Skip to main content

Advertisement

SpringerLink
Log in

Random-Drop Data Augmentation of Deep Convolutional Neural Network for Mineral Prospectivity Mapping

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

Abstract

Convolutional neural network (CNN) has demonstrated promising performance in classification and prediction in various fields. In this study, a CNN is used for mineral prospectivity mapping (MPM) in the southwestern Fujian Province, China. Two limitations of applying CNNs in MPM are addressed: insufficient labeled samples and difficulty of applying CNNs to geological prospecting big data for MPM, which are characterized by massive size, multiple sources, multiple types, multi-temporality, multiple scales, non-stationarity, and heterogeneity. The random-drop data augmentation method, which repeatedly takes dropouts from data, is adopted in this study for generating sufficient training samples. Various experiments are conducted to determine a suitable CNN architecture for MPM. The mapped areas obtained by the constructed CNN are strongly spatially correlated with the locations of known mineralization, and most of the known Fe polymetallic deposits are located in areas with high probabilities. Our findings indicate that such a random-drop data augmentation method is suitable and effective for constructing training datasets to predict the locations of rare geological events. Additionally, CNN appears as a promising tool for integrating multi-source geoscience data, thereby supporting further mineral exploration.

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

Similar content being viewed by others

References

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

    Google Scholar 

  • 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).

  • 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).

  • An, P., Moon, W. M., & Rencz, A. (1991). Application of fuzzy set theory for integration of geological, geophysical and remote sensing data. Canadian Journal of Exploration Geophysics, 27(1), 1–11.

    Google Scholar 

  • Bonham-Carter, G. F. (1989). Weights of evidence modeling: a new approach to mapping mineral potential. Statistical Applications in the Earth Sciences, 1, 171–183.

    Google Scholar 

  • Brown, W. M., Gedeon, T. D., Groves, D. I., & Barnes, R. G. (2012). Artificial neural networks: A new method for mineral prospectivity mapping. Australian Journal of Earth Sciences, 47, 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, 173–187.

    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, 77–787.

    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, 35–50.

    Google Scholar 

  • Chen, C., Dai, H., Liu, Y., & He, B. (2011). Mineral prospectivity mapping integrating multi-source geology spatial data sets and logistic regression modelling. In Proceedings 2011 IEEE international conference on spatial data mining and geographical knowledge services (pp. 214–217).

  • 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, 314–324.

    Google Scholar 

  • 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.

    Google Scholar 

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

    Google Scholar 

  • Deng, J., Dong, W., Socher, R., Li, L., Li, K., & Li, F. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248–255).

  • Dieleman, S., Willett, K. W., & Dambre, J. (2015). Rotation-invariant convolutional neural networks for galaxy morphology prediction. Monthly Notices of the Royal Astronomical Society, 450(2), 1441–1459.

    Google Scholar 

  • Guerra Prado, E. M., de Souza Filho, C. R., Carranza, E. J. M., & Motta, J. G. (2020). Modeling of Cu-Au Prospectivity in the Carajás mineral province (Brazil) through Machine Learning: Dealing with Imbalanced Training Data. Ore Geology Reviews, 124, 103611.

    Google Scholar 

  • Guo, H., Wang, L., & Liang, D. (2016). Big earth data from space: a new engine for earth science. Science Bulletin, 61(7), 505–513.

    Google Scholar 

  • Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N., et al. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 29(6), 82–97.

    Google Scholar 

  • Keskar, N. S., Mudigere, D., Nocedal, J., Smelyanskiy, M., & Tang, P. T. P. (2016). On large-batch training for deep learning: Generalization gap and sharp minima. arXiv preprint arXiv:1609.04836.

  • Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.

  • Krizhevsky, A., &Hinton, G. E. (2009). Learning multiple layers of features from tiny images. Vol. 1. No. 4. Technical report, University of Toronto, 2009.

  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105).

  • LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278–2324.

    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, 2037–2053.

