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Quantification of Uncertainty Associated with Evidence Layers in Mineral Prospectivity Mapping Using Direct Sampling and Convolutional Neural Network

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Abstract

Mineral prospectivity mapping (MPM) mainly focuses on searching prospective areas for a particular type of mineral deposits. However, MPM is typically subject to uncertainties originated from conceptual mineral deposit models, geoscience data, and prediction models. This study utilizes a hybrid model combining a direct sampling algorithm and a convolutional neural network to quantify the uncertainty associated with the evidence layers in MPM. Specifically, a direct sampling algorithm was first used to simulate equiprobable evidence layers that followed the similar pattern of geological features. A convolutional neural network was then employed to produce mineral prospectivity maps by integrating the simulated evidence layers. The initial risk–return analysis was conducted based on the obtained mineral prospectivity maps to search for areas linked to high potential and low risk. Finally, a Markowitz mean–variance model was adopted to further outline prior prospective areas to support future mineral exploration in the high-potential areas. A case study of mapping mineral prospectivity for gold polymetallic deposits in the Middle–Lower Yangtze River Valley metallogenic belt in the southeastern Hubei Province of China was implemented. The comparative results indicated that the hybrid model of the direct sampling algorithm and convolutional neural network, which considered the uncertainty of evidence layers, achieved a higher success rate and prediction accuracy than the deterministic framework.

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References

  • Aitchison, J. (1986). The statistical analysis of compositional data. Chapman & Hall.

    Book  Google Scholar 

  • Bistacchi, A., Massironi, M., Dal Piaz, G. V., Dal Piaz, G., Monopoli, B., Schiavo, A., & Toffolon, G. (2008). 3D fold and fault reconstruction with an uncertainty model: An example from an Alpine tunnel case study. Computers & Geosciences, 34(4), 351–372.

    Article  Google Scholar 

  • Boisvert, J. B., Pyrcz, M. J., & Deutsch, C. V. (2007). Multiple-point statistics for training image selection. Natural Resources Research, 16(4), 313–321.

    Article  Google Scholar 

  • Bonham-Carter, G. F., Agterberg, F. P., & Wright, D. F. (1989). Integration of geological datasets for gold exploration in Nova Scotia. Digital geologic and geographic information systems (pp. 15–23). American Geophysical Union (AGU).

    Chapter  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.

    Article  Google Scholar 

  • Burkin, J. N., Lindsay, M. D., Occhipinti, S. A., & Holden, E.-J. (2019). Incorporating conceptual and interpretation uncertainty to mineral prospectivity modelling. Geoscience Frontiers, 10(4), 1383–1396.

    Article  Google Scholar 

  • Caers, J. (2011). Modeling uncertainty in the earth sciences. Wiley.

    Book  Google Scholar 

  • Carranza, E. J. M., Woldai, T., & Chikambwe, E. M. (2005). Application of data-driven evidential belief functions to prospectivity mapping for aquamarine-bearing pegmatites, Lundazi district, Zambia. Natural Resources Research, 14(1), 47–63.

    Article  Google Scholar 

  • Chen, W., Ruan, Q., Yang, W., Li, J., Ke, Y., Fan, Z., & Zhai, S. (2012). The geological characteristics of Jinjingzui skarn gold deposit and its causes in the east of Hubei province. China Mining Magazine, 21(04), 56–59. (In Chinese with English abstract).

    Google Scholar 

  • 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), 314–324.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Daviran, M., Parsa, M., Maghsoudi, A., & Ghezelbash, R. (2022). Quantifying uncertainties linked to the diversity of mathematical frameworks in knowledge-driven mineral prospectivity mapping. Natural Resources Research, 31(5), 2271–2287.

    Article  Google Scholar 

  • Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G., & Barceló-Vidal, C. (2003). Isometric logratio transformations for compositional data analysis. Mathematical Geology, 35(3), 279–300.

    Article  Google Scholar 

  • Fabbri, A. G., & Chung, C.-J. (2008). On blind tests and spatial prediction models. Natural Resources Research, 17(2), 107–118.

    Article  Google Scholar 

  • Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.

    Article  Google Scholar 

  • Garrido, M., Sepúlveda, E., Ortiz, J., & Townley, B. (2020). Simulation of synthetic exploration and geometallurgical database of porphyry copper deposits for educational purposes. Natural Resources Research, 29(6), 3527–3545.

