Skip to main content

Advertisement

SpringerLink
Log in

Genetic Algorithm Optimized Light Gradient Boosting Machine for 3D Mineral Prospectivity Modeling of Cu Polymetallic Skarn-Type Mineralization, Xuancheng Area, Anhui Province, Eastern China

  • Original Paper
  • Published:
Natural Resources Research Aims and scope Submit manuscript

Abstract

While geological data gathering expands and advances along with mineral exploration, there is still a need for more innovation and enrichment because there are few complete analytical tools for these data. The light gradient boosting machine (LightGBM) technique is used in this study to estimate the prospectivity for minerals in three dimensions. However, because of the LightGBM model's large number of hyper-parameters, it is difficult to manually configure and alter model hyper-parameters, which has a substantial impact on the trained model's accuracy and dependability. Therefore, to optimize the LightGBM hyper-parameters, we use genetic algorithm (GA), which has the resilience and global optimization searchability in addressing difficult optimization problems. The GA–LightGBM algorithm for 3D mineral prospectivity modeling is the name we give to this combined approach. This study compares the GA–LightGBM algorithm against the GA-optimized support vector machine (SVM) and random forest (RF) algorithms in order to assess its applicability and superiority. The training set accuracy, test set accuracy, and Kappa coefficient values for the GA–LightGBM algorithm model were 0.9763, 0.9651, and 0.9453, respectively. The GA–LightGBM model's receiver operating characteristic curve was quite close to the upper left corner of the graph. The findings show that in terms of application and predictability, the GA–LightGBM ensemble learning approach performs better than the GA-optimized SVM and GA-optimized RF models. The outcomes of this study offer fresh perspectives for 3D mineral prospectivity modeling.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16

Similar content being viewed by others

Data Availability

The datasets generated during the current study are not publicly available due to a confidentiality agreement.

References

  • Abdulalim, A., Lqbal, M., Zahid, M., Khan, K., Nasir, A., & Jalal, F. (2022). Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis. Construction and Building Materials, 345, 128296.

    Article  Google Scholar 

  • Agterberg, F., Bonham-Carter, G. F., Cheng, Q., & Wright, D. F. (1993). Weights of evidence modeling and weighted logistic regression for mineral potential mapping. Computers in geology, 25, 13–32.

    Google Scholar 

  • Anhui Provincial Bureau of Geology and Mineral Resources. (1987). Regional Geology of Anhui Province. Geological Publishing House. In Chinese.

    Google Scholar 

  • Breslow, N., & Cain, K. (1988). Logistic regression for two-stage case-control data. Biometrika, 75(1), 11–20.

    Article  Google Scholar 

  • Cao, J., & Liu, X. (2021). Optimization of Hopfield Neural network for military training effect evaluation based on quantum Genetic Algorithm. Ordnance automation, 40(10), 56–60. In Chinese with English abstract.

    Google Scholar 

  • Chang, Y., Liu, X., & Wu, Y. (1991). The Copper-Iron Belt of the Lower and Middle reaches of the Changjiang River. Geological Publishing House.

    Google Scholar 

  • Chen, J., Mao, X., Liu, Z., & Deng, H. (2020). Three-dimensional metallogenic prediction based on random forest classification algorithm for the dayingezhuang gold deposit. Geotectonica et Metallogenia, 44(2), 231–241. In Chinese with English abstract.

    Google Scholar 

  • Chen, Y. (2015). Mineral potential mapping with a restricted Boltzmann machine. Ore Geology Reviews, 71, 749–760.

    Article  Google Scholar 

  • Chen, Y., & Wu, W. (2017). Application of one-class support vector machine to quickly identify multivariate anomalies from geochemical exploration data. Geochemistry-Exploration Environment Analysis, 17, 231–238.

    Article  Google Scholar 

  • Dasgupta, K., Mandal, B., Dutta, P., Mondal, J., & Dam, S. (2013). A genetic algorithm (ga) based load balancing strategy for cloud computing. Procedia Technology, 10, 340–347.

    Article  Google Scholar 

  • Deng, H., Zheng, Y., Chen, J., Yu, S., Xiao, K., & Mao, X. (2022). Learning 3D mineral prospectivity from 3D geological models using convolutional neural networks: Application to a structure-controlled hydrothermal gold deposit. Computers & Geosciences, 161, 105074.

    Article  Google Scholar 

  • Fabbri, A., & Chung, C. (2008). On blind tests and spatial prediction models. Natural Resources Research, 17, 107–118.

