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Bagging-based Positive–Unlabeled Data Learning Algorithm with Base Learners Random Forest and XGBoost for 3D Exploration Targeting in the Kalatongke District, Xinjiang, China

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Abstract

The Kalatongke district is located in northern Xinjiang, where the Kalatongke Cu–Ni sulfide deposit is one of the most famous early Permian Cu–Ni sulfide deposits. At present, 13 shallow mafic intrusions lying less than 500 m in the subsurface have been explored and almost all orebodies are hosted in these mafic intrusions. The ore-bearing intrusions are controlled by faults and are characterized by high magnetic, high density, low resistivity and low seismic wave velocity. With the depletion of proved resources, deep and peripheral prospecting is imminent in the study area. In this paper, three-dimensional (3D) mineral prospectivity modeling is implemented by integrating 3D geological–geophysical data and ore deposit geology. Relevant exploration criteria for prediction were acquired as follows: (1) The bi-dimensional empirical mode decomposition (BEMD) was applied to decompose magnetic and gravity data, and the decomposed images were used to interpret faults and construct fault buffering models; (2) the magnetic susceptibility and density models were obtained by inversion of gravity data and magnetic data; and (3) discrete smooth interpolation was used to interpolate frequency domain controlled-source electromagnetic method (FDCSEM) data and seismic data, and obtain resistivity and seismic wave velocity models. Taking fault buffering model, geophysical models (density, magnetic susceptibility, seismic wave velocity and resistivity) as exploration criteria, the bagging-based positive–unlabeled data learning (BPUL) algorithm that can effectively address the issue of imbalanced training data was used for 3D mineral prospectivity modeling. Random Forest (RF) and XGBoost were applied as the base learners of BPUL to develop novel BPUL–RF and BPUL–XGBoost models, respectively. The results indicate that the BPUL–XGBoost showed better performance than the BPUL–RF, RF and XGBoost prediction models. The prediction–area plot was applied to outline the targets, which are of great significance for further Cu–Ni exploration in the Kalatongke district.

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References

  • Bekker, J., & Davis, J. (2020). Learning from positive and unlabeled data: A survey. Machine Learning, 109(4), 719–760.

    Google Scholar 

  • Bergstra, J., Yamins, D., & Cox, D.D. (2013). Hyperopt: a python library for optimizing the hyperparameters of machine learning algorithms. In: Proceedings of the 12th Python in Science Conference, 13(20), Citeseer.

  • Bergstra, J., Bardenet, R., Bengio, Y., & Kégl, B. (2011). Algorithms for hyper-parameter optimization. Advances in Neural Information Processing Systems, 2011, 2546–2554.

    Google Scholar 

  • Bonham-Carter, G.F., Agterberg, F.P., & Wright, D.F. (1989). Weights of evidence modelling: a new approach to mapping mineral potential. In: Agterberg, F.P., Bonham-Carter, G.F. (Eds.), Statistical Applications in the Earth Sciences Geological Survey of Canada pp. 171–183. Paper 89-9).

  • Breiman, L. (1996). Bagging predictors. Machine Learning, 26(2), 123–140.

    Google Scholar 

  • 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.

    Google Scholar 

  • CAU&BGMRXUAR (Chang’an University &Bureau of Geology and Mineral Resources of Xinjiang Uygur Autonomous Region). (2011). Summary report on exploration work of Kalatongke copper-nickel mining area and its periphery in Fuyun County, Xinjiang. 1–147(in Chinese).

  • Chen, T., & Guestrin, C. (2016). Xgboost: a scalable tree boosting system. In: Proceedings 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. http://dx.doi.org/https://doi.org/10.1145/2939672.2939785

  • Chen, W., Xue, G. Q., Zhou, N. N., Tang, D. M., Hou, D. Y., He, Y. M., Lei, K. X., Chen, K., & Li, H. (2019). Delineating ore-forming rock using a frequency domain controlled-source electromagnetic method. Ore Geology Reviews, 115, 103167.

    Google Scholar 

  • Chen, Y., & Wu, W. (2017). Mapping mineral prospectivity by using one-class support vector machine to identify multivariate geological anomalies from digital geological survey data. Australian Journal of Earth Sciences, 64, 639–651.

