Skip to main content
SpringerLink
Log in

Machine Learning-Based 3D Modeling of Mineral Prospectivity Mapping in the Anqing Orefield, Eastern China

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

Highlights

  • Actual geological data, accurate models and precise samples are critical for ore targeting.

  • The RF-based prediction model is more applicable for mapping mineral prospectivity than other algorithms in this study.

  • The determination of sample set is more important than algorithm if there is not enough field data.

Abstract

Successful delineation of high potential targets for exploration in maturely-explored orefields is still a tough challenge. A reliable prediction model achieved by integration of various ore-related geological factors and exploration information in the 3D space is an effective approach to deal with this challenge. The Anqing orefield has been intensively exploited for decades, and thereby the possible potential left must be at depth. The accumulated abundant data of exploration and research provide us a possibility for carrying out machine learning-based 3D modeling. The 3D block models of the main geological bodies, resistivity and volumetric strain field in this orefield were used as multi-resource geological data to construct prediction models by using weight-of-evidence and machine learning methods. Through performance evaluation and comparison, the following conclusions were obtained: (1) it is more scientific and reasonable to use all the geological prospecting factors concurrently for mineral prospectivity mapping (MPM) rather than use one or a part of them; (2) random forest (RF) algorithm seems capable of MPM because of its high accuracy and reliability in prediction; and (3) rational selection of training and learning samples, especially, those from actual geological objects and exploration engineering, plays a more critical role in MPM than algorithm and methods themselves. Two different RF prediction models were obtained for MPM in the east and the surrounding part of this orefield based on the outcome of geophysical prospecting. The spaces with prediction probabilities higher than 0.508 in the east part and 0.501 in the surroundings take up only 3.71% volume of the whole orefield, but contain 95.92% of the mineralized blocks. The high potential targets are most likely parts of the above spaces with high prediction probabilities that have not been drilled yet up to now.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  • Abedi, M., Noruzi, G. H., & Bahroudi, A. (2012). Support vector machine for multi-classification of mineral prospectivity areas. Computers & Geosciences, 46(3), 272–283.

    Article  Google Scholar 

  • Agterberg, F. P., Bonham-Carter, G. F., & Wright, D. F. (1990). Statistical pattern integration for mineral exploration. Computer Applications in Resource Estimation, 1–21.

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

    Article  Google Scholar 

  • Bonham-Carter, G. F. (1994). Geographic information systems for geoscientists: Modelling with GIS (p. 398). Pergamon.

    Google Scholar 

  • Bonham-Carter, G. F., Agterberg, F. P., & Wright, D. F. (1989). Weights of evidence modeling: A new approach to mapping mineral potential. Geological Survey of Canada, 89(9), 171–183.

    Google Scholar 

  • Boser, B.E., Guyon, I.M., & Vapnik, V.N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory, ACM, pp. 144–152.

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

    Article  Google Scholar 

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

    Article  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. Journal of the Geological Society of Australia, 47(4), 757–770.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Carranza, E. J. M. (2008). Geochemical anomaly and mineral prospectivity mapping in GIS. In Handbook of exploration and environmental geochemistry (Vol. 11, p. 351). Amsterdam: Elsevier.

  • Carranza, E. J. M. (2015). Data-Driven Evidential Belief Modeling of Mineral Potential Using Few Prospects and Evidence with Missing Values. Natural Resources Research, 24(3), 291–304.

    Article  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(2015), 60–70.

    Article  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), 1–16.

    Article  Google Scholar 

  • Carranza, E. J. M., Ruitenbeek, F. J. A. V., Hecker, C., Meijde, M. V. D., & Meer, F. D. V. D. (2008). Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain. International Journal of Applied Earth Observation & Geoinformation, 10(3), 387.

  • Caté, A., Schetselaar, E., Mercier-Langevin, P., & Ross, P. S. (2018). Classification of lithostratigraphic and alteration units from drillhole lithogeochemical data using machine learning: A case study from the Lalor volcanogenic massive sulphide deposit, Snow Lake, Manitoba, Canada. Journal of Geochemical Exploration, 216–228.

  • Chang, Y. F., Liu, X. P., & Wu, Y. C. (1991). The Copper–Iron Belt of the Low and Middle Reaches of the Yangtze River. Geological Publish House: Beijing, China, pp. 359. (In Chinese)

  • Chen, G. D. (1982). Polygenetic compound ore deposits and their origin in the context of regularities in crustal evolution. Geotectonica Et Metallogenia, 1, 1–27. (In Chinese).