    Article  Google Scholar 

  • Hong, D., Huang, Z., Chan, S., & Wang, X. (2017). Geological characteristics and prospecting direction of copper polymetallic deposit in Majushan-Soba Mountain area, Xuancheng, Anhui Province. East China Geology, 38(1), 28–36. In Chinese with English abstract.

    Google Scholar 

  • Hou, M. (2005). Late Yanshanian Magmatic Activity and Deep Process in Jiangnan Uplift Belt (Anhui part). HeFei University of Technology. In Chinese with English abstract.

    Google Scholar 

  • Hu, X. (2020). Numerical Modeling of Ore-Forming Processes and Three Dimensional Prospectivity Modeling of Nanling-Xuancheng Mining District. HeFei University of Technology. In Chinese with English abstract.

    Google Scholar 

  • Hu, X., Li, X., Yuan, F., Ord, A., Jowitt, S. M., Li, Y., Dai, W., & Zhou, T. (2020). Numerical modeling of ore-forming processes within the Chating Cu-Au porphyry-type deposit, China: Implications for the longevity of hydrothermal systems and potential uses in mineral exploration. Ore Geology Reviews, 116(C), 103230.

    Article  Google Scholar 

  • Jia, R., Lv, Y., Wang, G., Carranza, E., Chen, Y., Wei, C., & Zhang, Z. (2021). A stacking methodology of machine learning for 3D geological modeling with geological-geophysical datasets, Laochang Sn camp, Gejiu (China). Computers & Geosciences, 151, 104754.

    Article  Google Scholar 

  • Jiang, F., Xu, X. C., Qian, S. L., Wang, M., Yang, Q. G., & Li, K. (2017). Zircon U-Pb age and genesis of the ore-bearing quartz-dioritic porphyries in the Chating Cu–Au ore deposit, Xuancheng city, Anhui province. Geological Journal of China Universities, 23(4), 591–605. in Chinese with English abstract.

    Google Scholar 

  • Ke, G., Meng, Q., Finley, T., Wang, T., Chen W., Ma, W., Ye, Q., & Liu, T. (2017). Light GBM: A Highly Efficient Gradient Boosting Decision Tree. In: 31st Conference on Neural Information Processing Systems (NIPS 2017).

  • Kim, J., Lee, H., & Oh, J. (2020). Study on prediction of ship’s power using light GBM and XGBoost. Journal of the Korean Society of Marine Engineering, 44(2), 174–180.

    Google Scholar 

  • Krawczyk, B., Minku, L., Gama, J., Stefanowski, J., & Wozniak, M. (2017). Ensemble learning for data stream analysis: A survey. Information Fusion, 37, 132–156.

    Article  Google Scholar 

  • Li, X.H., (2015). 3D prospectivity modeling for concealed orebody and system development. Doctor’s Dissertation of Hefei University of Technology (In Chinese with English abstract).

  • Li, H., Li, X., Yuan, F., Jowitt, S. M., Zhang, M., Zhou, J., Zhou, T., Li, X., Ge, C., & Wu, B. (2020a). Convolutional neural network and transfer learning based mineral prospectivity modeling for geochemical exploration of Au mineralization within the Guandian-Zhangbaling area, Anhui Province, China. Applied Geochemistry, 122, 104747.

    Article  Google Scholar 

  • Li, X., Yuan, F., Zhang, M., Jia, C., Jowitt, S. M., Ord, A., Zheng, T., Hu, X., & Li, Y. (2015). Three-dimensional mineral prospectivity modeling for targeting of concealed mineralization within the Zhonggu iron orefield, Ningwu Basin, China. Ore Geology Reviews, 71, 633–654.

    Article  Google Scholar 

  • Li, X., Yuan, F., Zhang, M., Jowitt, S. M., Ord, A., Zhou, T., & Dai, W. (2019a). 3D computational simulation-based mineral prospectivity modeling for exploration for concealed Fe–Cu skarn-type mineralization within the Yueshan orefield, Anqing district, Anhui Province, China. Ore Geology Reviews, 105, 1–17.

    Article  Google Scholar 

  • Li, Y. (2022). Mineralization of Skarn-type Cu-polymetallic deposits of the Xuancheng ore district. HeFei University of Technology. In Chinese with English abstract.