    Google Scholar 

  • Chen, Y. Q., Zhang, L. N., & Zhao, B. B. (2016). Application of Bi-dimensional empirical mode decomposition (BEMD) modeling for extracting gravity anomaly indicating the ore-controlling geological architectures and granites in the Gejiu tin-copper polymetallic ore field, southwestern China. Ore Geology Reviews, 88, 832–840.

    Google Scholar 

  • Claesen, M., Smet, F. D., Suykens, J. A. K., & Moor, B. D. (2015). A robust ensemble approach to learn from positive and unlabeled data using SVM base models. Neurocomputing, 160, 73–84.

    Google Scholar 

  • Dai, T. G., Yin, X. L., & Zhang, D. X. (2013). Diagenetic and metallogenic model of Kalatonke Cu-Ni deposit. The Chinese Journal of Nonferrous Metals, 23(9), 2567–2573. (in Chinese with English abstract).

    Google Scholar 

  • Ding, J. H., Xing, S. W., Xiao, K. Y., Ma, Y. B., Zhang, T. T., & Liu, Y. L. (2016). Geological characteristics and resource potential analysis of the Altay-north Junggar Chromite-Cu-Au-Pb-Zn-Ni metallogenic belt. Acta Geologica Sinica, 90(7), 1334–1352. (in Chinese with English abstract).

    Google Scholar 

  • Duan, J., Qian, Z. Z., Feng, Y. Q., Li, C. S., Ripley, E. M., Xu, G., & Jiao, J. G. (2017). Compositional variations of several Early Permian magmatic sulfide deposits in the Kalatongke district, southern Altai, western China: With genetic and exploration implications. Ore Geology Reviews, 90, 1–15.

    Google Scholar 

  • Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27, 861–874.

    Google Scholar 

  • Gao, J. F., Zhou, M. F., Lightfoot, P. C., & Qu, W. J. (2012). Heterogeneous Os isotope compositions in the Kalatongke sulfide deposit, NW China: The role of crustal contamination. Mineralium Deposita, 47, 731–738. https://doi.org/10.1007/s00126-012-0414-7

    Article  Google Scholar 

  • Gray, J. F., Goldfarb, R. J., Detra, D. E., & Slaughter, K. E. (1991). Geochemistry and exploration criteria for epithermal cinnabar and stibnite vein deposits in the Kuskokwim River region, southwestern Alaska. Journal of Geochemical Exploration, 41(3), 363–386.

    Google Scholar 

  • Han, C. M., Xiao, W. J., Su, B. X., Zhang, X. H., Wan, B., Song, D. F., Zhang, Z. Y., Zhang, J. E., Wang, Z. M., & Xie, M. C. (2019). Geological characteristics and metallogenic setting of representative magmatic Cu-Ni deposits in the Tianshan-Xingmeng orogenic belt, central Asia. Acta Geologica Sinica (English Edition), 93(5), 1205–1218.

    Google Scholar 

  • Han, C. M., Xiao, W. J., Zhao, G. C., Qu, W. J., Mao, Q. G., & Du, A. D. (2006). Re-Os isotopic analysis of the Kalatongke Cu-Ni Sulfide Deposit, Northern Xinjiang, NW China, and its geological implication. Acta Petrologica Sinica, 22(1), 163–170. (in Chinese with English abstract).

    Google Scholar 

  • Han, S. L., Wang, S., Tang, Z. P., Tan, K. X., Duan, X. Z., He, H. Y., Feng, Z. G., & Xie, Y. S. (2020). Integrated geophysical exploration of the coupling of a concealed rock body and metallogenic structures—Ag-Pb-Zn mining area case study in Jilinbaolige, Inner Mongolia. China. Journal of Applied Geophysics, 178, 104048.

    Google Scholar 

  • Hariharan, S., Tirodkar, S., Porwal, A., Bhattacharya, A., & Joly, A. (2017). Random forest-based prospectivity modelling of greenfield terrains using sparse deposit data: An example from the Tanami Region Western Australia. Natural Resources Research, 26(4), 489–507.