    Google Scholar 

  • Chen, J. P., Peng, L., Wen, W., Zhao, J., & Qing, H. (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.

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Cracknell, M. J., & Reading, A. M. (2014). Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information. Computers & Geosciences, 63, 22–33.

    Article  Google Scholar 

  • Cristianini, N., & Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. https://doi.org/10.1017/CBO9780511801389:173-186.

    Article  Google Scholar 

  • Cutler, A., Cutler, D. R., & Stevens, J. R. (2012). Random forests. Ensemble machine learning (pp. 157–175). Springer.

    Chapter  Google Scholar 

  • Delong, E. R., Delong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics, 44(3), 837–845.

    Article  Google Scholar 

  • Dong, S. W., & Qiu, R. L. (1993). Tectonism and magmatism in the anqing-Yueshan Area. Geological Publishing House: Beijing, China, pp. 154. (In Chinese)

  • Eugenio, B. D., & Glass, M. (2004). The kappa statistic: A second look. Computational Linguistics, 30(1), 95–101.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Fayyad, U. M., & Irani, K. B. (1992). The attribute selection problem in decision tree generation. National Conference on Artificial Intelligence, 104–110.

  • Harris, D. V., & Pan, G. (1999). Mineral favorability mapping: a comparison of artificial neural networks, logistic regression, and discriminant analysis. Natural Resources Research, 8(2), 93–109.

    Article  Google Scholar 

  • Haykin, S. (1994). Neural Networks: A Comprehensive Foundation. Neural Networks A Comprehensive Foundation, 71–80.

  • Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832–844.

    Article  Google Scholar 

  • Hood, S., Cracknell, M., & Gazley, M. (2018). Linking protolith rocks to altered equivalents by combining unsupervised and supervised machine learning. Journal of Geochemical Exploration, 186, 270–280.

    Article  Google Scholar 

  • Hopfield, J. J. (1988). Artificial neural networks. IEEE Circuits & Devices Magazine, 4(5), 3–10.

    Article  Google Scholar 

  • Keller, J. M., Gray, M. R., & Givens, J. A. (1985). A fuzzy k-nearest neighbor algorithm. IEEE Transactions on Systems, Man, and Cybernetics, 4, 580–585.

    Article  Google Scholar 

  • Lesmeister, C. (2017). Mastering machine learning with R: Advanced prediction, algorithms, and learning methods with R 3.x. Birmingham: Packt Publishing.

  • Li, F. Y., Pan, M., & Zhu, L. (2005). Research on the Algorithm for 3D Raster Buffer-Generation. Journal of Computer-Aided Design & Computer Graphics, 17(9), 1928–1932. (In Chinese with English Abstract).

    Google Scholar 

  • Li, X. H. (2016). Using “random forest” for classification and regression. Chinese Journal of Applied Entomology, 50(04), 1190–1197. (In Chinese with English Abstract).

    Google Scholar 

  • Li, X. H., Yuan, F., Zhang, M. M., Jowitt, S. M., Ord, A., Zhou, T. F., & Dai, W. Q. (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.

    Article  Google Scholar 

  • Liu, L. M., Shu, Z. M., Zhao, C. B., Wan, C. L., Cai, A. L., & Zhao, Y. L. (2008). The controlling mechanism of ore formation due to flow-focusing dilation spaces in skarn ore deposits and its significances for deep-ore exploration: Examples from the Tongling-Anqing district. Acta Petrologica Sinica, 24, 1848–1856. (In Chinese with English Abstract).

    Google Scholar 

  • Liu, L. M., Wan, C. L., Zhao, C. B., & Zhao, Y. L. (2011). Geodynamic constraints on orebody localization in the Anqing orefield, China: Computational modeling and facilitating predictive exploration of deep deposits. Ore Geology Reviews, 43, 249–263.

    Article  Google Scholar 

  • Liu, L. M., Zhao, Y. L., & Sun, T. (2012). 3D computational shape- and cooling process-modeling of magmatic intrusion and its implication for genesis and exploration of intrusion-related ore deposits: An example from the Yueshan intrusion in Anqing, China. Tectonophysics, 526, 110–123.

    Article  Google Scholar 

  • Loomis, J. M. (1982). Analysis of tactile and visual confusion matrices. Perception and Psychophysics, 31(1), 41–52.

