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Mapping Mineral Prospectivity via Semi-supervised Random Forest

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Abstract

The majority of machine learning algorithms that have been applied in data-driven predictive mapping of mineral prospectivity require a sufficient number of training samples (known mineral deposits) to obtain results with high performance and reliability. Semi-supervised learning can take advantage of the huge amount of unlabeled data to benefit the supervised learning tasks and hence provide a suitable scheme for mapping mineral prospectivity in cases where only few known mineral deposits are available. Semi-supervised random forest was used in this study to map mineral prospectivity in the southwestern Fujian metallogenic belt of China, where there is still excellent potential for mineral exploration due to the large proportion of areas covered by forest. The findings obtained from the current study include: (1) semi-supervised learning can make use of both the labeled and unlabeled samples to help improve the performance of mapping mineral prospectivity; (2) multi-dimensional scaling can be used to explore the clustering structure within the samples, which provides a tool to validate the usability of semi-supervised learning algorithms. In addition, the prospectivity map obtained in this study can be used to guide further mineral exploration in the southwestern Fujian of China.

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Modified after Lin (2011)

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References

  1. Abedi, M., Norouzi, G. H., & Bahroudi, A. (2012). Support vector machine for multi-classification of mineral prospectivity areas. Computers & Geosciences,46, 272–283.

  2. Abedi, M., Norouzi, G. H., & Torabi, S. A. (2013). Clustering of mineral prospectivity area as an unsupervised classification approach to explore copper deposit. Arabian Journal of Geosciences,6(10), 3601–3613.

  3. Agterberg, F. P. (1989). Computer programs for mineral exploration. Science,245(4913), 76–81.

  4. Agterberg, F. P. (1990). Combining indicator patterns for mineral resource evaluation. In China University of Geosciences (Ed.), Proceedings of international workshop on statistical prediction of mineral resources, Wuhan, China (Vol. 1, pp. 1–15).

  5. Agterberg, F. P., & Bonham-Carter, G. F. (1999). Logistic regression and weights of evidence modeling in mineral exploration. In Proceedings of the 28th international symposium on applications of computer in the mineral industry (APCOM), Golden, Colorado (pp. 483–490).

  6. Amini, S., Homayouni, S., & Safari, A. (2014). Semi-supervised classification of hyperspectral image using random forest algorithm. In 2014 IEEE geoscience and remote sensing symposium (pp. 2866–2869).

  7. Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research,7, 2399–2434.

  8. Bishop, C. M. (2006). Pattern recognition and machine learning. Berlin: Springer.

  9. Blum, A., & Chawla, S. (2001). Learning from labeled and unlabeled data using graph mincuts. In Proceedings of the 18th international conference on machine learning (ICML), Williamston, MA (pp. 19–26).

  10. Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. In Proceedings of the 11th annual conference on computational learning theory, Madison, WI (pp. 92–100).

  11. Bonham-Carter, G. F. (1994). Geographic information systems for geoscientists: Modelling with GIS. Oxford: Pergamon Press (398 pp).

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

  13. Borg, I., & Groenen, P. (1997). Modern multidimensional scaling: Theory and applications. Springer series in statistics. New York: Springer.

  14. Breiman, L. (2001). Random forests. Machine Learning,45(1), 5–32.

  15. Brown, W. M., Gedeon, T. D., Groves, D. I., & Barnes, R. G. (2000). Artificial neural networks: A new method for mineral prospectivity mapping. Australian Journal of Earth Sciences,47(4), 757–770.

  16. Caers, J. (2011). Modeling uncertainty in the earth sciences. Hoboken: Wiley.

  17. Camps-Valls, G., Marsheva, T. V. B., & Zhou, D. (2007). Semi-supervised graph-based hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing,45(10), 3044–3054.

  18. Carranza, E. J. M. (2004). Weights of evidence modeling of mineral potential: A case study using small number of prospects, Abra, Philippines. Natural Resources Research,13(3), 173–187.

  19. Carranza, E. J. M. (2009). Geochemical anomaly and mineral prospectivity mapping in GIS (Vol. 11). Amsterdam: Elsevier.

  20. Carranza, E. J. M. (2014). Evidential belief predictive modeling of mineral prospectivity using few prospects and evidence with missing values. Natural Resources Research. https://doi.org/10.1007/s11053-0149250-z.

  21. Carranza, E. J. M. (2017). Natural resources research publications on geochemical anomaly and mineral potential mapping, and introduction to the special issue of papers in these fields. Natural Resources Research,26(4), 379–410.

  22. Carranza, E. J. M., & Hale, M. (2001). Geologically constrained fuzzy mapping of gold mineralization potential, Baguio district, Philippines. Natural Resources Research,10(2), 125–136.