    Google Scholar 

  • Huang, N. E., Shen, Z., Long, S. R., Wu, M. L. C., Shih, H. H., Zheng, Q. N., Yen, N. C., Tung, C. C., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971), 903–995.

    Google Scholar 

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

    Google Scholar 

  • Jia, Z. Y., Zhang, M. J., Tang, Z. L., Li, W. Y., Ren, L. Y., & Hu, P. Q. (2009). Petrogenesis of Kalatongke Cu-Ni sulfide deposit in Xinjiang. Mineral Deposits, 28(5), 673–686. (in Chinese with English abstract).

    Google Scholar 

  • Jiang, C. Y., Xia, M. Z., Qian, Z. Z., Yu, X., Lu, R. H., & Guo, F. F. (2009). Petrogenesis of Kalatongke mafic rock intrusions. Xinjiang. Acta Petrologica Sinica, 25(4), 749–764. (in Chinese with English abstract).

    Google Scholar 

  • Jiao, J. G., Wang, Y., Qian, Z. Z., Wang, B., Lu, H., Liu, H., & Zheng, P. P. (2014). Tentative discussion on rock forming and ore forming mechanism of Kalatongke Cu-Ni sulfide deposit and chronology of Kalatongke Y9 intrusion. Mineral Deposits, 33(4), 675–688.

    Google Scholar 

  • Kaboutari, A., Bagherzadeh, J., & Kheradmand, F. (2014). An evaluation of two-step techniques for positive-unlabeled learning in text classification. International Journal of Computer Applications Technology and Research, 3(9), 592–594.

    Google Scholar 

  • Kang, Z., Qin, K. Z., Mao, Y. J., Tang, D. M., & Yao, Z. S. (2020). The formation of a magmatic Cu-Ni sulfide deposit in mafic intrusions at the Kalatongke, NW China: Insights from amphibole mineralogy and composition. Lithos, 352–353, 105317.

    Google Scholar 

  • Lee, W. S., & Liu, B. (2003). Learning with positive and unlabeled examples using weighted logistic regression. Proceedings of the Twentieth International Conference on Machine Learning, 3, 448–455.

    Google Scholar 

  • Leite, E. P., & de Souza Filho, C. R. (2009). Artificial neural networks applied to mineral potential mapping for copper-gold mineralizations in the Carajás Mineral Province Brazil. Geophysical Prospecting, 57(6), 1049–1065.

    Google Scholar 

  • Li, C. S., Zhang, M. J., Fu, P. E., Qian, Z. Z., Hu, P. Q., & Ripley, E. M. (2012). The Kalatongke magmatic Ni–Cu deposits in the Central Asian Orogenic Belt, NW China: Product of slab window magmatism? Mineralium Deposita, 47(1), 51–67.

    Google Scholar 

  • Li, G. Z., Lei, W. Y., & Zhang, Z. W. (2011). A summary of magma evolution and sulfide mineralization of the Kalatongke Cu-Ni sulfide deposit, Xinjiang. China. China Mining Magazine, 20(10), 52–56. (in Chinese with English abstract).

    Google Scholar 

  • Li, R. X., Wang, G. W., & Carranza, E. J. M. (2016). GeoCube: A 3D mineral resources quantitative prediction and assessment system. Computers & Geosciences, 89, 161–173.

    Google Scholar 

  • Li, S., Chen, J. P., 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. https://doi.org/10.1007/s12583-020-1365-z

    Article  Google Scholar 

  • Li, W. K., Guo, Q. H., & Elkan, C. (2010). A positive and unlabeled learning algorithm for one-class classification of remote-sensing data. IEEE Transactions on Geoscience and Remote Sensing, 49, 717–725.

    Google Scholar 

  • Liu, B., Lee, W. S., Yu, P. S., & Li, X. (2002). Partially supervised classification of text documents. ICML, 2, 387–394.

