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3D Mineral Prospectivity Mapping with Random Forests: A Case Study of Tongling, Anhui, China

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Abstract

In the past few decades, a variety of data-driven predictive modeling techniques has led to a dramatic advancement in mineral prospectivity mapping (MPM). The random forests (RF) algorithm, a machine learning method, has been applied successfully to data-driven MPM. However, there are two main challenges that need to be examined. Firstly, whether RF modeling can be used for the 3D MPM. The voxel (in 3D) has replaced the pixel (in 2D) to represent geological features, and so the capability of the RF model should be tested. Secondly, when we conduct regional-scale MPM, building a suitable conceptual model has a significant influence on the results; however, mineral deposit models often focus on just deposit-scale features. These two challenges were encountered in the case study in the Tongling ore cluster, which is the most representative skarn ore-concentrated area in the Middle–Lower Yangtze River Valley Metallogenic Belt in Eastern China. Thus, 3D geological models of the Tongling ore cluster were constructed from the multiple geological datasets. Then, a conceptual model was translated into 3D predictor layers. Finally, we tested and compared the MPM capabilities of the RF and compared it with weights-of-evidence (WofE) modeling. The results indicate that RF modeling not only outperforms WofE modeling in 3D MPM, but it also has capability to assess the relative importance of different predictor layers. Further testing of this method is warranted in other areas with different scales or metallogenic model to investigate fully its efficiency.

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References

  • Agterberg, F. P., & Bonham-Carter, G. F. (1990). Deriving weights of evidence from geoscience contour maps for the prediction of discrete events. In Proceedings of the 22nd APCOM symposium, Berlin, Germany (Vol. 2, pp. 381–395).

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

    Google Scholar 

  • Agterberg, F. P., 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 

  • Agterberg, F. P., Bonham-Carter, G. F., & Wright, D. F. (1990). Statistical pattern integration for mineral exploration. In Computer applications in resource estimation (pp. 1–21). Elsevier.

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

    Google Scholar 

  • Bonham-Carter, G. F. (2014). Geographic information systems for geoscientists: Modelling with GIS (Vol. 13). Amsterdam: Elsevier.

    Google Scholar 

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

    Google Scholar 

  • Breiman, L. (1984). Classification and regression trees. London: Chapman & Hall/CRC.

    Google Scholar 

  • Breiman, L. (1996). Bagging predictors. Machine Learning,24(2), 123–140.

    Google Scholar 

  • Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Boca Raton: CRC Press.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  • 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.

    Google Scholar 

  • Cargill, S. M., & Clark, A. L. (1978). Report on the activity of IGCP Project 98. Mathematical Geology,10(5), 411–417.

    Google Scholar 

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

    Google Scholar 

  • Carranza, E. J. M., & Laborte, A. G. (2015b). 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 

  • 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.

    Google Scholar 

  • Chang, Y. F., Liu, X. P., & Wu, Y.-C. (1991). The copper–iron belt of the lower and middle reaches of the Changjiang River (pp. 1–56). Beijing: Geological Publishing House.

    Google Scholar 

  • Chen, J., Lu, P., Wu, W., Zhao, J., & Hu, Q. (2007). A 3-D prediction method for blind orebody based on 3-D visualization model and its application. Earth Science Frontiers,14(5), 54–61.

    Google Scholar 

  • Chen, J. P., & Wang, C. (2012). Three-dimensional metallogenic prediction in Yongmei Region based on digital ore deposit model. Scientific and Technological Management of Land and Resources,29, 14–20.

    Google Scholar 

  • Cheng, Q. (2016). Fractal density and singularity analysis of heat flow over ocean ridges. Scientific Reports, 6(1).

  • 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.

    Google Scholar 

  • Chung, C. F. (1978). Computer program for the logistic model to estimate the probability of occurrence of discrete events. Energie, Mines et Ressources Canada.

  • Chung, C.-J. F., & Agterberg, F. P. (1980). Regression models for estimating mineral resources from geological map data. Journal of the International Association for Mathematical Geology,12(5), 473–488.

    Google Scholar 

  • 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.

    Google Scholar 

  • Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random forests for land cover classification. Pattern Recognition Letters,27(4), 294–300.

    Google Scholar 

  • Grömping, U. (2009). Variable importance assessment in regression: Linear regression versus random forest. American Statistician,63(4), 308–319.

    Google Scholar 

  • Harris, D. P. (1968). Alaska’s base and precious metals’ resources: A probabilistic regional appraisal. University Park: Pennsylvania State University, College of Earth and Mineral Sciences.

    Google Scholar 

  • Harris, J. R., Sanborn-Barrie, M., Panagapko, D. A., Skulski, T., & Parker, J. R. (2006). Gold prospectivity maps of the Red Lake greenstone belt: Application of GIS technology. Canadian Journal of Earth Sciences,43(7), 865–893.

