Skip to main content

Three-Dimensional Mineral Prospectivity Mapping by XGBoost Modeling: A Case Study of the Lannigou Gold Deposit, China

Abstract

Three-dimensional mineral prospectivity mapping (3DMPM) aims to explore deep mineral resources and many methods have been developed for this task in recent years. The eXtreme Gradient Boosting (XGBoost) algorithm, an improvement of the gradient boosting decision tree model, has been used widely in many fields due to its high computational efficiency and its ability to alleviate overfitting effectively. The Lannigou gold deposit in Guizhou is a well-known epithermal gold deposit in the "Golden Triangle" area of Guizhou, Guangxi and Yunnan, China, with potential for deep exploration. Geological data were used to establish a three-dimensional (3D) model, and subsequently a prospectivity model was built based on the metallogenic system and on geological anomaly theories. The 3D spatial reconstruction of mineralization anomalies was completed and 3D prediction layers of the ore-controlling factor were implemented to establish the basic data for the prediction model. The XGBoost classification model was proved efficient for 3DMPM, outperforming the weights of evidence method according to prediction success rate and accuracy.

This is a preview of subscription content, access via your institution.

Figure 1
Figure 2

modified from Cong et al., 2016)

Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14

References

  • Agterberg, F. P., & Bonham-Carter, G. F. (2005). Measuring the performance of mineral-potential maps. Natural Resources Research, 14(1), 1–17.

    Article  Google Scholar 

  • Agterberg, F. P., & Cheng, Q. (2002). Conditional independence test for weights-of-evidence modeling. Natural Resources Research, 11(4), 249–255.

    Article  Google Scholar 

  • Al-Anazi, A., & Gates, I. D. (2010). On the capability of support vector machines to classify lithology from well Logs. Natural Resources Research, 19(2), 125–139.

    Article  Google Scholar 

  • Brandmeier, M., Cabrera Zamora, I. G., Nykänen, V., & Middleton, M. (2020). Boosting for mineral prospectivity modeling: A new GIS toolbox. Natural Resources Research, 29(1), 71–88.

  • Cai, M., Cheng, D., Zhang, C., Zhu, R., Tang, Y., & Qu, J. (2017). Prediction of debris composition in Glutenite by machine learning method: A case study in Baikouquan formation of Well X723 in the NW margin of Junggar basin. Journal of xi’an Shiyou University (natural Science Edition), 32(5), 22–28. (in Chinese with English abstract).

    Google Scholar 

  • Cai, L., Wu, D., Fang, L., & Zheng, X.(2019). Tree species identification using xgboost based on GF-2 images. Forest Resources Management, 10(5), 44-51. https://doi.org/10.13466/j.cnki.lyzygl.2019.05.009 (in Chinese with English abstract).

    Article  Google Scholar 

  • Caine, J. S., Evans, J. P., & Forster, C. B. (1996). Fault zone architecture and permeability structure. Geology, 24(11), 1025–1028.

    Article  Google Scholar 

  • Cao, J., Zhang, Z., Du, J., Zhang, L., Song, Y., & Sun, G. (2020). Multi-geohazards susceptibility mapping based on machine learning- a case study in Jiuzhaigou, China. Natural Hazards, 102(3), 851–871.

    Article  Google Scholar 

  • Caumon, G., Ortiz, J. M., & Rabeau, O. (2006). A comparative study of three-driven mineral potential mapping techniques. International Association for Mathematical Geology.

  • Carranza, E. J. M., & Laborte, A. G. (2015). Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines). Computers & Geosciences, 74, 60–70.

  • Chen, J., Chen, Y., Zeng, M., Hu, Z., Zhao, J., Hu, Q., Sang, B., Tang, Y., & Duan, Y. (2008). 3D positioning and quantitative prediction of the Koktokay No. 3 pegmatite dike, Xinjiang, China, based on the digital miner al deposit model. Geological Bulletin of China, 27(4), 552–559. (in Chinese with English abstract)

  • Chen, J., Chen, Y., Zhu, P., Wang, L., Sang, B., & Zhao, J. (2011a). Digital ore deposit model and its application: a case study of the prognosis of the Koktokay No.3 pegmatite dike concealed rare metal deposit in Altay area of Xinjiang. Geological Bulletin of China, 30(5), 630–64. (in Chinese with English abstract)

  • Chen, J., Lü, P., Wu, W., Zhao, J., & Hu, Q. (2007a). A 3-D prediction method for blind orebody based on 3-D visualization model and its application. Earth Science Frontiers, 14(5), 54–62. (in Chinese with English abstract).