    Article  Google Scholar 

  • Ge, Y., Jin, Y., Stein, A., Chen, Y., Wang, J., Wang, J., Cheng, Q., Bai, H., Liu, M., & Atkinson, P. (2019). Principles and methods of scaling geospatial earth science data. Earth-Science Reviews, 197, 102897.

    Article  Google Scholar 

  • Ghezelbash, R., Maghsoudi, A., & Carranza, E. J. M. (2020). Sensitivity analysis of prospectivity modeling to evidence maps: Enhancing success of targeting for epithermal gold, Takab district, NW Iran. Ore Geology Reviews, 120, 103394.

    Article  Google Scholar 

  • Goodchild, M. F. (2011). Scale in GIS: An overview. Geomorphology, 130(1), 5–9.

    Article  Google Scholar 

  • Goovaerts, P. (1997). Geostatistics for natural resource evaluation. Oxford University Press.

    Google Scholar 

  • Graffelman, J., Pawlowsky-Glahn, V., Egozcue, J. J., & Buccianti, A. (2018). Exploration of geochemical data with compositional canonical biplots. Journal of Geochemical Exploration, 194, 120–133.

    Article  Google Scholar 

  • Guo, J., Wang, Z., Li, C., Li, F., Jessell, M. W., Wu, L., & Wang, J. (2022). Multiple-point geostatistics-based three-dimensional automatic geological modeling and uncertainty analysis for borehole data. Natural Resources Research, 31(4), 2347–2367.

    Article  Google Scholar 

  • Hansen, T. M., Vu, L. T., Mosegaard, K., & Cordua, K. S. (2018). Multiple point statistical simulation using uncertain (soft) conditional data. Computers & Geosciences, 114, 1–10.

    Article  Google Scholar 

  • Hou, W., Yang, Q., Chen, X., Xiao, F., & Chen, Y. (2021). Uncertainty analysis and visualization of geological subsurface and its application in metro station construction. Frontiers of Earth Science, 15(3), 692–704.

    Article  Google Scholar 

  • Hu, Q. (2014). The mesozoic diagenetic & metallogenic tectonic background and the temporal & spatial distribution in the southeast of Hubei Province. Resources Environment & Engineering, 28(06), 767–776. (In Chinese with English abstract).

    Google Scholar 

  • Hua, E., Sun, F., Cen, Z., Xiao, D., Luo, H., Yu, W., & Li, L. (2022). Discussion on prospecting direction of copper and gold deposits in Southeast Hubei. Mineral Resources and Geology, 36(01), 9–13. (In Chinese with English abstract).

    Google Scholar 

  • Ke, Y., Cai, H., Du, K., Wu, Y., & Yuan, H. (2016). Analysis of geological characteristics and prospecting potential of Jiguanzui Cu-Au deposits in Daye city, Hubei Province. Resources Environment & Engineering, 30(06), 817–824. (In Chinese with English abstract).

    Google Scholar 

  • Kirkwood, C., Economou, T., Pugeault, N., & Odbert, H. (2022). Bayesian deep learning for spatial interpolation in the presence of auxiliary information. Mathematical Geosciences, 54(3), 507–531.

    Article  Google Scholar 

  • Kreuzer, O. P., Etheridge, M. A., Guj, P., McMahon, M. E., & Holden, D. J. (2008). Linking mineral deposit models to quantitative risk analysis and decision-making in exploration. Economic Geology, 103(4), 829–850.

    Article  Google Scholar 

  • Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems (pp. 1097–1105).

  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444.

    Article  Google Scholar 

  • Li, B., Liu, B., Wang, G., Chen, L., & Guo, K. (2021a). Using geostatistics and maximum entropy model to identify geochemical anomalies: A case study in Mila Mountain region, southern Tibet. Applied Geochemistry, 124, 104843.

    Article  Google Scholar 

  • Li, L., Romary, T., & Caers, J. (2015). Universal kriging with training images. Spatial Statistics, 14, 240–268.

    Article  Google Scholar 

  • Li, S., Chen, J., & Xiang, J. (2020). Applications of deep convolutional neural networks in prospecting prediction based on two-dimensional geological big data. Neural Computing and Applications, 32(7), 2037–2053.

    Article  Google Scholar 

  • Li, T., Zuo, R., Xiong, Y., & Peng, Y. (2021b). Random-drop data augmentation of deep convolutional neural network for mineral prospectivity mapping. Natural Resources Research, 30(1), 27–38.

    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.

    Article  Google Scholar 

  • Lisitsin, V. A., Porwal, A., & McCuaig, T. C. (2014). Probabilistic fuzzy logic modeling: Quantifying uncertainty of mineral prospectivity models using Monte Carlo simulations. Mathematical Geosciences, 46(6), 747–769.

