Skip to main content

Advertisement

SpringerLink
Log in

Big Data Analytics of Identifying Geochemical Anomalies Supported by Machine Learning Methods

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

Abstract

Big data analytics brings a novel way for identifying geochemical anomalies in mineral exploration because it involves processing of the whole geochemical dataset to reveal statistical correlations between geochemical patterns and known mineralization. Traditional methods of processing exploration geochemical data mainly involve the identification of positive geochemical anomalies related to mineralization, but ignore negative geochemical anomalies. Therefore, the identified geochemical anomalies do not completely reflect the sought geochemical signature of mineralization, leading to uncertainty in geochemical prospecting. In this study, data for 39 geochemical variables from a regional stream sediment geochemical survey of southwest Fujian Province of China were subjected to big data analytics for identifying geochemical anomalies related to skarn-type Fe polymetallic mineralization through deep autoencoder network. The receiver operating characteristic (ROC) and areas under curve (AUC) were applied to evaluate the performance of big data analytics. The AUC of the anomaly map obtained using all the geochemical variables is larger than the AUC of the anomaly map obtained using only five selected elements known to be associated with the mineralization (i.e., Fe2O3, Cu, Pb, Zn, Mn). This indicates that big data analytics, with the support of machine learning methods, is a powerful tool for identifying multivariate geochemical anomalies related to mineralization.

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

Similar content being viewed by others

References

  • Bergmann, R., Ludbrook, J., & Spooren, W. P. J. M. (2000). Different outcomes of the Wilcoxon–Mann–Whitney test from different statistics packages. The American Statistician, 54, 72–77.

    Google Scholar 

  • Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15, 662–679.

    Article  Google Scholar 

  • Carbonell, J. G., Michalski, R. S., & Mitchell, T. M. (1983). An overview of machine learning. In R.S. Michalski, J.G. Carbonell, & T.M. Mitchell (Eds.), Machine learning: An artificial intelligence approach (pp. 3–23). Berlin: Springer.

    Google Scholar 

  • Chen, Y., Lu, L., & Li, X. (2014). Application of continuous restricted Boltzmann machine to identify multivariate geochemical anomaly. Journal of Geochemical Exploration, 140, 56–63.

    Article  Google Scholar 

  • Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19, 171–209.

    Article  Google Scholar 

  • Chen, Y., & Wu, W. (2016). A prospecting cost-benefit strategy for mineral potential mapping based on ROC curve analysis. Ore Geology Reviews, 74, 26–38.

    Article  Google Scholar 

  • Chen, Y., & Wu, W. (2017). Application of one-class support vector machine to quickly identify multivariate anomalies from geochemical exploration data. Geochemistry: Exploration, Environment, Analysis, 17, 231–238.

    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, 314–324.

    Article  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.

    Article  Google Scholar 

  • Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G., & BarceloVidal, C. (2003). Isometric logratiotrans formations for compositional data analysis. Mathematic Geology, 35, 279–300.

    Article  Google Scholar 

  • Ekbia, H., Mattioli, M., Kouper, I., Arave, G., Ghazinejad, A., Bowman, T., et al. (2015). Big data, bigger dilemmas: A critical review. Journal of the Association for Information Science and Technology, 66, 1523–1545.

    Article  Google Scholar 

  • Fan, J., Han, F., & Liu, H. (2014). Challenges of big data analysis. National Science Review, 1, 293–314.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Finlayson, A. E. T. (2011). Dealing with data: Fostering fidelity. Science, 331, 1515.

    Article  Google Scholar 

  • Gantz, J., & Reinsel, D. (2011). Extracting value from chaos. IDC Iview, 1142, 1–12.

    Google Scholar 

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

    Google Scholar 

  • Gonbadi, A. B., Tabatabaei, S. H., & Carranza, E. J. M. (2015). Supervised geochemical anomaly detection by pattern recognition. Journal of Geochemical Exploration, 157, 81–91.

    Article  Google Scholar 

  • Guo, H., Wang, L., Chen, F., & Liang, D. (2014). Scientific big data and digital earth. Chinese Science Bulletin, 59, 5066–5073.

    Article  Google Scholar 

  • Hinton, G. E., Osindero, S., & Teh, Y. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527–1554.

    Article  Google Scholar 

  • Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313, 504–507.

    Article  Google Scholar 

  • Howe, D., Costanzo, M., Fey, P., Gojobori, T., Hannick, L., Hide, W., et al. (2008). Big data: The future of biocuration. Nature, 455, 47–50.