    Article  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. Geotectonicaet Metallogenia, 44(2), 231–241. (in Chinese with English abstract).

    Google Scholar 

  • Chen, J., Shi, R., Chen, Z., Wang, Li., & Sun, Y. (2012a). 3D positional and quantitative prediction of the Xiaoqinling gold ore belt in Tongguan, Shaanxi, China. Acta Geologica Sinica (English Edition), 86(3), 653–660.

    Article  Google Scholar 

  • Chen, J., Yu, M., Yu, P., Shang, B., Zheng, X., & Wang, L. (2014a). Method and practice of 3D geological modeling at key metallogenic belt with large and medium scale. Acta Geologica Sinica, 88(6), 1187–1195. (in Chinese with English abstract).

    Article  Google Scholar 

  • Chen, J., Yu, P., Shi, R., Yu, M., & Zhang, S. (2014b). Research on three-dimensional quantitative prediction and evaluation methods of regional concealed ore bodies. Earth Science Frontiers, 21(5), 211–220. (in Chinese with English abstract).

    Google Scholar 

  • Chen, J., Wang, C., Shang, B., & Shi, R. (2012b). Three-dimensional metallogenic prediction in Yongmei region based on digital ore deposit model. Scientific & Technological Management of Land & Resources, 29(6), 14–20.

    Google Scholar 

  • Chen, M. (2007). The genetic model of Jinfeng (Lannigou) gold deposit based on the coupling of metallotectonics and ore-forming fluid. Beijing. Chinese Academy of Geological Sciences. (in Chinese with English abstract)

  • Chen, M., Mao, J., Uttley, P. J., Norman, T., Wu, L., Zheng, J., & Qin, Y. (2007b). Structure analysis and structural metallogenesis of Jinfeng (Lannigou) gold deposit in Guizhou province. Mineral Deposits, 26(4), 380–396. (in Chinese with English abstract).

    Google Scholar 

  • Chen, M., Mao, J., Bierlein, F. P., Norman, T., & Uttley, P. J. (2011b). Structural features and metallogenesis of the carlin-type Jinfeng (Lannigou) gold deposit, Guizhou Province, China. Ore Geology Reviews, 43(2011), 217–234.

    Google Scholar 

  • Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In The 22nd ACM sigkdd international conference. ACM.

  • Cline, J. S., & Hofstra, A. A. (2000). Ore-fluid evolution at the Getchell Carlin-type gold deposit, Nevada, USA. European Journal of Mineralogy, 12(1), 195–212.

    Article  Google Scholar 

  • Cong, Y., Xiao, K., Liu, Z., & Dong, Q. (2016). Geological characteristics and resource potential analysis of the Nanpanjiang-Youjiang Sn–Sb–Mn–Zn–Al–Au metallogenic belt. Acta Geologica Sinica, 90(07), 1573–1588. (in Chinese with English abstract).

    Google Scholar 

  • Deng, J., & Wang, Q. (2016). Gold mineralization in China: Metallogenic provinces, deposit types and tectonic framework. Gondwana rEsearch, 36, 219–274.

    Article  Google Scholar 

  • Deng, J., Wang, Q., & Li, G. (2017). Tectonic evolution, superimposed orogeny, and composite metallogenic system in China. Gondwana Research, 50, 216–266.

    Article  Google Scholar 

  • Dietterich, T. (1995). Overfitting and undercomputing in machine learning. ACM Computing Surveys, 27(3), 326–327.

    Article  Google Scholar 

  • Dong, Q., Xiao, K., Chen, J., & Cong, Y. (2010). The quantitative analysis of regional metallogenic fault in the northern segment of the Sanjiang metallogenic belt, southwestern China. Geological Bulletin of China, 29(10), 1479–1485. (in Chinese with English abstract).