    Article  Google Scholar 

  • Lowd, D., & Domingos, P. (2005). Naive Bayes models for probability estimation. In Proceedings of the 22nd international conference on Machine learning, ACM, pp. 529–536

  • Mao, J. W., Xie, G. Q., Duan, C., Pirajno, F., Ishiyama, D., & Chen, Y. C. (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, 294–314.

    Article  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.

    Article  Google Scholar 

  • Miesch, A. T. (1980). Scaling variables and interpretation of eigenvalues in principal component analysis of geologic data. Journal of the International Association for Mathematical Geology, 12(6), 523–538.

    Article  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, 578–591.

  • Othman, A. A., & Gloaguen, R. (2017). Integration of spectral, spatial and morphometric data into lithological mapping: A comparison of different Machine Learning Algorithms in the Kurdistan Region, NE Iraq. Journal of Asian Earth Sciences, 90–102.

  • Pan, Y., & Dong, P. (1999). The lower chanjiang (yangzi/yangzte river) metallogenic belt, east china: Intrusion- and wall rock-hosted cu-fe-au, mo, zn, pb and ag deposits. Ore Geology Reviews, 15, 177–242.

    Article  Google Scholar 

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

  • Porwal, A. (2003). Knowledge-driven and data-driven fuzzy models for predictive mineral potential mapping. Natural Resources Research, 12(1), 1–25.

    Article  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, 477–483.

  • Porwal, A., González-álvarez, I., Markwitz, V., Mccuaig, T. C., & Mamuse, A. (2010). Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the yilgarn craton, western australia. Ore Geology Reviews, 38(3), 184–196.

    Article  Google Scholar 

  • Qin, Y. Z., & Liu, L. M. (2018). Quantitative 3D association of geological factors and geophysical fields with mineralization and its significance for ore prediction: An example from anqing Orefield, China. Minerals, 8(7), 300.

    Article  Google Scholar 

  • Qin, Y. Z., Wu, W. C., Xie, L. F., Ou, P. H., & Huang, X. L. (2021). Application of machine learning based mineral prospectivity mapping in the Yuexi Antimony Orefield, Hunan province. Journal of East China University of Technology (natural Science), 01(004), 28–40. (In Chinese with English Abstract).

    Google Scholar 

  • Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106.

    Article  Google Scholar 

  • Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chicarivas, 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(71), 804–818.

    Article  Google Scholar 

  • Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning (from theory to algorithms). Support Vector Machines, 15, 167–178. https://doi.org/10.1017/CBO9781107298019.

  • Smirnoff, A., Boisvert, E., & Paradis, S. J. (2008). Support vector machine for 3d modelling from sparse geological information of various origins. Computers and Geosciences, 34(2), 127–143.

    Article  Google Scholar 

  • Stein, M. L. (1999). Interpolation of spatial data: Some theory for Kriging. New York: Springer.

  • Sun, T., Chen, F., Zhong, L., Liu, W. M., & Wang, Y. (2019). GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China. Ore Geology Reviews, 109, 26–49.

    Article  Google Scholar 

  • Sun, T., Li, H., Wu, K., Chen, F., Zhu, Z., & Hu, Z. (2020). Data-driven predictive modelling of mineral prospectivity using machine learning and deep learning methods: A case study from Southern Jiangxi Province, China. Minerals, 10(2), 102.

    Article  Google Scholar 

  • Sun, T., Wu, K., Chen, L., Liu, W. M., Wang, Y., & Zhang, C. S. (2017). Joint application of fractal analysis and weights-of-evidence method for revealing the geological controls on regional-scale tungsten mineralization in Southern Jiangxi Province, China. Minerals, 7(12), 243.

    Article  Google Scholar 

  • Vapnik, V., Golowich, S., & Smola, A. (1996). Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems, 9, 281–287.

    Google Scholar 

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

    Article  Google Scholar 

  • Wang, X. C., & Zhou, Y. C. (1995). Geological characteristics and origin of Anqing Cu–Fe deposit. Geology and Exploration, 31, 16–23. (In Chinese with English Abstract).

    Google Scholar 

  • Wu, W. C., Zucca, C., Karam, F., & Liu, G. P. (2016). Enhancing the performance of regional land cover mapping. International Journal of Earth Observation and Geoinformation, 52, 422–432.

    Article  Google Scholar 

  • Wu, W. C., Zucca, C., Muhaimeed, A. S., Alshafie, W. M., Alquraish, A. M. F., Nangia, V., Zhu, M. Q., & Liu, G. P. (2018). Soil salinity prediction and mapping by machine learning regression in Central Mesopotamia. Land Degradation and Development, 29(11), 4005–4014.