    Article  Google Scholar 

  • Liu, Y., Cheng, Q., Carranza, E. J. M., & Zhou, K. (2019). Assessment of geochemical anomaly uncertainty through geostatistical simulation and singularity analysis. Natural Resources Research, 28(1), 199–212.

    Article  Google Scholar 

  • Luo, S., Yao, H., Li, Q., Wang, W., Wan, K., Meng, Y., & Liu, B. (2019). High-resolution 3D crustal S-wave velocity structure of the middle-lower Yangtze River metallogenic belt and implications for its deep geodynamic setting. Science China Earth Sciences, 62(9), 1361–1378.

    Article  Google Scholar 

  • Maller, R. A., & Turkington, D. A. (2003). New light on the portfolio allocation problem. Mathematical Methods of Operations Research, 56(3), 501–511.

    Article  Google Scholar 

  • Maller, R., Durand, R., & Jafarpour, H. (2010). Optimal portfolio choice using the maximum Sharpe ratio. Journal of Risk, 12(4), 49–73.

    Article  Google Scholar 

  • Mao, J., Wang, Y., Lehmann, B., Yu, J., Du, A., Mei, Y., et al. (2006). Molybdenite Re–Os and albite 40Ar/39Ar dating of Cu–Au–Mo and magnetite porphyry systems in the Yangtze River valley and metallogenic implications. Ore Geology Reviews, 29(3), 307–324.

    Article  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.

    Article  Google Scholar 

  • Mao, J., Xie, G., Zhang, Z., Li, X., Wang, Y., Zhang, C., & Li, Y. (2005). Mesozoic large-scale metallogenic pulses in North China and corresponding geodynamic setting. Acta Petrologica Sinica, 21, 169–188.

    Google Scholar 

  • Mariethoz, G., Renard, P., & Straubhaar, J. (2010). The direct sampling method to perform multiple-point geostatistical simulations. Water Resources Research, 46, W11536. https://doi.org/10.1029/2008WR007621

    Article  Google Scholar 

  • Mariethoz, G., Renard, P., & Straubhaar, J. (2011). Extrapolating the fractal characteristics of an image using scale-invariant multiple-point statistics. Mathematical Geosciences, 43(7), 783.

    Article  Google Scholar 

  • Markowitz, H. (1952). Portfolio selection*. The Journal of Finance, 7(1), 77–91.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Meerschman, E., Pirot, G., Mariethoz, G., Straubhaar, J., Van Meirvenne, M., & Renard, P. (2013). A practical guide to performing multiple-point statistical simulations with the direct sampling algorithm. Computers & Geosciences, 52, 307–324.

    Article  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.

    Article  Google Scholar 

  • Oriani, F., Straubhaar, J., Renard, P., & Mariethoz, G. (2014). Simulation of rainfall time series from different climatic regions using the direct sampling technique. Hydrology and Earth System Sciences, 18(8), 3015–3031.

    Article  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 depostis. Ore Geology Reviews, 15(4), 177–242.

    Article  Google Scholar 

  • Pardo-Igúzquiza, E., & Chica-Olmo, M. (2005). Interpolation and mapping of probabilities for geochemical variables exhibiting spatial intermittency. Applied Geochemistry, 20(1), 157–168.

    Article  Google Scholar 

  • 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., Carranza, E. J. M., & Ahmadi, B. (2022). 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 

  • Parsa, M., & Pour, A. B. (2021). A simulation-based framework for modulating the effects of subjectivity in greenfield mineral prospectivity mapping with geochemical and geological data. Journal of Geochemical Exploration, 229, 106838.

    Article  Google Scholar 

  • Rezaee, H., Mariethoz, G., Koneshloo, M., & Asghari, O. (2013). Multiple-point geostatistical simulation using the bunch-pasting direct sampling method. Computers & Geosciences, 54, 293–308.

    Article  Google Scholar 

  • Singer, D. A. (2010). Progress in integrated quantitative mineral resource assessments. Ore Geology Reviews, 38(3), 242–250.

    Article  Google Scholar 

  • Singer, D. A., & Kouda, R. (1999). Examining risk in mineral exploration. Natural Resources Research, 8(2), 111–122.

    Article  Google Scholar 

  • Srinivas, M., & Patnaik, L. M. (1994). Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 24(4), 656–667.