    Google Scholar 

  • Fan, J., Wang, X., Wu, L., Zhou, H., Zhang, F., Yu, X., & Xiang, Y. (2018). Comparison of support vector machine and extreme gradient boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China. Energy Conversion and Management, 164(2018), 102–111.

    Article  Google Scholar 

  • Fu, G., Lü, Q., Yan, J., Farquharson, C. G., Qi, G., Zhang, K., Zhang, Y., Wang, H., & Luo, F. (2021). 3D mineral prospectivity modeling based on machine learning: A case study of the Zhuxi tungsten deposit in northeastern Jiangxi Province, South China. Ore Geology Reviews, 131(2021), 104010.

  • Gondy, L. A., Thomas, C., & Bayes, N. (1993). Programs for machine learning. Advances in Neural Information Processing Systems, 79(2), 937–944.

    Google Scholar 

  • Guo, H., Lu, Z., Liu, S., Zhang, F., & Yu, L. (2003). Geological characteristics of the Zhaishang carlin type gold deposit and its ore control factor, Gansu. Gold Geology, 9(3), 21–26. (in Chinese with English abstract).

    Google Scholar 

  • Hagemann, S. G., Lisitsin, V., & Huston. D. L. (2016). Mineral system analysis: Quovadis. Ore Geology Reviews, 504–522.

  • Han, X. (2012). The study on geologic-geochemical characteristics and causes discuss of the Lannigou carlin-type gold deposits in Guizhou. Chengdu University of Technology. (in Chinese with English abstract).

    Google Scholar 

  • Hao, J. (2007). Controlling structure mode of micro-fine disseminated gold deposits of Qianxi`nan. Guizhou University. (in Chinese with English abstract).

    Google Scholar 

  • Hu, Q., Chen, J., & Tian, Y. (2018).3D Metallogenic prediction and prediction evaluation: A case study in Hongqigou-Shenshuitan Gold deposit in the Eastern Kunlun Metallogenic Belt. Geoscience, 32(2), 335–343. (in Chinese with English abstract). https://doi.org/10.19657/j.geoscience.1000-8527.2018.02.12

    Article  Google Scholar 

  • Islam, S., Sholahuddin, A., & Abdullah, A. S. (2021). Extreme gradient boosting (XGBoost) method in making forecasting application and analysis of USD exchange rates against rupiah. Journal of Physics Conference Series, 1722, 012–016.

    Article  Google Scholar 

  • Jia, W., Sun, L., & Jing, Y. (2018). Surgical prognosis quality score of femoral neck fracture based on XGBoost model. Journal of Taiyuan University of Technology, 49(1), 174–178. (in Chinese with English abstract). https://doi.org/10.16355/j.cnki.issn1007-9432tyut.2018.01.027

  • Joly, A., Porwal, A., & Campbell, M. C. T. (2010). 3D geophysical and geological modeling for understanding the gold mineral systems in the Tanami Orogen, Western Australia. EGU Vienna. EGU General Assembly Conference Abstracts.

  • Li, S., Chen, J., Liu, C., & Wang, Y. (2021). Mineral prospectivity prediction via convolutional neural networks based on geological big data. Journal of Earth Science, 32(2), 327–347.

    Article  Google Scholar 

  • Li, T., Zuo, R., Zhao, X., & Zhao, K. (2022). Mapping prospectivity for regolith-hosted REE deposits via convolutional neural network with generative adversarial network augmented data. Ore Geology Reviews, 142, 104693.

  • Li, W., Yin, Y., Quan, X., & Zhang, H. (2019). Gene expression value prediction based on XGBoost algorithm. Frontiers in Genetics, 10, 1077.

    Article  Google Scholar 

  • Li, W., Chen, J., Jia, Y., Zhou, G., Mao, X., & Xiao, K. (2020). Three-dimensional modeling and comprehensive metallogenic prediction of the Zaozigou Gold Deposit, Gansu Province. Acta Geoscientica Sinica, 41(2), 144–156. (in Chinese with English abstract).