    Google Scholar 

  • Li, Y., Yuan, F., Deng, Y., Li, X., Liu, G., Li, H., Lu, S., & Zhou, Y. (2019b). Origin and metallogenic significance of the ore-forming intrusion and itsmafic microgranular enclaves in the Qiaomaishan deposit, Xuancheng City, Anhui Province: Geochronology, geochemistry, Sr-Nd-Hf-O isotopic constraints. Acta Petrologica Sinica, 35(12), 3838–3862. In Chinese with English abstract.

    Article  Google Scholar 

  • Li, Y., Yuan, F., Jowitt, S., Deng, Y., Hu, X., Liu, G., Li, X., & Zhou, T. (2020b). Geochronology, petrogenesis and metallogenic implications of mineralization-related intrusive rocks in the Xuancheng ore district, Eastern China. Ore Geology Reviews, 125, 103690.

    Article  Google Scholar 

  • Li, Y., Yuan, F., Jowitt, S., Li, X., Deng, Y., Li, X., & Zhou, T. (2021a). Garnet major and trace element evidence of the alteration and mineralizing processes associated with genesis of the Qiaomaishan skarn deposit, Xuancheng ore district, eastern China. Ore Geology Reviews, 137, 104304.

    Article  Google Scholar 

  • Li, Y., Yuan, F., Jowitt, S. M., Wang, F., Li, X., Deng, Y., Wang, Y., & Zhou, T. (2021b). Molybdenite Re–Os, titanite and garnet U-Pb dating of the Magushan skarn Cu–Mo deposit, Xuancheng district, Middle-Lower Yangtze River Metallogenic Belt. Geoscience Frontiers, 12(3), 101116.

    Article  Google Scholar 

  • Liu, X., & Duan, L. (2015). Geological characteristics and metallogenic regularity of Tongshan-Buckwheat Cu–S–W–Fe polymetallic deposit in Xuancheng. Geology of Anhui, 25(3), 174–178. In Chinese with English abstract.

    Google Scholar 

  • Liu, Y., Zhu, L., & Zhou, Y. (2018). Application of Convolutional Neural Network in prospecting prediction of ore deposits: Taking the Zhaojikou Pb-Zn ore deposit in Anhui Province as a case. Acta Petrologica Sinica, 34(11), 3217–3224. In Chinese with English abstract.

    Google Scholar 

  • Mao, J., Stein, H., Du, A., Zhou, T., Mei, Y., Li, Y., Zang, W., & Li, J. (2004). Re-Os age determination of Cu-Au (Mo) deposits in the middle and lower reaches of Yangtze River and its indication to mineralization. Acta Geologica Sinica, 78(1), 121–131. In Chinese with English abstract.

    Google Scholar 

  • Mao, J., Xie, G., Duan, C., Pirajno, F., Ishiyama, D., & Chen, Y. (2011). A tectonogenetic model for porphyry–skarn–stratabound Cu–Au–Mo–Fe and magnetite–apatite deposits along the Middle-Lower Yangtze River Valley, Eastern China. Ore Geology Reviews, 43(1), 294–314.

    Article  Google Scholar 

  • Mao, X., Ren, J., Liu, Z., Chen, J., Tang, L., Deng, H., Bayless, R., Yang, B., Wang, M., & Liu, C. (2019). Three-dimensional prospectivity modeling of the Jiaojia-type gold deposit, Jiaodong Peninsula, Eastern China: A case study of the Dayingezhuang deposit. Journal of Geochemical Exploration, 203, 27–44.

    Article  Google Scholar 

  • Mao, X., Zhang, B., Deng, H., Zou, Y., & Chen, J. (2016). Three-dimensional morphological analysis method for geologic bodies and its parallel implementation. Computers & Geosciences, 96, 11–22.

    Article  Google Scholar 

  • Mao, X., Zhao, Y., Deng, H., Zhang, B., Liu, Z., Chen, J., Zou, Y., & Lai, J. (2018). Quantitative analysis of intrusive body morphology and its relationship with skarn mineralization—A case study of Fenghuangshan copper deposit, Tongling, Anhui, China. Transactions of Nonferrous Metals Society of China, 28(1), 151–162.

    Article  Google Scholar 

  • Nathwani, C., Wilkinson, J., Fry, G., Armstrong, R., Smith, D., & Ihlenfeld, C. (2022). Machine learning for geochemical exploration: classifying metallogenic fertility in arc magmas and insights into porphyry copper deposit formation. Mineralium Deposita, 57(7), 1143–1166.