    Article  Google Scholar 

  • Srinivas, S., Sarvadevabhatla, R. K., Mopuri, K. R., Prabhu, N., Kruthiventi, S. S. S., & Babu, R. V. (2017). An introduction to deep convolutional neural nets for computer vision. Deep learning for medical image analysis (pp. 25–52). Academic Press.

    Chapter  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, W., Xie, Z., Chen, J., Zhang, X., Chai, Z., Du, A., Zhao, J., Zhang, C., & Zhou, T. (2003). Os-Os dating of copper and molybdenum deposits along the middle and lower reaches of the Yangtze river, China. Economic Geology, 98(1), 175–180.

    Google Scholar 

  • Talebi, H., Mueller, U., Peeters, L. J. M., Otto, A., de Caritat, P., Tolosana-Delgado, R., & van den Boogaart, K. G. (2022). Stochastic modelling of mineral exploration targets. Mathematical Geosciences, 54(3), 593–621.

    Article  Google Scholar 

  • Talebi, H., Peeters, L. J. M., Mueller, U., Tolosana-Delgado, R., & van den Boogaart, K. G. (2020). Towards geostatistical learning for the geosciences: A case study in improving the spatial awareness of spectral clustering. Mathematical Geosciences, 52(8), 1035–1048.

    Article  Google Scholar 

  • Talebi, H., Mueller, U., & Tolosana-Delgado, R. (2019). Joint simulation of compositional and categorical data via direct sampling technique—application to improve mineral resource confidence. Computers & Geosciences, 122, 87–102.

    Article  Google Scholar 

  • van der Grijp, Y., Minnitt, R., & Rose, D. (2021). Modelling a complex gold deposit with multiple-point statistics. Ore Geology Reviews, 139, 104427.

    Article  Google Scholar 

  • Wang, J., & Zuo, R. (2018). Identification of geochemical anomalies through combined sequential Gaussian simulation and grid-based local singularity analysis. Computers & Geosciences, 118, 52–64.

    Article  Google Scholar 

  • Wang, L., Huang, J., Yu, J., Griffin, W. L., Wang, R., Zhang, S., & Yang, Y. (2014). Zircon U–Pb dating and Lu–Hf isotope study of intermediate-mafic sub-volcanic and intrusive rocks in the Lishui Basin in the middle and lower reaches of Yangtze river. Chinese Science Bulletin, 59(27), 3427–3440.

    Article  Google Scholar 

  • Wang, M., Shang, X., Wei, K., Liu, D., & Zhang, F. (2019). Elemental geochemical characteristics of tonglushan skarn-type Cu–Fe–Au Deposit in the Southeastern Hubei, China and their geological implications. Journal of Earth Science and Environment, 41(04), 431–444. (In Chinese with English abstract).

    Google Scholar 

  • Wang, Z., Yin, Z., Caers, J., & Zuo, R. (2020a). A Monte Carlo-based framework for risk-return analysis in mineral prospectivity mapping. Geoscience Frontiers, 11(6), 2297–2308.

    Article  Google Scholar 

  • Wang, Z., Zuo, R., & Yang, F. (2022). Geological mapping using direct sampling and a convolutional neural network based on geochemical survey data. Mathematical Geosciences. https://doi.org/10.1007/s11004-022-10023-z

    Article  Google Scholar 

  • Wang, Q., Zhou, L., Little, S. H., Liu, J., Feng, L., & Tong, S. (2020b). The geochemical behavior of Cu and its isotopes in the Yangtze river. Science of The Total Environment, 728, 138428.

    Article  Google Scholar 

  • Wei, J., Qi, X., Zhou, Y., Wang, F., Zhu, D., & Lu, L. (2019). Characteristics of pyrite and deep metallogenic potential in Jiguanzui Cu–Au deposit, Southeast Hubei Province. Metal Mine, 11, 132–141. (In Chinese with English abstract).

    Google Scholar 

  • Xie, G., Mao, J., Zhao, H., Wei, K., Jin, S., Pan, H., & Ke, Y. (2011). Timing of skarn deposit formation of the Tonglushan ore district, southeastern Hubei Province, middle-lower Yangtze river valley metallogenic belt and its implications. Ore Geology Reviews, 43(1), 62–77.

    Article  Google Scholar 

  • Xie, G., Zhao, H., Zhao, C., Li, X., Hou, K., & Pan, H. (2009). Re–Os dating of molybdenite from Tonglushan ore district in southeastern Hubei Province, middle-lower Yangtze river belt and its geological significance. Mineral Deposits, 28(03), 227–239. (In Chinese with English abstract).