    Google Scholar 

  • Lin, D. (2011). Research on late paleozoic-triassic tectonic evolution and metallogenetic regularities of iron-polymetallic deposits in the Southwestern Fujian Province. Beijing: China University of Geoscience. (In Chinese with English abstract).

    Google Scholar 

  • Mao, J., Tao, K., Xie, F., Zheng, X., & Chen, S. (2001). Rock-forming and ore-forming processes and tectonic environments in southwest Fujian. Acta Petrologica Et Mineralogica, 1, 329–336. (In Chinese with English abstract).

    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 

  • Meinert, L. D. (1992). Skarns and skarn deposits. Geoscience Canada, 19(4), 192–195.

    Google Scholar 

  • Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted Boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10) (pp. 807–814).

  • 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 

  • 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 

  • Simard, P. Y., Steinkraus, D., & Platt, J. C. (2003). Best practices for convolutional neural networks applied to visual document analysis. International Conference on Document Analysis and Recognition, 2, 958–962.

    Google Scholar 

  • Simonyan, K., &Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

  • 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.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  • Xiong, Y., & Zuo, R. (2020). Recognizing multivariate geochemical anomalies for mineral exploration by combining deep learning and one-class support vector machine. Computers & Geosciences, 140, 104484.

    Google Scholar 

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

    Google Scholar 

  • Zhang, D., Ren, N., & Hou, X. (2018b). 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(6), 23–25.

    Google Scholar 

  • Zhang, Z., & Zuo, R. (2015). Tectonic Evolution of southwestern Fujian Province and spatial-temporal distribution regularity of mineral deposits. Acta Petrologica Sinica, 1, 217–229. (In Chinese with English abstract).

    Google Scholar 

  • 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.

    Google Scholar 

  • Zhao, Y., Tan, H., & Sun, J. (1982). Characteristics of the skarn zoning of the makeng and yengshan iron ore deposits in Fujian and their relationship with the mineralization zoning. ACTA Petrologica and Mineralogica et. Analytica, 1, 11–22. (In Chinese with English abstract).

    Google Scholar 

  • Zhao, Y., Tan, H., Xu, Z., Yuan, R., Zheng, R., & Lin, F. (1980). Geological conditions of the formation of calcific-skarn iron deposits in southwestern Fujian and characteristics of their alternation and mineralization. Acta Geoscientica Sinica, 1, 22–48. (In Chinese with English abstract).

    Google Scholar 

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

    Google Scholar 

  • Zuo, R. (2020). Geodata science-based mineral prospectivity mapping: A review. Natural Resources Research. https://doi.org/10.1007/s11053-020-09700-9.

    Article  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., Peng, Y., Li, T., & Xiong, Y. (2020). Challenges of geological prospecting big data mining and integration using deep learning algorithms. Earth Science. https://doi.org/10.3799/dqkx.2020.111. (In Chinese with English abstract).

    Article  Google Scholar 

  • Zuo, R., & Wang, Z. (2020). Effects of random negative training samples on mineral prospectivity mapping. Natural Resources Research. https://doi.org/10.1007/s11053-020-09668-6.

    Article  Google Scholar 

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

    Google Scholar 

  • Zuo, R., & Xiong, Y. (2020). Geodata science and geochemical mapping. Journal of Geochemical Exploration, 209, 106431.

    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.

    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.

    Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 41772344). We thank two reviewers’ comments and suggestions, which helped us to improve this study.

Author information

Authors and Affiliations

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

    Tong Li, Renguang Zuo, Yihui Xiong & Yong Peng

Authors
  1. Tong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Renguang Zuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yihui Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yong Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Renguang Zuo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, T., Zuo, R., Xiong, Y. et al. Random-Drop Data Augmentation of Deep Convolutional Neural Network for Mineral Prospectivity Mapping. Nat Resour Res 30, 27–38 (2021). https://doi.org/10.1007/s11053-020-09742-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-020-09742-z

Keywords

Search

Navigation