    Article  Google Scholar 

  • Nielsen, S., Cunningham, F., Hay, R., Partington, G., & Stokes, M. (2015). 3D prospectivity modelling of orogenic gold in the Marymia Inlier, Western Australia. Ore Geology Reviews, 71, 578–591.

    Article  Google Scholar 

  • Parsa, M. (2021). A data augmentation approach to XGboost-based mineral potential mapping: An example of carbonate-hosted Zn-Pb mineral systems of Western Iran. Journal of Geochemical Exploration, 228, 106811.

    Article  Google Scholar 

  • Parsa, M., & Maghsoudi, A. (2021). Assessing the effects of mineral systems-derived exploration targeting criteria for random Forests-based predictive mapping of mineral prospectivity in Ahar-Arasbaran area, Iran. Ore Geology Reviews, 138, 104399.

    Article  Google Scholar 

  • Payne, C., Cunningham, F., Peters, K., Nielsen, S., Puccioni, E., Wildman, C., & Partington, G. (2015). From 2D to 3D: Prospectivity modelling in the Taupo volcanic zone, New Zealand. Ore Geology Reviews, 71, 558–577.

    Article  Google Scholar 

  • Peng, W. (2017). Research on Image Classification Algorithm based on Support Vector Machine and Semi-Supervised Deep Belief Network Learning. Jiangxi University of Science and Technology. In Chinese with English abstract.

    Google Scholar 

  • Pirajno, F., & Zhou, T. (2015). Intracontinental porphyry and porphyry skarn mineral systems in eastern China: Scrutiny of a special case “made-in-China.” Economic geology and the bulletin of the Society of Economic Geologists, 110(3), 603–629.

    Article  Google Scholar 

  • Porwal, A., Carranza, E., & Hale, M. (2003). Artificial neural networks for mineral-potential mapping: A case study from Aravalli Province, western India. Natural Resources Research, 12(3), 155–171.

    Article  Google Scholar 

  • Porwal, A., González-Álvarez, I., Markwitz, V., McCuaig, T., & Mamuse, A. (2010). Weights-ofevidence and logistic regression modeling ofmagmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia. Ore Geology Reviews, 38, 184–196.

    Article  Google Scholar 

  • Qin, Y., Wu, W., Xie, L., Ou, P., & Huang, X. (2021). Application of machine learning based mineral prospectivity mapping in the Yuexi antimony orefield, Hunan province. Journal of East China University of Technology (Natural Science), 44(1), 28–40. In Chinese with English abstract.

    Google Scholar 

  • Rajaraman, S., Siegelman, J., Alderson, P., Folio, L., Folio, L., & Antani, S. (2020). Iteratively pruned deep learning ensembles for COVID-19 detection in chest X-Rays. IEEE Access, 8, 115041–115050.

    Article  Google Scholar 

  • Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. (2015). Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews, 71, 804–818.

    Article  Google Scholar 

  • Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1249.

    Google Scholar 

  • Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5, 197–227.

    Article  Google Scholar 

  • Schill, W., & Jockel, K. (1993). Logistic analysis in case-control studies under validation sampling. Biometrika, 80(2), 339–352.

    Article  Google Scholar 

  • Singer, D., & Kouda, R. (1996). Application of feed forward neural network in search for Kuoroko deposits in the Hokuroku district, Japan. Mathematical Geology, 28, 1017–1023.

    Article  Google Scholar 

  • Taha, A., Xi, Y., He, Q., Hu, A., Wang, S., & Liu, X. (2023). Investigating the capabilities of various multispectral remote sensors data to map mineral prospectivity based on random forest predictive model: A case study for gold deposits in Hamissana Area, NE Sudan. Minerals, 13(1), 49.

    Article  Google Scholar 

  • Wang, G., Li, R., Carranza, E. J. M., Zhang, S., Yan, C., Zhu, Y., Qu, J., Hong, D., Song, Y., Han, J., Ma, Z., Zhang, H., & Yang, F. (2015). 3D geological modeling for prediction of subsurface Mo targets in the Luanchuan district, China. Ore Geology Reviews, 71, 592–610.

    Article  Google Scholar 

  • Wang, S. (2015). Porphyry Deposits and Associated Magmatic Activity in the Anhui Segment of the Middle-Lower Yangtze River Valley Metallogenic Belt. Hefei University of Technology. In Chinese with English abstract.