  23. Carranza, E. J. M., & Hale, M. (2003). Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines. Ore Geology Reviews,22(1–2), 117–132.

  24. Carranza, E. J. M., & Laborte, A. G. (2015a). 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.

  25. Carranza, E. J. M., & Laborte, A. G. (2015b). Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm. Ore Geology Reviews,71, 777–787.

  26. Carranza, E. J. M., & Laborte, A. G. (2016). Data-driven predictive modeling of mineral prospectivity using random forests: A case study in Catanduanes Island (Philippines). Natural Resources Research,25(1), 35–50.

  27. Chapelle, O., Scholkopf, B., & Zien, A. (2006). Semi-supervised learning. Cambridge, MA.

  28. Chen, C., Dai, H., Liu, Y., & He, B. (2011). Mineral prospectivity mapping integrating multisource geology spatial data sets and logistic regression modeling. In Proceedings of IEEE international conference on spatial data mining and geographical knowledge services (ICSDM) (pp. 214–217).

  29. Cheng, Q. (2007). Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China. Ore Geology Reviews,32(1–2), 314–324.

  30. Cheng, Q. (2012). Singularity theory and methods for mapping geochemical anomalies caused by buried sources and for predicting undiscovered mineral deposits in covered areas. Journal of Geochemical Exploration,122, 55–70.

  31. Cheng, Q. (2015). BoostWofE: A new sequential weights of evidence model reducing the effect of conditional dependency. Mathematical Geosciences,47(5), 591–621.

  32. Cheng, Q., & Agterberg, F. P. (1999). Fuzzy weights of evidence method and its application in mineral potential mapping. Natural Resources Research,8(1), 27–35.

  33. Cheng, Q., Xu, Y., & Grunsky, E. (2000). Integrated spatial and spectrum method for geochemical anomaly separation. Natural Resources Research,9(1), 43–52.

  34. Fatehi, M., & Asadi, H. H. (2017a). Data integration modeling applied to drill hole planning through semi-supervised learning: A case study from the Dalli Cu–Au porphyry deposit in the central Iran. Journal of African Earth Sciences,128, 147–160.

  35. Fatehi, M., & Asadi, H. H. (2017b). Application of semi-supervised fuzzy c-means method in clustering multivariate geochemical data, a case study from the Dalli Cu–Au porphyry deposit in central Iran. Ore Geology Reviews,81, 245–255.

  36. Gao, Y., Zhang, Z., Xiong, Y., & Zuo, R. (2016). Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province, China. Ore Geology Reviews,75, 16–28.

  37. Ge, C., Han, F., Zhou, T., & Chen, D. (1981). Geological characteristics of the Makeng iron deposit of marine volcano-sedimentary origin. Acta Geoscientifica Sinica,3, 47–69. (in Chinese with English Abstract).

  38. Grandvalet, Y., & Bengio, Y. (2005). Semi-supervised learning by entropy minimization. In Advances in neural information processing systems (pp. 529–536).

  39. Granek, J., & Haber, E. (2015,). Data mining for real mining: A robust algorithm for prospectivity mapping with uncertainties. In Proceedings of the 2015 SIAM international conference on data mining (pp. 145–153). Society for Industrial and Applied Mathematics.

  40. Joachims, T. (1999). Transductive inference for text classification using support vector machines. In Proceedings of the 18th international conference on machine learning (ICML), Bled, Slovenia (pp. 200–209).

  41. Leistner, C., Saffari, A., Santner, J., & Bischof, H. (2009). Semi-supervised random forests. In 2009 IEEE 12th international conference on computer vision (pp. 506–513).

  42. Lima, L., Görnitz, N., Varella, L., Vellasco, M., Müller, K., & Nakajima, S. (2017). Porosity estimation by semi-supervised learning with sparsely available labeled samples. Computers & Geosciences,106, 33–48.

  43. Lin, D. (2011). Research on late Paleozoic–Triassic tectonic evolution and metallogenetic regularities of iron-polymetallic deposits in the southwestern Fujian province. Doctoral dissertation. Beijing: China University of Geosciences.

  44. Liu, Y., Cheng, Q., Xia, Q., & Wang, X. (2015). The use of evidential belief functions for mineral potential mapping in the Nanling belt. South China. Frontiers of Earth Science,9(2), 342–354.

  45. Mao, J., Xu, N., Hu, Q., Xing, G., & Yang, Z. (2004). The Mesozoic rock-forming and ore forming processes and tectonic environment evolution in Shanghang-Datian region, Fujian. Acta Petrologica Sinica,20, 285–296. (in Chinese with English Abstract).