    Google Scholar 

  • Liu, J. X., Zhou, Y. J., Xu, M. C., Wang, X. J., Zhang, B. W., Li, P., Zhang, K., Wang, K., Gao, J. H., Wang, G. K., Chai, M. T., & Rong, L. X. (2017). The application of seismic exploration technology in the Kalatongke orefield. Geophysical & Geochemical Exploration, 41(3), 437–444. https://doi.org/10.11720/wtyht.2017.3.07

    Article  Google Scholar 

  • Mao, J. W., Pirajno, F., Zhang, Z. H., Chai, F. M., Yang, J. M., Wu, H., Chen, S. P., Cheng, S. L., & Zhang, C. Q. (2006). Late Variscan Post-collisional Cu-Ni Sulfide deposits in East Tianshan and Altay in China Principal characteristics and possible relationship with Mantle Plume. Acta Geologica Sinica, 80(7), 925–942. (in Chinese with English abstract).

    Google Scholar 

  • Mao, X. C., Ren, J., Liu, Z. K., Chen, J., Tang, L., Deng, H., Bayless, R. C., Yang, M., Wang, M. J., & Liu, C. M. (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.

    Google Scholar 

  • Mordelet, F., & Vert, J. P. (2014). A bagging SVM to learn from positive and unlabeled examples. Pattern Recognition Letters, 37, 201–209.

    Google Scholar 

  • Nunes, J. C., Bouaoune, Y., Delechelle, E., Niang, O., & Bunel, P. (2003). Image analysis by bi-dimensional empirical mode decomposition. Image and Vision Computing, 21, 1019–1026.

    Google Scholar 

  • Nunes, J. C., Guyot, S., & Delechelle, E. (2005). Texture analysis based on local analysis of the bidimensional empirical mode decomposition. Machine Vision and Applications, 16, 177–188.

    Google Scholar 

  • Nykänen, V., Lahti, I., Niiranen, T., & Korhonen, K. (2015). Receiver operating characteristics (ROC) as validation tool for prospectivity models—a magmatic Ni–Cu case study from the Central Lapland Greenstone Belt, northern Finland. Ore Geology Reviews, 71, 853–860.

    Google Scholar 

  • Olaniyan, O., Smith, R. S., & Lafrance, B. (2015). Regional 3D geophysical investigation of the Sudbury Structure. Interpretation, 3(2), SL63–SL81. https://doi.org/10.1190/int-2014-0200.1

    Article  Google Scholar 

  • Qian, Z. Z., Duan, J., Li, C. S., Xu, G., Feng, Y. Q., & Ren, M. (2016). Paleozoic mafic-intermediate intrusions (320–287Ma) in the Kalatongke area, southern Altai, NW China: Products of protracted magmatism in a convergent tectonic setting. Journal of Asian Earth Sciences, 159, 294–307.

    Google Scholar 

  • Qian, Z. Z., Wang, J. Z., Jiang, C. Y., Jiao, J. G., Yan, H. Q., He, K., & Sun, T. (2009). Geochemistry characters of platinum-group elements and its significances on the process of mineralization in the Kalatongke Cu-Ni sulfide deposit, Xinjiang. China. Acta Petrogica Sinica, 25(4), 832–844. (in Chinese with English abstract).

    Google Scholar 

  • Qin, K. Z., Tian, Y., Yao, Z. S., Wang, Y., Mao, Y. J., Wang, B., Xue, S. C., Tang, D. M., & Kang, Z. (2013). Metallogenic conditions, magma conduit and exploration potential of the Kalatongke Cu-Ni orefield in Northern Xinjiang. Geology In China, 41(3), 912–935.

    Google Scholar 

  • Ran, H. Y., & Xiao, S. H. (1994). Trace element abundances and tectonic environment of the host intrusion of Kalatongke Cu-Ni deposit. Geochimica (Beijing), 23(4), 392–401. (in Chinese with English abstract).

    Google Scholar 

  • Rodriguez-Galiano, V., Chica-Olmo, M., & Chica-Rivas, M. (2014). Predictive modelling of gold potential with the integration of multisource information based on random forest: A case study on the Rodalquilar area, Southern Spain. International Journal of Geographical Information Science, 28, 1336–1354.

    Google Scholar 

  • Song, X. Y., & Li, X. R. (2009). Geochemistry of the Kalatongke Ni–Cu–(PGE) sulfide deposit, NW China: Implications for the formation of magmatic sulfide mineralization in a postcollisional environment. Mineralium Deposita, 44(3), 303–327.