    Google Scholar 

  • King, G., & Zeng, L. (2001). Logistic regression in rare events data. Political Analysis,9(2), 137–163.

    Google Scholar 

  • Li, N., Bagas, L., Li, X., Xiao, K., Li, Y., Ying, L., et al. (2016). An improved buffer analysis technique for model-based 3D mineral potential mapping and its application. Ore Geology Reviews,76, 94–107.

    Google Scholar 

  • Li, W. (1989). On the Yangtze type copper ore deposits and their origin. Bulletin of the Nanjing Institute Geology and Mineral Resources, Chinese Academy Geological Sciences,10(2), 1–14.

    Google Scholar 

  • Li, X., Yuan, F., Zhang, M., Jia, C., Jowitt, S. M., Ord, A., et al. (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.

    Google Scholar 

  • Li, X., Yuan, F., Zhang, M., Jowitt, S. M., Ord, A., Zhou, T., et al. (2019). 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.

    Google Scholar 

  • Liaw, A., & Wiener, M. (2002). Classification and regression by randomforest. R News,2, 18–22.

    Google Scholar 

  • Lippitt, C. D., Rogan, J., Li, Z., Eastman, J. R., & Jones, T. G. (2008). Mapping selective logging in mixed deciduous forest: A comparison of machine learning algorithms. Photogrammetric Engineering and Remote Sensing,74(10), 1201–1211.

    Google Scholar 

  • Liu, P., Mao, J., Yao, W., Wang, X., Jia, L., & Yang, H. (2017). Petrogenesis of the mafic microgranular enclaves (MMEs) and their host granodiorites from the Zijinshan intrusion along the Middle-Lower Yangtze River Valley: Implications for geod … Lithos Petrogenesis of the ma fi c microgranular enclaves (MMEs). Lithos,288–289(September), 1–19.

    Google Scholar 

  • Lü, Q., Qi, G., & Yan, J. (2012). 3D geologic model of Shizishan ore field constrained by gravity and magnetic interactive modeling: A case history. Geophysics,78(1), B25–B35.

    Google Scholar 

  • Lü, Q., Qi, G., & Yan, J. (2013). 3D geologic model of Shizishan ore field constrained by gravity and magnetic interactive modeling: A case history. Geophysics,78(1), 25–35.

    Google Scholar 

  • Malehmir, A., Thunehed, H., & Tryggvason, A. (2009). The Paleoproterozoic Kristineberg mining area, northern Sweden: Results from integrated 3D geophysical and geologic modeling, and implications for targeting ore deposits. Geophysics,74(1), B9.

    Google Scholar 

  • Mao, J., Xie, G., Duan, C., Pirajno, F., Ishiyama, D., & Chen, Y. (2011). A tectono-genetic 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.

    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.

    Google Scholar 

  • Mauriello, P., & Patella, D. (1999). Principles of probability tomography for natural-source electromagnetic induction fields. Geophysics,64(5), 1403–1417.

    Google Scholar 

  • 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.

    Google Scholar 

  • Nielsen, S. H. H., 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.

    Google Scholar 

  • Oh, H.-J., & Lee, S. (2010). Application of artificial neural network for gold–silver deposits potential mapping: A case study of Korea. Natural Resources Research,19(2), 103–124.

    Google Scholar 

  • Pan, Y., & Dong, P. (1999). The lower Changjiang (Yangzi/Yangtze River) metallogenic belt, east central China: Intrusion- and wall rock-hosted Cu–Fe–Au, Mo, Zn, Pb, Ag deposits. Ore Geology Reviews,15(4), 177–242.

    Google Scholar 

  • Parker, J. A., Kenyon, R. V., & Troxel, D. E. (1983). Comparison of interpolating methods for image resampling. IEEE Transactions on Medical Imaging,2(1), 31–39.

    Google Scholar 

  • 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.

    Google Scholar 

  • Porwal, A., Carranza, E. J. M., & Hale, M. (2001). Extended weights-of-evidence modelling for predictive mapping of base metal deposit potential in Aravalli Province, western India. Exploration and Mining Geology,10(4), 273–287.

    Google Scholar 

  • 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.

    Google Scholar 

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

    Google Scholar 

  • Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann.

    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.

    Google Scholar 

  • Rodriguez-Galiano, V. F., 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(7), 1336–1354.

    Google Scholar 

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

    Google Scholar 

  • Shi, R., & Chen, J. (2015). On the massive metal accumulation on the eastern margin of the North China Craton and the prospecting evaluation—A case study of Jiaojia gold concentration belt. Geosciences Journal,19(4), 599–612.

    Google Scholar 

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

    Google Scholar 

  • Story, M., & Congalton, R. G. (1986). Remote sensing brief accuracy assessment: A user’s perspective. Photogrammetric Engineering and Remote Sensing,52(3), 397–399.

