRS J. Photogramm. Remote Sens. 158, 279-317. https://doi.org/10.1016/j.isprsjprs.2019.09.006.

[47] Peukert, D., Xu, C., Dowd, P., 2022. A review of sensor-based sorting in mineral processing: the potential benefits of sensor fusion. Minerals 12, 1364. https://doi.org/10.3390/min12111364.

[48] Qi, L., Qin, X., Gao, F., Dong, J., Gao, X., 2023. SAWU-Net: Spatial attention weighted unmixing network for hyperspectral images. IEEE Geosci. Remote Sens. Lett. 20, 1-5. https://doi.org/10.1109/LGRS.2023.3270183.

[49] Ran, Y., Qu, G., Yang, J., Zhou, S., Li, B., Wang, H., Wei, Y., 2024. Efficient separation and extraction of lithium from low-grade claystone by chloride salt-enhanced roasting process. J. Clean. Prod. 434, 140156. https://doi.org/10.1016/j.jclepro.2023.140156.

[50] Ren, S., He, K., Girshick, R., Sun, J., 2017. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137-1149. https://doi.org/10.1109/TPAMI.2016.2577031.

[51] Robben, C., Wotruba, H., 2019. Sensor-based ore sorting technology in mining—past, present and future. Minerals 9, 523. https://doi.org/10.3390/min9090523.

[52] Roy, S.K., Deria, A., Shah, C., Haut, J.M., Du, Q., Plaza, A., 2023. Spectral-spatial morphological attention transformer for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 61, 1-15. https://doi.org/10.1109/TGRS.2023.3242346.

[53] Roy, S.K., Manna, S., Song, T., Bruzzone, L., 2021. Attention-based adaptive spectral-spatial kernel ResNet for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 59, 7831-7843. https://doi.org/10.1109/TGRS.2020.3043267.

[54] Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C., 2018. Mobilenetv2:Inverted residuals and linear bottlenecks. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510-4520.

[55] Schafer, R.W., 2011. What is a Savitzky-Golay filter?[lecture notes]. IEEE Signal Process. Mag. 28, 111-117.

[56] Shatwell, D.G., Murray, V., Barton, A., 2023. Real-time ore sorting using color and texture analysis. Int. J. Min. Sci. Technol. 33, 659-674. https://doi.org/10.1016/j.ijmst.2023.03.004.

[57] Shu, Z., Liu, Z., Zhou, J., Tang, S., Yu, Z., Wu, X.-J., 2023. Spatial-spectral split attention residual network for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 16, 419-430. https://doi.org/10.1109/JSTARS.2022.3225928.

[58] Signoroni, A., Savardi, M., Baronio, A., Benini, S., 2019. Deep learning meets hyperspectral image analysis: a multidisciplinary review. J. Imaging 5, 52. https://doi.org/10.3390/jimaging5050052.

[59] Sun, G., Huang, D., Peng, Y., Cheng, L., Wu, B., Zhang, Y., 2024. Efficient segmentation with texture in ore images based on box-supervised approach. Eng. Appl. Artif. Intell. 128, 107490. https://doi.org/10.1016/j.engappai.2023.107490.

[60] TOMRA, 2023. Sensor-based sorting in lithium mining operations unlocking untapped potential. https://www.tomra.com/en/mining/media-center/news/2023/sensor-based-sorting-in-lithium-mining-operations-unlocking-untapped-potential (accessed 23 July 2024).

[61] Wang, C., Liu, B., Liu, L., Zhu, Y., Hou, J., Liu, P., Li, X., 2021. Short-wave infrared photodetector. Artif. Intell. Rev. 54, 5205-5253. https://doi.org/10.1007/s10462-021-10018-y.

[62] Wang, D., Li, P., Qu, W., Yin, L., Zhao, Z., Lei, Z., Wen, S., 2013. Discovery and preliminary study of the high tungsten and lithium contents in the Dazhuyuan bauxite deposit, Guizhou, China. Sci. China Earth Sci. 56, 145-152.

[63] Wang, S., He, L., Guo, Y., Hu, K., Li, D., Zhao, Y., Ma, X., 2024. Dual-energy X-ray transmission identification method of multi-thickness coal and gangue based on SVM distance transformation. Fuel 356, 129593.

[64] Wen, H., Luo, C., Du, S., Yu, W., Gu, H., Ling, K., Cui, Y., Li, Y., Yang, J., 2020. Carbonate-hosted clay-type lithium deposit and its prospecting significance. Chin. Sci. Bull. 65, 53-59. https://doi.org/10.1360/TB-2019-0179.