    Google Scholar 

  • Li, Y., Guo, H., Li, Y., & Liu, X. (2016). A boosting based ensemble learning algorithm in imbalanced data classification. Systems Engineering-Theory & Practice, 36(1), 189–199. (in Chinese with English abstract). https://doi.org/10.12011/1000-6788(2016)01-0189-11

  • Liu, J., Mao, G., Wu, S., Wang, J., Ma, X., Li, L., Liu, G., Liao, Y., & Zheng, W. (2010). Metallogenic characteristics and formation mechanism of Zhaishang gold deposit, southern, Gansu Province. Mineral Deposits, 29(1),85–100. (in Chinese with English abstract)

  • Liu, L., Lu, J., Tao, C., Liao, S., Su, C., Huang, N., & Xu, X. (2022). Fuzzy forest machine learning predictive model for mineral prospectivity: A case study on Southwest Indian Ridge 48.7°E–50.5°E. Natural Resources Research, 31(1), 99–116.

  • Luo, X. (1993). The features of F3 fault controlling gold deposit and the study of mechanism of tectonic mineralization in Lannigou gold orefield. Geology of Guizhou, 1(1), 26–34. (in Chinese with English abstract).

    Google Scholar 

  • Luo, J., Zhang, Q., Song, B., Wang, X., Yang, Z., Zhao, Y., & Liu, S. (2017). Application of integrated geophysical and geochemical data processing to metallogenic target zone quantitative prediction and optimization. Bulletin of Mineralogy, Petrology and Geochemistry, 36(6), 886–890. (in Chinese with English abstract).

    Google Scholar 

  • Ma, X., Fang, C., & Ji, J. (2020). Prediction of outdoor air temperature and humidity using Xgboost. IOP Conference Series: Earth and Environmental Science, 427(1), 012013–012019.

    Article  Google Scholar 

  • Ma, X., Liu, J., Li, L., Mao, G., & Guo, Y. (2008). Zhaishang gold deposit in Gansu Province: Characteristics, evolution of ore-forming fluids and their metallogenic implications. Acta Petrologica Sinica, 24(9), 2069–2078. (in Chinese with English abstract).

    Google Scholar 

  • Mao, X., Tang, Y., Lai, J., Zou, Y., Chen, J., Peng, S., & Shao, Y. (2011). Three-dimensional structure of metallogenic geologic bodies in the Fenghuangshan ore field and ore-controlling geological factors. Acta Geologica Sinica, 85(9), 1507–1518.

    Google Scholar 

  • Mao, X., Zou, Y., Chen, J., Lai, J., Peng, S., Shao, Y., Shu, Z., Lu, J., & Lu, C. (2010). Three-dimensional visual prediction of concealed ore bodies in the deep and marginal parts of crisis mines: A case study of the Fenghnangshan ore field in Tongling, Anhui, China. Geological Bulletin of China, 29(2/3), 401–413.

    Google Scholar 

  • Nie, A. (2007). A mineralization mechanism as well as minerogenetic prospect of Carlin-type gold deposit in southwestern of Guizhou. Kunming University of Science and Technology.

    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 

  • Payne, C. E., Cunningham, F., Peters, K. J., Nielsen, S., Puccioni, E., Wildman, C., & Partington, G. A. (2015). From 2D to 3D: Prospectivity modelling in the Taupo Volcanic Zone, New Zealand. Ore Geology Reviews, 29(1), 558–577.

    Article  Google Scholar 

  • Parsa, M., Maghsoudi, A., & Yousefi, M. (2018). Spatial analyses of exploration evidence data to model skarn-type copper prospectivity in the Varzaghan district, NW Iran. Ore Geology Reviews, 92, 97–112.

    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.

  • Parsa, M., & Carranza, E. J. M. (2021). Modulating the impacts of stochastic uncertainties linked to deposit locations in data-driven predictive mapping of mineral prospectivity. Natural Resources Research, 30(5), 3081–3097.

    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.