  46. McCuaig, T. C., Beresford, S., & Hronsky, J. (2010). Translating the mineral systems approach into an effective exploration targeting system. Ore Geology Reviews,38(3), 128–138.

  47. McKay, G., & Harris, J. R. (2016). Comparison of the data-driven Random Forests model and a knowledge-driven method for mineral prospectivity mapping: A case study for gold deposits around the Huritz Group and Nueltin Suite, Nunavut, Canada. Natural Resources Research,25(2), 125–143.

  48. Nykänen, V., Groves, D. I., Ojala, V. J., & Gardoll, S. J. (2008). Combined conceptual/empirical prospectivity mapping for orogenic gold in the northern Fennoscandian Shield, Finland. Australian Journal of Earth Sciences,55(1), 39–59.

  49. Porwal, A., & Carranza, E. J. M. (2015). Introduction to the special issue: GIS-based mineral potential modelling and geological data analyses for mineral exploration. Ore Geology Reviews,71, 477–483.

  50. Porwal, A., Carranza, E. J. M., & Hale, M. (2006a). A hybrid fuzzy weights-of-evidence model for mineral potential mapping. Natural Resources Research,15(1), 1–14.

  51. Porwal, A., Carranza, E. J. M., & Hale, M. (2006b). Bayesian network classifiers for mineral potential mapping. Computers & Geosciences,32(1), 1–16.

  52. 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.

  53. Rose, K. (1998). Deterministic annealing for clustering, compression, classification, regression, and related optimization problems. Proceedings of the IEEE,86(11), 2210–2239.

  54. Saffari, A., Grabner, H., & Bischof, H. (2008). Serboost: Semi-supervised boosting with expectation regularization. In European conference on computer vision (ICCV), Berlin, Heidelberg (pp. 588–601).

  55. Shahshahani, B. M., & Landgrebe, D. A. (1994). The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon. IEEE Transactions on Geoscience and Remote Sensing,32(5), 1087–1095.

  56. Shu, L., Faure, M., Yu, J., & Jahn, B. (2011). Geochronological and geochemical features of the Cathaysia block (South China): New evidence for the Neoproterozoic breakup of Rodinia. Precambrian Research,187(3–4), 263–276.

  57. Wang, H., Cheng, Q., & Zuo, R. (2015). Spatial characteristics of geochemical patterns related to Fe mineralization in the southwestern Fujian province (China). Journal of Geochemical Exploration,148, 259–269.

  58. Wang, H., & Zuo, R. (2015). A comparative study of trend surface analysis and spectrum-area multifractal model to identify geochemical anomalies. Journal of Geochemical Exploration,155, 84–90.

  59. Wang, J., & Zuo, R. (2019). Recognizing geochemical anomalies via stochastic simulation-based local singularity analysis. Journal of Geochemical Exploration,198, 29–40.

  60. Wang, Z., Dong, Y., & Zuo, R. (2019a). Mapping geochemical anomalies related to Fe-polymetallic mineralization using the maximum margin metric learning method. Ore Geology Reviews,107, 258–265.

  61. Wang, Z., Zuo, R., & Dong, Y. (2019b). Mapping geochemical anomalies through integrating random forest and metric learning methods. Natural Resources Research,12, 3. https://doi.org/10.1007/s11053-019-09471-y.

  62. Wickelmaier, F. (2003). An introduction to MDS. Sound Quality Research Unit, Aalborg University, Denmark,46(5), 1–26.

  63. Xie, X., Mu, X., & Ren, T. (1997). Geochemical mapping in China. Journal of Geochemical Exploration,60(1), 99–113.

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

  65. Xiong, Y., & Zuo, R. (2017). Effects of misclassification costs on mapping mineral prospectivity. Ore Geology Reviews,82, 1–9.

  66. Xiong, Y., & Zuo, R. (2018). GIS-based rare events logistic regression for mineral prospectivity mapping. Computers & Geosciences,111, 18–25.

  67. Yang, Z., Zhang, D., Feng, C., She, H., & Li, J. (2008). SHRIMP zircon U-Pb dating of quartz porphyry from Zhongjia Tin-polymetallic deposit in Longyan area, Fujian province, and its geological significance. Miner Deposit,27, 329–335. (in Chinese with English abstract).

  68. Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. In 33rd Annual meeting of the association for computational linguistics.

  69. Young, F. W. (1987). Multidimensional scaling: History, theory, and applications. New Jersey: Lawrence Erlbaum Associates.

  70. Yousefi, M., & Carranza, E. J. M. (2015a). Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping. Computers & Geosciences,74, 97–109.