    Google Scholar 

  • Song, X. Y., Xiao, J. F., Zhu, D., Zhu, W. G., & Chen, L. M. (2010). New insights on the formation of magmatic sulfide deposits in magma conduit system. Earth Science Frontiers, 17(1), 153–163. (in Chinese with English abstract).

    Google Scholar 

  • Tang, Z. L., Qian, Z. Z., & Jiang, C. Y. (2006). Magmatic Ni-Cu-PGE Sulphide Deposits and Metallogenic Prognosis in China (pp. 1–295). Beijing: Geological Publishing House. (in Chinese with English abstract).

    Google Scholar 

  • Tang, D. M., Qin, K. Z., Su, B. X., Mao, Y. J., Evans, N. J., Niu, Y. J., & Kang, Z. (2020). Sulfur and copper isotopic signatures of chalcopyrite at Kalatongke and Baishiquan: Insights into the origin of magmatic Ni-Cu sulfide deposits. Geochimica et Cosmochimica Acta, 275, 209–228.

    Google Scholar 

  • Tang, Z. L., Yan, H. Q., Jiao, J. G., & Pan, Z. X. (2007). Regional metallogenic controls of small intrusion hosted Ni-Cu(PGE) ore deposits in China. Earth Science Frontiers, 14(5), 092–103. (in Chinese with English abstract).

    Google Scholar 

  • Tao, G. S., Wang, G. W., & Zhang, Z. Q. (2018). Extraction of mineralization-related anomalies from gravity and magnetic potential fields for mineral exploration targeting: Tongling Cu (-Au) district. China. Natural Resources Research, 28(2), 461–486.

    Google Scholar 

  • Wang, F. T., Ma, T. L., Liu, G. H., & Li, Y. G. (1992). Metallogeny and Prospecting Model of the Kalatongke Cu-Ni-Au Ore Belt in Xinjiang (pp. 1–278). Beijing: Geological Publishing House. (in Chinese with English abstract).

    Google Scholar 

  • Wang, G. W., Ma, Z. B., Li, R. X., Song, Y. W., Qu, J. N., Zhang, S. T., Yan, C. H., & Han, J. W. (2017). Integration of multi-source and multi-scale datasets for 3D structural modeling for subsurface exploration targeting, Luanchuan Mo-polymetallic district, China. Journal of Applied Geophysics, 139, 269–290.

    Google Scholar 

  • Wang, G. W., Zhang, S. T., Yan, C. H., Song, Y. W., Sun, Y., Li, D., & Xu, F. M. (2011). Mineral potential targeting and resource assessment based on 3D geological modeling in Luanchuan region, China. Computes & Geosciences, 37, 1976–1988.

    Google Scholar 

  • Wang, G. W., Zhang, Z. Q., Li, R. X., Li, J. J., Sha, D. M., Zeng, Q. D., Pang, Z. S., Li, D. P., & Huang, L. L. (2021). Resource prediction and assessment based on 3D/4D big data modeling and deep integration in key ore districts of North China. Science China Earth Sciences, 64(9), 1590–1606.

    Google Scholar 

  • Wang, G. W., Zhu, Y. Y., Zhang, S. T., Yan, C. H., Song, Y. W., Ma, Z. B., Hong, D. M., & Chen, T. Z. (2012). 3D geological modeling based on gravitational and magnetic data inversion in the Luanchuan ore region, Henan Province, China. Journal of Applied Geophysics, 80, 1–11. https://doi.org/10.1016/j.jappgeo.2012.01.006

    Article  Google Scholar 

  • Wang, J., Zuo, R. G., & Xiong, Y. H. (2020). Mapping mineral prospectivity via semi-supervised random forest. Natural Resources Research, 29, 189–202.

    Google Scholar 

  • Wu, B. Y., Qiu, W. R., Jia, J. X., & Liu, N. H. (2020). Landslide susceptibility modeling using bagging-based positive-unlabeled learning. IEEE Geoscience and Remote Sensing Letters, 18(5), 766–770.