  • Parsa, M., Carranza, E. J. M., & Ahmadi, B. (2021). Deep GMDH neural networks for predictive mapping of mineral prospectivity in terrains hosting few but large mineral deposits. Natural Resources Research, 31(1), 37–50.

    Article  Google Scholar 

  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12, 2825-2830.

    Google Scholar 

  • Qin, Y., Liu, L., & Wu, W. (2021). Machine learning-based 3D modeling of mineral prospectivity mapping in the Anqing Orefield, Eastern China. Natural Resources Research, 9(5), 1–22.

    Google Scholar 

  • Rabby, Y. W., & Li, Y. (2020). Landslide susceptibility mapping using integrated methods: A case study in the Chittagong Hilly Areas, Bangladesh. Geosciences, 10(483), 483.

    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 

  • Sadr, M. P., & Nazeri, M. (2018). Random forests algorithm in podiform chromite prospectivity mapping in Dolatabad area, SE Iran. Journal of Mining and Environment, 9(2), 403–416.

    Google Scholar 

  • Sebtosheikh, M. A., & Salehi, A. (2015). Lithology prediction by support vector classifiers using inverted seismic attributes data and petrophysical logs as a new approach and investigation of training data set size effect on its performance in a heterogeneous carbonate reservoir. Journal of Petroleum Science and Engineering, 134, 143–149.

    Article  Google Scholar 

  • Shi, R., Chen, J., Liu, H., & Wang, Q. (2014). The 3D prediction model and division of targets in Jiaojia gold ore belt, Shandong Province. Geoscience, 28(4), 743–750. (in Chinese with English abstract).

    Google Scholar 

  • Su, H., & Wang, G. (2013). Tectonic ore-controlling and ore-forming research of Lannigou gold deposit in SW Guizhou: constraint from experiment simulations and scanning electron microscope. Global Geology, 32(2),403–411. (in Chinese with English abstract).

  • Singer, D. A., & Kouda, R. (1996). Application of a feed forward neural network in the search for Kuroko deposits in the Hokuroku District. Mathematical Geology, 28(8), 1017–1023.

    Article  Google Scholar 

  • Torlay, L., Perrone-Bertolotti, M., Thomas, E., & Baciu, M. (2017). Machine learning–XGBoost analysis of language networks to classify patients with epilepsy. Brain Informatics, 4(3), 159–169.

    Article  Google Scholar 

  • Wu, C. (2012). Organic matter in Carlin-type gold deposits and paleo-oil reservoirs in Southwest Guizhou – Source, Maturity and Association, Beijing, China University of Geosciences (Beijing). (in Chinese with English abstract)

  • Wu, H. (2019a). Research on diabetes prediction model based on XGBoost algorithm. In 8th International conference on advanced materials and computer science (ICAMCS 2019a).

  • Wu, S. (2019b). The study of tectonic-magmatic-hydrothermal metallogenic model of Carlin-type gold deposit in Southwestern Guizhou Province, China, Beijing, China university of Geosciences (Beijing). (in Chinese with English abstract).

  • Wyborn, L., Heinrich, C., & Jaques, A. (1994). Australian Proterozoic mineral systems: Essential ingredients and mappable criteria. The AusIMM Annual Conference, 109–115.

  • Xiang, J., Chen, J., Bagas, L., Li, S., Wei, H., & Chen, B. (2020). Southern China's manganese resource assessment: An overview of resource status, mineral system, and prediction model. Ore Geology Reviews, 116, 103261.

  • Xiang, J., Xiao, K., Carranza, E.J.M., Chen, J., & Li, S. (2020). 3D Mineral Prospectivity Mapping with Random Forests: A Case Study of Tongling, Anhui, China. Natural Resources Research, 29(1), 395–414. https://doi.org/10.1007/s11053-019-09578-2

    Article  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 - ScienceDirect. Ore Geology Reviews, 71, 611–632.

  • Xiao, K., Li, N., Sun, L., Zou, W., & Li, Y. (2012). Large scale 3D mineral prediction methods and channels based on 3D information technology. Journal of Geology, 36(3), 229–236. (in Chinese with English abstract).

    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(2), 348–357.