  71. Yousefi, M., & Carranza, E. J. M. (2015b). 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.

  72. 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.

  73. Yousefi, M., Carranza, E. J. M., & Kamkar-Rouhani, A. (2013). Weighted drainage catchment basin mapping of stream sediment geochemical anomalies for mineral potential mapping. Journal of Geochemical Exploration,128, 88–96.

  74. Yousefi, M., & Nykänen, V. (2016). Data-driven logistic-based weighting of geochemical and geological evidence layers in mineral prospectivity mapping. Journal of Geochemical Exploration,164, 94–106.

  75. Yu, C. (2002). Complexity of earth systems—Fundamental issues of earth sciences (I). Earth Sciences, China University of Geosciences,27, 509–519. (in Chinese with English abstract).

  76. Yuan, Y., Feng, H., Zhang, D., Di, Y., Wang, C., & Ni, J. (2013). Geochronology of Dapai iron-polymetallic deposit in Yongding city, Fujian province and its geological significance. Acta Mineral Sin,33, 73–75.

  77. Zhang, C., Su, H., Yu, M., & Hu, C. (2012). Zircon U–Pb age and Nd–Sr–Pb isotopic characteristics of Dayang-Juzhou granite in Longyan, Fujian province and its geological significance. Acta Petrol Sin,28, 225–242. (in Chinese with English abstract).

  78. Zhang, D., Ren, N., & Hou, X. (2018a). An improved logistic regression model based on a spatially weighted technique (ILRBSWT v1. 0) and its application to mineral prospectivity mapping. Geoscientific Model Development,11, 2525–2539.

  79. Zhang, Z., Cheng, Q., Yang, J., & Hu, X. (2018b). Characterization and origin of granites from the Luoyang Fe deposit, southwestern Fujian Province, South China. Journal of Geochemical Exploration,184, 119–135.

  80. Zhang, Z., & Zuo, R. (2014). Sr–Nd–Pb isotope systematics of magnetite: implications for the genesis of Makeng Fe deposit, southern China. Ore Geology Reviews,57, 53–60.

  81. Zhang, Z., Zuo, R., & Cheng, Q. (2015). The mineralization age of the Makeng Fe deposit, South China: Implications from U–Pb and Sm–Nd geochronology. International Journal of Earth Sciences,104(3), 663–682.

  82. Zhang, Z., Zuo, R., & Xiong, Y. (2016). A comparative study of fuzzy weights of evidence and random forests for mapping mineral prospectivity for skarn-type Fe deposits in the southwestern Fujian metallogenic belt, China. Science China Earth Sciences,59(3), 556–572.

  83. Zhong, J., Chen, Y. J., Chen, J., Qi, J. P., & Dai, M. C. (2018). Geology and fluid inclusion geochemistry of the Zijinshan high-sulfidation epithermal Cu–Au deposit, Fujian Province, SE China: Implication for deep exploration targeting. Journal of Geochemical Exploration,184, 49–65.

  84. Zhu, X. (2017). Semi-supervised learning. In C. Sammut & I. Webb (Eds.), Encyclopedia of machine learning and data mining (pp. 1142–1147).

  85. Zhu, X., & Goldberg, A. B. (2009). Introduction to semi-supervised learning. Synthesis Lectures on Artificial Intelligence and Machine Learning,3(1), 1–130.

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

  87. Zuo, R., & Xiong, Y. (2018). Big data analytics of identifying geochemical anomalies supported by machine learning methods. Natural Resources Research,27, 5–13.

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

  89. Zuo, R., Zhang, Z., Zhang, D., Carranza, E. J. M., & Wang, H. (2015). Evaluation of uncertainty in mineral prospectivity mapping due to missing evidence: A case study with skarn-type Fe deposits in Southwestern Fujian Province, China. Ore Geology Reviews,71, 502–515.

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Acknowledgments

Thanks are due to John Carranza, Editor-in-Chief for Natural Resources Research, and two anonymous reviewers for their comments and suggestions, which helped improve this study. This research benefited from the joint financial support from the Scientific Research Foundation for the Youth Teachers of Chengdu University of Technology (10912-KYQD-07280), and National Natural Science Foundation of China (No. 41772344).

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Correspondence to Renguang Zuo.

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Wang, J., Zuo, R. & Xiong, Y. Mapping Mineral Prospectivity via Semi-supervised Random Forest. Nat Resour Res 29, 189–202 (2020). https://doi.org/10.1007/s11053-019-09510-8

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Keywords

  • Mapping mineral prospectivity
  • Semi-supervised
  • Random forest
  • Southwestern Fujian metallogenic belt