    Article  Google Scholar 

  • Xiao, K. Y., Li, N., Porwal, A., Holden, E. J., Bagas, L., & Lu, Y. (2015). Research on gis-based 3d prospectivity mapping and a case study of jiama copper-polymetallic deposit in Tibet, China. Ore Geology Reviews, 71(3), 611–632.

    Article  Google Scholar 

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

  • 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.

    Article  Google Scholar 

  • Yuan, F., Li, X. H., Zhang, M. M., Jowitt, S. M., Jia, C., Zheng, T. K., & Zhou, T. F. (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.

    Article  Google Scholar 

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

  • Zhai, Y. S., Wang, J. P., Peng, R. M., & Liu, J. J. (2009). Research on superimposed metallogenic and polygenetic mineral deposit. Earth Science Frontiers, 16(6), 282–290. (In Chinese).

    Google Scholar 

  • Zhang, L. J., Zhou, T. F., Fan, Y., & Yuan, F. (2008). SHRIMP U-Pb zircon dating of Yueshan intrusion in the Yueshan ore field, Anhui, and its significance. Acta Petrological Sinica, 24, 1725–1732. (In Chinese with English Abstract).

    Google Scholar 

  • Zhou, T. F., Yuan, F., Yue, S. C., Liu, X. D., Zhang, X., & Fan, Y. (2007). Geochemistry and evolution of ore-forming fluids of the Yueshan Cu–Au skarn-and vein-type deposits, Anhui Province, South China. Ore Geology Reviews, 31, 279–303.

    Article  Google Scholar 

  • Zhou, Y. Z., Li, P. X., Wang, S. G., Xiao, F., Li, J. Z., & Gao, L. (2017). Research progress on big data and intelligent modelling of mineral deposits. Bulletin of Mineralogy, Petrology and Geochemistry, 36(2), 327–331. (In Chinese with English Abstract).

    Google Scholar 

  • Zuo, R. G., Zhang, Z. J., Zhang, D. J., Carranza, E. J. M., & Wang, H. C. (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.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Zuo, R. G. (2020). Geodata science-based mineral prospectivity papping: A Review. Natural Resources Research, 29, 3415–3424.

    Article  Google Scholar 

Download references

Acknowledgments

The research funding leading to this paper was jointly provided by the National Natural Science Foundation of China granted to Prof. Liangming Liu (Grant No. 41772351) and the Start-up Fund for Scientific Research from the East China University of Technology granted to Dr. Yaozu Qin (Grant No. DHBK2019040). The Tongling Nonferrous Metal Group Corp. Ltd. is acknowledged for providing financial and logistic support to the field work of this research. Moreover, the constructive comments on the manuscript by two anonymous reviewers and timely editorial handling by editors are highly appreciated.

Funding

This work was financially supported by the National Natural Science Foundation of China granted to Prof. Liangming Liu (Grant No. 41772351) and the Start-up Fund for Scientific Research, from the East China University of Technology granted to Dr. Yaozu Qin (Grant No. DHBK2019040).

Author information

Authors and Affiliations

  1. Key Laboratory of Digital Land and Resources, Faculty of Earth Sciences, East China University of Technology, Nanchang, 330013, China

    Yaozu Qin & Weicheng Wu

  2. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring of Education Ministry, Changsha, 410083, China

    Yaozu Qin & Liangming Liu

  3. Computational Geosciences Research Centre, School of Geoscience and Info-Physics, Central South University, Changsha, 410083, China

    Yaozu Qin & Liangming Liu

Authors
  1. Yaozu Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liangming Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weicheng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YQ conducted the machine learning-based computational experiments and prediction models, analyzed the results and wrote the draft paper; Prof. Liu offered the research data and ideas for this study; Prof. Wu provided the revision for this manuscript.

Corresponding author

Correspondence to Liangming Liu.

Ethics declarations

Conflict of interest

We declare that we have no any commercial or associative interest that represents a conflict of interest in connection with our work and this manuscript entitled “Machine Learning-Based 3D Modeling of Mineral Prospectivity Mapping in the Anqing Orefield, Eastern China”.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qin, Y., Liu, L. & Wu, W. Machine Learning-Based 3D Modeling of Mineral Prospectivity Mapping in the Anqing Orefield, Eastern China. Nat Resour Res 30, 3099–3120 (2021). https://doi.org/10.1007/s11053-021-09893-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-021-09893-7

Keywords

Search

Navigation