    Article  Google Scholar 

  • Xiaoye, J., Hofstra, A., Hunt, A., Liu, J.-Z., Yang, W., & Li, J.-W. (2020). Noble gases fingerprint the source and evolution of ore-forming fluids of carlin-type gold deposits in the golden triangle, South China. Economic Geology, 115(2), 455–469.

    Article  Google Scholar 

  • Xiong, Y., Zuo, R., & Carranza, E. (2018). Mapping mineral prospectivity through big data analytics and a deep learning algorithm. Ore Geology Reviews, 102, 811–817.

    Article  Google Scholar 

  • Yan, X., Gu, H., Xiao, Y., Ren, H., & Ni, J. (2019). XGBoost algorithm applied in the interpretation of tight-sand gas reservoir on well logging data. Oil Geophysical Prospecting, 54(2), 447–455. (in Chinese with English abstract).

    Google Scholar 

  • Ye, C., & Du, D. (2018). Structural style and structural ore control characteristics of the Zhenfeng Lannigou, Guizhou. Journal of Guiyang University Natural Sciences(Quarterly), 13(1), 97–100. (in Chinese with English abstract).

  • Yu, C. (2001). Fractal growth of ore-forming dynamical systems at the edge of chaos-a new metallogeny and methodology. Earth Science Frontiers, 8(3), 9–27. (in Chinese with English abstract).

    Google Scholar 

  • Yan, J., Hu, R., Liu, S., Lin, Y., Zhang, J., & Fu, S. (2018). NanoSIMS element mapping and sulfur isotope analysis of Au-bearing pyrite from Lannigou Carlin-type Au deposit in SW China: New insights into the origin and evolution of Au-bearing fluids. Ore Geology Reviews, 92, 29-41. https://doi.org/10.1016/j.oregeorev.2017.10.015

    Article  Google Scholar 

  • Yu, W., Liu, G., Wang, X., Chen, X., & Wang, Z. (2010). Discussion on the ore-controlling factors of Zhaishang gold deposit in Gansu province. Gold Science & Technology, 18(3), 46–50. (in Chinese with English abstract)

  • Zeng, M. (2017). Geological tectonic evolution and gold mineralization in the epithermal mineralization area of southwestern Guizhou. Chengdu University of Technology. (in Chinese with English abstract).

    Google Scholar 

  • Zhai, Y. (1999). On the metallogenic system. Earth Science Frontiers, 6(1), 13–27. (in Chinese with English abstract).

    Google Scholar 

  • Zhang, S., Emmanuel, J. M., Xiao, K., Wei, H., Yang, F., Chen, Z., & Li, N. (2021). Mineral prospectivity mapping based on isolation forest and random forest: Implication for the existence of spatial signature of mineralization in outliers. Natural Resources Research, 8(5), 1–19.

    Google Scholar 

  • Zhang, Q., Chen, J., Chen, X., Li, G., Liu, C., & Zhu, J. (2020a). 3D quantitative prediction in the Lannigou gold deposit, Guizhou Province. Acta Geoscientica Sinica, 41(2), 193–206. (in Chinese with English abstract).

    Google Scholar 

  • Zhang, Q., Chen, J., Hu, B. & Zhu, Y. (2018): Three-dimensional prediction of concealed ore based on fuzzy weights of evidence and information contents: a case study in Luokuang area in Shanxi province. China Mining Magazine, 27(7), 171–177. (in Chinese with English abstract).

  • Zhang, X., & Zhang, Y. (2018). Reservoir prediction through cross-validation based on support vector machine. Geophysical Prospecting for Petroleum, 57(4), 597–600. (in Chinese with English abstract).

    Google Scholar 

  • Zhang, Z., Wang, G., Ding, Y., & Carranza, E. (2020b). 3D mineral exploration targeting with multi-dimensional geoscience datasets, Tongling Cu(-Au) District, China. Journal of Geochemical Exploration, 221(2021), 106702.

  • Zhang, Z., Zhang, J., Wang, G., Carranza, E., & Wang, H. (2020c). From 2D to 3D modeling of mineral prospectivity using multi-source geoscience datasets, Wulong Gold District, China. Natural Resources Research., 29(1), 345–364.