    Google Scholar 

  • Wang, Y., Wang, C., & Liu, H. (2011). Application of simulated Annealing genetic Algorithm in multi-user detection technology. Application of Electronic Technique, 37(04), 102–105. in Chinese with English abstract.

    Google Scholar 

  • Wu, B., Li, X., Yuan, F., Li, H., & Zhang, M. (2022). Transfer learning and siamese neural network based identification of geochemical anomalies for mineral exploration: A case study from the Cu-Au deposit in the NW Junggar area of northern Xinjiang Province, China. Journal of Geochemical Exploration, 232, 106904.

    Article  Google Scholar 

  • Xi, Y., Chai, T., & Hun, W. (1996). Review of genetic algorithm. Control Theory and Applications, 06, 697–708. In Chinese.

    Google Scholar 

  • Xiao, K., Li, N., Porwal, A., Holden, E. J., Bagas, L., & Lu, Y. (2015). GIS-based 3D prospectivity mapping: A case study of Jiama copper-polymetallic deposit in Tibet. China. Ore Geology Reviews, 71(3), 357–367.

    Google Scholar 

  • Xiao, K., Pirajno, F., Xu, Y., Zhang, S., Sun, L., Cong, Y., Fan, J., Yin, J., Zhu, Y., & Wei, H. (2020). Study on the major minerals potential in China. Ore Geology Reviews, 127, 103816.

    Article  Google Scholar 

  • Xiao, K., Xiang, J., Fan, M., & Xu, Y. (2021). 3D mineral prospectivity mapping based on deep metallogenic prediction theory: A case study of the lala copper mine, Sichuan, China. Journal of Earth Science, 32(02), 48–357.

    Article  Google Scholar 

  • Xiao, Q. (2021). Mineralization of the Chating Copper and Gold Deposit in Xuancheng Ore District. Hefei University of Technology. In Chinese with English abstract.

    Google Scholar 

  • Xiong, Y., & Zuo, R. (2016). Recognition of geochemical anomalies using a deep autoencoder network. Computers & Geosciences, 86, 75–82.

    Article  Google Scholar 

  • Xiong, Y., & Zuo, R. (2021). A positive and unlabeled learning algorithm for mineral prospectivity mapping. Computers & Geosciences, 147, 104667.

    Article  Google Scholar 

  • Xu, X., Xu, X., Xie, Q., Fu, Z., Qian, S., & Xie, Z. (2019). Geological and geochemical characteristics and genesis of Chaling Cu-Au deposit in Xuancheng, Anhui Province. Acta Petrologica Sinica, 35(12), 3659–3676. In Chinese with English abstract.

    Article  Google Scholar 

  • Ye, R. (2020). 3D Geological Modeling and Metallogenic Prediction of Magushan ore field in Nanling-Xuancheng ore district. Hefei University of Technology. In Chinese with English abstract.

    Google Scholar 

  • Ye, Z., Feng, A., & Gao, H. (2019). Google store customer purchasing power prediction based on deep LightGBM integrated learning model. Journal of Computer Applications, 39(12), 3434–3439. In Chinese with English abstract.

    Google Scholar 

  • Yuan, F., Zhang, M., Li, X., Ge, C., Lu, S., Li, J., Zhou, Y., & Lan, X. (2019). Prospectivity modeling: From two dimension to three-dimension. Acta Petrologica Sinica, 35(12), 3863–3874. In Chinese with English abstract.

    Article  Google Scholar 

  • Zhai, Y., Yao, S., Lin, X., Zhou, X., Wan, T., Jin, F., & Zhou, Z. (1992). The Metallogeny of the Fe-Cu (Au) Deposits in the Middle-Lower Yangtze Region (p. 235). Beijing: Geological Publishing House. In Chinese.

    Google Scholar 

  • Zhang, D. (2018). Lcing prediction of fan blade based on LightGBM. Shanghai Teachers University. In Chinese with English abstract.

    Google Scholar 

  • Zhang, M., Zhou, G., Shen, L., Zhao, W., Liao, B., Yuan, F., Li, X., Hu, X., & Wang, C. (2019). Comparison of 3D prospectivity modeling methods for Fe-Cu skarn deposits: A case study of the Zhuchong Fe-Cu deposit in the Yueshan orefield (Anhui), eastern China. Ore Geology Reviews, 114, 103126.

    Article  Google Scholar 

  • Zhang, Q., Chen, J., Xu, H., Jia, Y., Chen, X., Jia, Z., & Liu, H. (2022). Three-dimensional mineral prospectivity mapping by XGBoost modeling: A case study of the lannigou gold deposit, China. Natural Resources Research, 31(3), 1135–1156.