    Google Scholar 

  • Wang, G., & Huang, L. (2012). 3D geological modeling for mineral resource assessment of the Tongshan Cu deposit, Heilongjiang Province, China. Geoscience Frontiers,3(4), 483–491.

    Google Scholar 

  • Wang, G., Zhu, Y., Zhang, S., Yan, C., Song, Y., Ma, Z., et al. (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.

    Google Scholar 

  • Wang, J., Zuo, R., & Xiong, Y. (2019). Mapping mineral prospectivity via semi-supervised random forest. Natural Resources Research, 1–14 (accepted).

  • 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, 611–632.

    Google Scholar 

  • Xu, X., Fan, Z., He, J., Liu, X., Liu, X., Xie, Q., et al. (2014). Metallogenic model for the copper-gold-polymetallic deposits in Shizishan ore-field, Tongling, Anhui Province. Acta Petrologica Sinica,30(4), 1054–1074.

    Google Scholar 

  • Yuan, F., Li, X., Zhang, M., Jowitt, S. M., Jia, C., Zheng, T., et al. (2014). Three-dimensional weights of evidence-based prospectivity modeling: A case study of the Baixiangshan mining area, Ningwu Basin, Middle and Lower Yangtze Metallogenic Belt, China. Journal of Geochemical Exploration,145, 82–97.

    Google Scholar 

  • Yusheng, Z., Shuzhen, Y., Xinduo, L., Fuquan, J., Xunruo, Z., Tianfeng, W., et al. (1992). Metallogenic regularity of iron and copper deposits in the Middle and Lower Valley of the Yangtze River. Mineral Deposits,11(1), 1–12.

    Google Scholar 

  • Zhai, Y. S., Yao, S. Z., Lin, X. D., Zhou, X. N., Wan, T. F., Jin, F. Q., et al. (1992). Fe–Cu–Au Metallogeny of the Middle-Lower Changjiang Region (p. 235). Beijing: Geological.

    Google Scholar 

  • Zhang, S., Xiao, K., Carranza, E. J. M., & Yang, F. (2019). Maximum entropy and random forest modeling of mineral potential: Analysis of gold prospectivity in the Hezuo–Meiwu district, west Qinling Orogen, China. Natural Resources Research,28(3), 645–664.

    Google Scholar 

  • Zhang, L., Zhou, T., Yuan, F., Fan, Y., & Cooke, D. R. (2011). Petrogenetic–metallogenetic setting and temporal–spatial framework of the Yueshan district, Anhui Province, east-central China. International Geology Review,53(5–6), 542–561.

    Google Scholar 

  • Zhang, Z. J., Zuo, R. G., & Xiong, Y. H. (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.

    Google Scholar 

  • Zhao, P., Chen, J., & Zhang, S. (2003). The new development of “three components” quantitative mineral prediction. Earth Science Frontiers,10(2), 455–464.

    Google Scholar 

  • Zhou, T. F., Wang, S. W., Yuan, F., Fan, Y., Zhang, D. Y., Chang, Y. F., et al. (2016). Magmatism and related mineralization of the intracontinental porphyry deposits in the Middle-Lower Yangtze River Valley Metallogenic Belt. Yanshi Xuebao/Acta Petrologica Sinica,32(2), 271–288.

    Google Scholar 

  • Zuo, R. (2017). Machine learning of mineralization-related geochemical anomalies: A review of potential methods. Natural Resources Research,26(4), 457–464.

    Google Scholar 

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

    Google Scholar 

  • Zuo, R., Cheng, Q., & Agterberg, F. P. (2009). Application of a hybrid method combining multilevel fuzzy comprehensive evaluation with asymmetric fuzzy relation analysis to mapping prospectivity. Ore Geology Reviews,35(1), 101–108.

    Google Scholar 

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Acknowledgments

This study was financially supported by Project Nos. 2017YFC0601501 and 2017YFC0601502 from the National Key Research and Development Program of China, Project Nos. 1212010733806 and 1212011120140 from China National Mineral Resources Assessment Initiative.

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Authors and Affiliations

  1. MNR Key Laboratory of Metallogeny and Mineral Resource Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing, 100037, China

    Jie Xiang & Keyan Xiao

  2. School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Westville Campus, Durban, South Africa

    Emmanuel John M. Carranza

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

    Jianping Chen & Shi Li

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  1. Jie Xiang
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Correspondence to Jie Xiang or Keyan Xiao.

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Xiang, J., Xiao, K., Carranza, E.J.M. et al. 3D Mineral Prospectivity Mapping with Random Forests: A Case Study of Tongling, Anhui, China. Nat Resour Res 29, 395–414 (2020). https://doi.org/10.1007/s11053-019-09578-2

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