    Google Scholar 

  • Xia, Y. F., Liu, C. Z., Li, Y. Y., & Liu, N. N. (2017). A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Systems With Applications, 78, 225–241.

    Google Scholar 

  • Xiao, W. J., Zhang, L. C., Qin, K. Z., Sun, S., & Li, J. L. (2004). Paleozoic accretionary and collisional tectonics of the eastern Tianshan (China): Implications for the continental growth of Central Asia. American Journal of Science, 304(4), 370–395.

    Google Scholar 

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

    Google Scholar 

  • Xu, G. M., Cheng, Q. M., Zuo, R. G., & Wang, H. C. (2015). Application of improved bi-dimensional empirical mode decomposition (BEMD) based on Perona-Malik to identify copper anomaly association in the southwestern Fujian (China). Journal of Geochemical Exploration, 164, 65–74.

    Google Scholar 

  • Yang, N., Zhang, Z. K., Yang, J. H., Hong, Z. L., & Shi, J. (2021). A Convolutional neural network of GoogLeNet applied in mineral prospectivity prediction based on multi-source geoinformation. Natural Resources Research, 30(6), 3905–3923.

    Google Scholar 

  • Yin, B. J., Zuo, R. G., & Xiong, Y. H. (2021). Mineral prospectivity mapping via gated recurrent unit model. Natural Resources Research, 31, 2065–2079.

    Google Scholar 

  • Yousefi, M., & Carranza, E. J. M. (2015). Prediction-area (P-A) plot and C-A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Computers & Geosciences, 79, 69–81.

    Google Scholar 

  • Yousefi, M., & Carranza, E. J. M. (2016). Data-driven index overlay and Boolean logic mineral prospectivity modeling in greenfields exploration. Natural Resources Research, 25(1), 3–18.

    Google Scholar 

  • Zhang, Z. H., Chai, F. M., Du, A. D., Zhang, Z. C., Yan, S. H., Yang, J. M., Qu, W. J., & Wang, Z. L. (2005). Re-Os dating and ore-forming material tracing of the Karatungk Cu-Ni sulfide deposit in northern Xinjiang. Acta Petrologica Et Mineralogica, 24(4), 285–293. (in Chinese with English abstract).

    Google Scholar 

  • Zhang, Z. W., Li, W. Y., Zhang, J. W., Wang, Y. L., & You, M. X. (2015). Geological distribution characteristics and metallogenic background of magmatic Ni-Cu sulfide deposits in the north part of Xinjiang. Northwestern Geology, 48(3), 335–354.

    Google Scholar 

  • Zhang, Z. C., Mao, J. W., Chai, F. M., Yan, S. H., Chen, B. L., & Pirajno, F. (2009). Geochemistry of the Permian Kalatongke Mafic Intrusions, Northern Xinjiang, Northwest China: Implications for the Genesis of Magmatic Ni-Cu Sulfide deposits. Economic Geology, 104, 185–203.

    Google Scholar 

  • Zhang, Z. Q., Wang, G. W., Carranza, E. J. M., Fan, J. J., Liu, X. X., Zhang, X., Dong, Y. L., Chang, X. P., & Sha, D. (2022). An integrated framework for data-driven mineral prospectivity mapping using bagging-based positive-unlabeled learning and bayesian cost-sensitive logistic regression. Natural Resources Research, 31, 3041–3060.

    Google Scholar 

  • Zhang, Z. Q., Wang, G. W., Carranza, E. J. M., Zhang, J. J., Tao, G. S., Zeng, Q. D., Sha, D. M., Li, D. T., Shen, J. F., & Pang, Z. (2019b). Metallogenic model of the Wulong gold district, China, and associated assessment of exploration criteria based on multi-scale geoscience datasets. Ore Geology Reviews, 114, 103138.

    Google Scholar 

  • Zhang, Z. Q., Wang, G. W., Liu, C., Cheng, L. Z., & Sha, D. M. (2021). Bagging-based positive-unlabeled learning algorithm with Bayesian hyperparameter optimization for three-dimensional mineral potential mapping. Computers & Geosciences, 154, 104817.