    Article  Google Scholar 

  • Zhao, Y., Lin, W., Bi, S., & Li, D. (1986). Basic geological characteristics of skarn deposits of China. Bulletin of Chinese Academy of Geological Sciences, 03, 59–87. In Chinese with English abstract.

    Google Scholar 

  • Zhou, G. (2021). Study on Element migration and enrichment rule of data-driven Cu-sulpho-tungsten polymetallic deposit in Buckwheat Mountain and its prospecting indication. HeFei University of Technology. In Chinese with English abstract.

    Google Scholar 

  • Zhou, T., Fan, Y., Wang, S., & White, N. C. (2017). Metallogenic regularity and Metallogenic model of the Middle-Lower Yangtze River Valley Metallogenic Belt. Acta Petrologica Sinica, 33(11), 3353–3372. In Chinese with English abstract.

    Google Scholar 

  • Zhou, T., Fan, Y., Yuan, F., Zhang, L., Ma, L., Qian, B., & Xie, J. (2011). Petrogenesis and metallogeny study of the volcanic basin in the Middle and Lower Yangtze Metallogenic Belt. Acta Geologica Sinica, 85, 712–730. In Chinese with English abstract.

    Google Scholar 

  • Zhou, T., Fan, Y., Yuan, F., & Zhong, G. X. (2012). Progress of geological study in the Middle-Lower Yangtze River Valley metallogenic belt. Acta Petrologica Sinica, 28, 3051–3066. In Chinese with English abstract.

    Google Scholar 

  • Zhou, T. F., Wang, S. W., Fan, Y., Yuan, F., Zhang, D. Y., & White, N. C. (2015). A review of the intracontinental porphyry deposits in the Middle-Lower Yangtze River Valley metallogenic belt, Eastern China. Ore Geology Reviews, 65, 433–456.

    Article  Google Scholar 

  • Zhu, Y., Huang, H., & Wei, J. (2021). Online tool wear status identification based on GA-Light GBM. Modular Machine Tool & Automatic Manufacturing Technique, 10, 83–87. In Chinese with English abstract.

    Google Scholar 

  • Zuo, R., & Carranza, E. (2011). Support vector machine: A tool for mapping mineral prospectivity. Computers & Geosciences, 37, 1967–1975.

    Article  Google Scholar 

  • Zuo, R., Luo, Z., Xiong, Y., & Yin, B. (2022). Geologically constrained variational autoencoder for mineral prospectivity mapping. Natural Resources Research, 31(3), 1121–1133.

    Article  Google Scholar 

Download references

Acknowledgments

This research was financially supported by the National Natural Science Foundation of China (Grant Nos. 42230802, 42072321, 41820104007).

Author information

Authors and Affiliations

  1. Ore Deposit and Exploration Centre (ODEC), School of Resources and Environmental Engineering, Hefei University of Technology, Hefei, 230009, Anhui, China

    He Li, Xiaohui Li, Feng Yuan, Mingming Zhang, Xiangling Li, Can Ge & Zhiqiang Wang

  2. Anhui Province Engineering Research Center for Mineral Resources and Mine Environments, Hefei University of Technology, Hefei, 230009, Anhui, China

    He Li, Xiaohui Li, Feng Yuan, Mingming Zhang, Xiangling Li, Can Ge & Zhiqiang Wang

  3. Geological Exploration Technology Institute of Anhui Province, Hefei, 230001, Anhui, China

    Dong Guo & Xueyi Lan

  4. Public Geological Survey Management Center of Anhui Province, Hefei, 230091, Anhui, China

    Minhui Tang & Sanming Lu

Authors
  1. He Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaohui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Feng Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mingming Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiangling Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Can Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zhiqiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Dong Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xueyi Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Minhui Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Sanming Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaohui Li.

Ethics declarations

Conflict of Interest

The authors have no competing interests to declare that are relevant to the content of this article.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, H., Li, X., Yuan, F. et al. Genetic Algorithm Optimized Light Gradient Boosting Machine for 3D Mineral Prospectivity Modeling of Cu Polymetallic Skarn-Type Mineralization, Xuancheng Area, Anhui Province, Eastern China. Nat Resour Res 32, 1897–1916 (2023). https://doi.org/10.1007/s11053-023-10227-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-023-10227-y

Keywords

Search

Navigation