[65] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S., 2018. Cbam: Convolutional block attention module. In:Proceedings of the European Conference on Computer Vision (ECCV), pp. 3-19.

[66] Wu, Z., 2024. Advance review on occurrence state and leaching of lithium in sedimentary bauxite (aluminum) deposits, China. Ore Geol. Rev. 164, 105851. https://doi.org/10.1016/j.oregeorev.2023.105851.

[67] Yang, K., Sun, H., Zou, C., Lu, X., 2022. Cross-attention spectral-spatial network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 60, 1-14. https://doi.org/10.1109/TGRS.2021.3133582.

[68] Yang, X., Ye, Y., Li, X., Lau, R.Y.K., Zhang, X., Huang, X., 2018. Hyperspectral image classification with deep learning models. IEEE Trans. Geosci. Remote Sens. 56, 5408-5423. https://doi.org/10.1109/TGRS.2018.2815613.

[69] Yang, Z., Xu, M., Liu, S., Sheng, H., Zheng, H., 2023. Spatial-spectral attention bilateral network for hyperspectral unmixing. IEEE Geosci. Remote Sens. Lett. 20, 1-5. https://doi.org/10.1109/LGRS.2023.3295437.

[70] Yu, C., Han, R., Song, M., Liu, C., Chang, C.-I., 2022. Feedback attention-based dense CNN for hyperspectral image classification. IEEE Tran. Geosci. Remote Sens. 60, 1-16. https://doi.org/10.1109/TGRS.2021.3058549.

[71] Yu, J., Sun, H., An, Y., Gao, P., Zhang, X., Han, Y., 2023. Energy conservation and consumption reduction in grinding operations through ceramic media stirring mill: an industrial validation test. Powder Technol. 429, 118943.

[72] Zhang, J.-Y., Wang, Q., Liu, X.-F., Zhou, G.-F., Xu, H.-P., Zhu, Y.-G., 2022. Provenance and ore-forming process of Permian lithium-rich bauxite in central Yunnan, SW China. Ore Geol. Rev. 145, 104862. https://doi.org/10.1016/j.oregeorev.2022.104862.

[73] Zhang, X., Wang, Z., 2023. Spatial proximity feature selection with residual spatial-spectral attention network for hyperspectral image classification. IEEE Access 11, 23268-23281. https://doi.org/10.1109/ACCESS.2023.3253627.

[74] Zhang, Y., Cheng, L., Peng, Y., Xu, C., Fu, Y., Wu, B., Sun, G., 2023. Faster OreFSDet: a lightweight and effective few-shot object detector for ore images. Pattern Recognit. 141, 109664. https://doi.org/10.1016/j.patcog.2023.109664.

[75] Zhang, Y., Hu, Y., Wang, L., Sun, W., 2019. Systematic review of lithium extraction from salt-lake brines via precipitation approaches. Minerals Eng. 139, 105868. https://doi.org/10.1016/j.mineng.2019.105868.

[76] Zhao, W., Du, S., 2016. Spectral-spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach. IEEE Trans. Geosci. Remote Sens. 54, 4544-4554. https://doi.org/10.1109/TGRS.2016.2543748.

[77] Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D., 2020. Distance-IoU loss: Faster and better learning for bounding box regression. In:Proceedings of the AAAI Conference on Artificial Intelligence, pp. 12993-13000.

[78] Zhong, H., Sun, Y., Yang, Y., Wang, D., Huang, F., Zhao, Z., 2019. Bauxite (aluminum)-type lithium resources and analysis of its development and utilization potential. Mineral Deposits 38, 898-916 (in Chinese with English abstract).

[79] Zhong, Z., Li, J., Luo, Z., Chapman, M., 2018. Spectral-spatial residual network for hyperspectral image classification: a 3-D deep learning framework. IEEE Trans. Geosci. Remote Sens. 56, 847-858. https://doi.org/10.1109/TGRS.2017.2755542.

[80] Zhu, L., Gu, H., Wen, H., Yang, Y., 2021a. Lithium extraction from clay-type lithium resource using ferric sulfate solutions via an ion-exchange leaching process. Hydrometallurgy 206, 105759. https://doi.org/10.1016/j.hydromet.2021.105759.

[81] Zhu, M., Jiao, L., Liu, F., Yang, S., Wang, J., 2021b. Residual spectral-spatial attention network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 59, 449-462. https://doi.org/10.1109/TGRS.2020.2994057.