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Yang, N., Zhang, Z., Yang, J., & Hong, Z. (2022). Applications of data augmentation in mineral prospectivity prediction based on convolutional neural networks. Computers & Geosciences, 161, 105075.

    Article  Google Scholar 

  • Yao, Y., Ruan, Q., Jin, H., & He, J. (2014). Discussion on geological characteristics and prospecting direction of altered rock type gold deposit, Southeastern Hubei. Resources Environment & Engineering, 28(06), 823–829. (In Chinese with English abstract).

    Google Scholar 

  • Yin, B., Zuo, R., & Xiong, Y. (2022). Mineral prospectivity mapping via gated recurrent unit model. Natural Resources Research, 31(4), 2065–2079.

    Article  Google Scholar 

  • Yin, B., Zuo, R., Xiong, Y., Li, Y., & Yang, W. (2021). Knowledge discovery of geochemical patterns from a data-driven perspective. Journal of Geochemical Exploration, 231, 106872.

    Article  Google Scholar 

  • Zhang, C., Zuo, R., & Xiong, Y. (2021). Detection of the multivariate geochemical anomalies associated with mineralization using a deep convolutional neural network and a pixel-pair feature method. Applied Geochemistry, 130, 104994.

    Article  Google Scholar 

  • Zhang, W. (2015). Ore genesis of the Jiguanzui Cu-Au deposit in Southeastern Hubei Province, China. Wuhan of China (Doctoral dissertation, China University of Geosciences) p. 142. (In Chinese with English abstract).

  • Zhao, Y., Zhang, Y., & Bi, C. (1999). Geology of gold-bearing skarn deposits in the middle and lower Yangtze river valley and adjacent regions. Ore Geology Reviews, 14(3), 227–249.

    Article  Google Scholar 

  • Zhou, T., Fan, Y., & Yuan, F. (2008). Advances on petrogensis and metallogeny study of the mineralization belt of the middle and lower reaches of the Yangtze river area. Acta Petrologica Sinica, 24(08), 1665–1678. (In Chinese with English abstract).

    Google Scholar 

  • Zuo, R. (2011). Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum–area fractal modeling in the Gangdese Belt, Tibet (China). Journal of Geochemical Exploration, 111(1), 13–22.

    Article  Google Scholar 

  • Zuo, R. (2020). Geodata science-based mineral prospectivity mapping: a review. Natural Resources Research, 29(6), 3415–3424.

    Article  Google Scholar 

  • Zuo, R., Kreuzer, O. P., Wang, J., Xiong, Y., Zhang, Z., & Wang, Z. (2021). Uncertainties in GIS-based mineral prospectivity mapping: Key types, potential impacts and possible solutions. Natural Resources Research, 30(5), 3059–3079.

    Article  Google Scholar 

  • Zuo, R., & Wang, Z. (2020). Effects of random negative training samples on mineral prospectivity mapping. Natural Resources Research, 29(6), 3443–3455.

    Article  Google Scholar 

  • Zuo, R., Xia, Q., & Wang, H. (2013). Compositional data analysis in the study of integrated geochemical anomalies associated with mineralization. Applied Geochemistry, 28, 202–211.

    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, 1–14.

    Article  Google Scholar 

  • Zuo, R., & Xu, Y. (2022). Graph deep learning model for mapping mineral prospectivity. Mathematical Geosciences. https://doi.org/10.1007/s11004-022-10015-z

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

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Acknowledgments

We are grateful to an anonymous reviewer and Dr. Mohammad Parsa for their valuable comments and suggestions which improved this study. This study was supported by the National Natural Science Foundation of China (41972303, 42102332, and 42172326).

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National Natural Science Foundation of China, 41972303, Renguang Zuo, 42102332, Renguang Zuo, 42172326, Ziye Wang.

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  1. State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, 430074, China

    Fanfan Yang, Ziye Wang & Renguang Zuo

  2. Hubei Geological Survey, Wuhan, 430034, China

    Siquan Sun & Bao Zhou

  3. Hubei Geological Exploration Engineering Technology Research Center, Wuhan, 430034, China

    Siquan Sun & Bao Zhou

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Yang, F., Wang, Z., Zuo, R. et al. Quantification of Uncertainty Associated with Evidence Layers in Mineral Prospectivity Mapping Using Direct Sampling and Convolutional Neural Network. Nat Resour Res 32, 79–98 (2023). https://doi.org/10.1007/s11053-022-10144-6

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