    Google Scholar 

  • Zhang, Z. Q., Wang, G. W., Ma, Z. B., Carranza, E. J. M., Jia, W. J., Du, J. G., Tao, G. S., & Deng, Z. P. (2019a). Batholith-stock scale exploration targeting based on multi-source geological and geophysical datasets in the Luanchuan Mo polymetallic district China. Ore Geology Reviews, 118, 103225.

    Google Scholar 

  • Zhang, Z. C., Yan, S. H., Chen, B. L., He, L. X., He, Y. S., & Zhou, G. (2003). Geochemistry of the Kalatongke basic complex in Xinjiang and its constraints on genesis of the deposit. Acta Petrologica et Mineralogica, 22(3), 217–224. (in Chinese with English abstract).

    Google Scholar 

  • Zhou, J. Y., Xu, M. C., Liu, J. X., Gao, J. H., Wang, X. J., & Zhang, B. W. (2016). Application of seismic reflection imaging in the Karatungk Cu-Ni deposit of Xinjiang. Geology and Exploration, 52(5), 910–917. (in Chinese with English abstract).

    Google Scholar 

  • Zhou, Y. M., Xi, L., & Shao, X. L. (2014). Prospecting applications of geophysical methods in G21 anomalies area around Kalatongke Copper-Nickel mine. Journal of Xinjiang University (Natural Science Edition), 31(2), 238–242. (in Chinese with English abstract).

    Google Scholar 

  • Zhu, Z. Y., & Liu, G. F. (2016). Analysis of potential field data and its application based on bidimensional empirical mode decomposition. Progress in Geophysics, 31(2), 882–892.

    Google Scholar 

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

    Google Scholar 

  • Zuo, R. G., & Wang, Z. Y. (2020). Effects of random negative training samples on mineral prospectivity mapping. Natural Resources Research, 29, 3443–3455.

    Google Scholar 

  • Zuo, R. G., Xiong, Y., Wang, J., & Carranza, E. J. M. (2019). Deep learning and its application in geochemical mapping. Earth-Science Reviews, 129, 1–14.

    Google Scholar 

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Acknowledgments

The authors gratefully acknowledge the Associate Editor Prof. Renguang Zuo and two reviewers for their useful comments to improve this paper. This research was supported by the National Key Research and Development Programs of China (Grant No. 2017YFC0601200) and 2021 Graduate Innovation Fund Project of China University of Geosciences, Beijing (Grant No. ZD2021YC008). The authors thank Prof. Kezhang Qin and Dongmei Tang (Key Laboratory of Mineral Resources, Institute of Geology and Geophysics, Chinese Academy of Sciences) for the data support and metallogenic model analysis comments.

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Authors and Affiliations

  1. School of Earth Sciences and Resources, China University of Geosciences, Beijing, 100083, China

    Meng Gao, Gongwen Wang, Wangdong Yang, Zhiqiang Zhang, Dingzhou Cai, Yunchou Xu & Shuren Yang

  2. MNR Key Laboratory for Exploration Theory & Technology of Critical Mineral Resources, China University of Geosciences, Beijing, 100083, China

    Gongwen Wang & Wangdong Yang

  3. Beijing Key Laboratory of Land and Resources Information Research and Development, Beijing, 100083, China

    Gongwen Wang

  4. Hebei Key Laboratory of Strategic Critical Mineral Resources, Hebei GEO University, Shijiazhuang, 050031, Hebei, China

    Zhiqiang Zhang

  5. Guangdong Nonferrous Metals Geological Exploration Institution, Guangdong, 510089, China

    Dingzhou Cai

Authors
  1. Meng Gao
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  2. Gongwen Wang
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  3. Wangdong Yang
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  4. Zhiqiang Zhang
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  5. Dingzhou Cai
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  6. Yunchou Xu
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  7. Shuren Yang
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Corresponding author

Correspondence to Gongwen Wang.

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Gao, M., Wang, G., Yang, W. et al. Bagging-based Positive–Unlabeled Data Learning Algorithm with Base Learners Random Forest and XGBoost for 3D Exploration Targeting in the Kalatongke District, Xinjiang, China. Nat Resour Res 32, 437–459 (2023). https://doi.org/10.1007/s11053-023-10170-y

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