    Article  Google Scholar 

  • Zhao, J., Chi, H., Shao, Y., & Peng, X. (2022). Application of AdaBoost Algorithms in Fe Mineral Prospectivity Prediction: A Case Study in Hongyuntan–Chilongfeng Mineral District, Xinjiang Province, China. Natural Resources Research, https://doi.org/10.1007/s11053-022-10017-y

  • Zhao, P., Chen, J., & Chen, J. (2001). On diversity of mineralization and the spectrum ore deposits. Journal of China University of Geosciences (Earth Science), 26(2), 111–117. (in Chinese with English abstract).

    Google Scholar 

  • Zhao, P., & Chi, S. (1991). A preliminary view on geological anomaly. Journal of China University of Geosciences(Earth Science), 16(3), 241–248. (in Chinese with English abstract)

  • Zhao, P., & Meng, X. (1993). Geological anomaly and mineral prediction. Journal of China University of Geosciences (earth Science)., 18(1), 39–47. (in Chinese with English abstract).

    Google Scholar 

  • Zheng, S., Hu, Y., Guan, S., & Liu, X. (2020). Structural deformation and evolution of the Lannigou Gold Orefield in southwestern Guizhou. Geological Review, 66(5), 1431–1445. (in Chinese with English abstract).

    Google Scholar 

  • Zheng, X. (2013). Three dimensional concealed orebodies quantitative prediction system architecture and development. China University of Geosciences (Beijing). (in Chinese with English abstract).

    Google Scholar 

  • Zhou, B. (2014). Research and application of imbalanced data classification algorithms based on ensemble learning. Dalian University of Technology. (in Chinese with English abstract).

    Google Scholar 

  • Zou, Y., Chen, Y., & Deng, H. (2021). Gradient boosting decision tree for lithology identification with well logs: A case study of zhaoxian gold deposit, Shandong Peninsula, China. Natural Resources Research, 30(5), 3197–3217.

    Article  Google Scholar 

  • Zhou, Y., Zuo, R., Liu, G., Yuan, F., Mao, X., Guo, Y., Xiao, F., Liao, J., & Liu, J. (2021). The great-leap-forward development of mathematical geoscience during 2010–2019: Big data and artificial intelligence algorithm are changing mathematical geoscience. Bulletin of Mineralogy, Petrology and Geochemistry, 40(3), 556–573.

    Google Scholar 

  • Zuo, R., & Carranza, E. J. M. (2011). Support vector machine: A tool for mapping mineral prospectivity. Computers & Geosciences, 37(12), 1967–1975.

    Article  Google Scholar 

  • Zuo, R., & Xiong, Y. (2017). Big data analytics of identifying geochemical anomalies supported by machine learning methods. Natural Resources Research, 27(1), 5–13.

    Article  Google Scholar 

  • Zuo, R., Xiong, Y., Wang, J., & Carranza, E. J. M. (2019). Deep learning and its application in geochemical mapping. Earth-Science Reviews., 192(2019), 1–14.

    Article  Google Scholar 

  • Zuo, R., Wang, J., & Yin, B. (2021). Visualization and interpretation of geochemical exploration data using GIS and machine learning methods. Applied Geochemistry, 134, 105111.

Download references

Acknowledgments

This research was funded by No. 2017YFC0601502 from the National Key Research and Development Program of China and No. 6142A01190104 from Research on key technology of mineral prediction based on geological big data analysis. We are also grateful for the constructive comments and suggestions from Associate Editor Renguang Zuo, Prof. Mohammad Parsa and one anonymous reviewer.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jianping Chen.

Ethics declarations

Conflict of Interest

There are no conflicts of interest to declare.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, Q., Chen, J., Xu, H. et al. Three-Dimensional Mineral Prospectivity Mapping by XGBoost Modeling: A Case Study of the Lannigou Gold Deposit, China. Nat Resour Res 31, 1135–1156 (2022). https://doi.org/10.1007/s11053-022-10054-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-022-10054-7

Keywords

  • 3DMPM
  • Machine learning
  • XGBoost
  • Lannigou gold deposit