    Article  Google Scholar 

  • Hu, H., Wen, Y., Chua, T. S., & Li, X. (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE Access, 2, 652–687.

    Article  Google Scholar 

  • Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349, 255–260.

    Article  Google Scholar 

  • Kirkwood, C., Cave, M., Beamish, D., Grebby, S., & Ferreira, A. (2016). A machine learning approach to geochemical mapping. Journal of Geochemical Exploration, 167, 49–61.

    Article  Google Scholar 

  • Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. Informatica, 31, 249–268.

    Google Scholar 

  • Liu, Y., Ma, S., Zhu, L., Sadeghi, M., Doherty, A. L., Cao, D., et al. (2016). The multi-attribute anomaly structure model: An exploration tool for the Zhaojikou epithermal Pb-Zn deposit, China. Journal of Geochemical Exploration, 169, 50–59.

    Article  Google Scholar 

  • Ma, S., Zhu, L., & Liu, C. (2013). Anomaly models of spatial structures for copper–molybdenum ore deposits and their application. Acta Geologica Sinica (English Edition), 3, 843–857.

    Google Scholar 

  • Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.

  • Mayer-Schonberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work and think. New York: Houghton Mifflin Harcourt Publishing Company.

    Google Scholar 

  • Nykänen, V., Niiranen, T., Molnár, F., Lahti, I., Korhonen, K., Cook, N., et al. (2017). Optimizing a knowledge-driven prospectivity model for gold deposits within Peräpohja Belt, Northern Finland. Natural Resources Research, 26, 571–584.

    Article  Google Scholar 

  • O’Brien, J. J., Spry, P. G., Nettleton, D., Xu, R., & Teale, G. S. (2015). Using random forests to distinguish gahnite compositions as an exploration guide to Broken Hill-type Pb–Zn–Ag deposits in the Broken Hill domain, Australia. Journal of Geochemical Exploration, 49, 74–86.

    Article  Google Scholar 

  • Parsa, M., Maghsoudi, A., & Yousefi, M. (2017). A receiver operating characteristics-based geochemical data fusion technique for targeting undiscovered mineral deposits. Natural Resources Research. doi:10.1007/s11053-017-9351-6.

    Google Scholar 

  • Rose, A. W., Hawkes, H. E., & Webb, J. S. (1979). Geochemistry in mineral exploration (p. 657). New York, NY: Academic Press.

    Google Scholar 

  • Shi, C., & Wang, C. (1995). Regional geochemical secondary negative anomalies and their significance. Journal of Geochemical Exploration, 55, 11–23.

    Article  Google Scholar 

  • Twarakavi, N. K. C., Misra, D., & Bandopadhyay, S. (2006). Prediction of arsenic in bedrock derived stream sediments at a gold mine site under conditions of sparse data. Natural Resources Research, 15, 15–26.

    Article  Google Scholar 

  • Wang, J., Chen, W., Zhang, Q., Jin, W., Jiao, S., Wang, Y., et al. (2017). MORB data mining: Reflection of basalt discrimination diagram. Geotectonica et Metallogenia, 41, 420–431. (in Chinese with English abstract).

    Google Scholar 

  • Wang, H., Cheng, Q., & Zuo, R. (2015a). Quantifying the spatial characteristics of geochemical patterns via GIS-based geographically weighted statistics. Journal of Geochemical Exploration, 157, 110–119.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Wang, X., Xie, X., Zhang, B., & Hou, Q. (2011). Geochemical probe into China’s continental crust. Acta Geoscientical Sinica, 32, 65–83. (in Chinese with English abstract).

    Google Scholar 

  • Wang, H., Xu, Z., Fujita, H., & Liu, S. (2016). Towards felicitous decision making: An overview on challenges and trends of big data. Information Sciences, 367, 747–765.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Wang, J., & Zuo, R. (2015). A MATLAB-based program for processing geochemical data using fractal/multifractal modeling. Earth Science Informatics, 8, 937–947.

    Article  Google Scholar 

  • Wang, Z., Zuo, R., & Zhang, Z. (2015). Spatial analysis of Fe deposits in Fujian Province, China: Implications for mineral exploration. Journal of Earth Science, 26, 813–820.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Xie, X., Wang, X., Zhang, Q., Zhou, G., Cheng, H., Liu, D., et al. (2008). Multi-scale geochemical mapping in China. Geochemistry: Exploration Environment, Analysis, 8, 333–341.

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Xiong, Y., Zuo, R., Wang, K., & Wang, J. (2017). Identification of geochemical anomalies via local RX anomaly detector. Journal of Geochemical Exploration. doi:10.1016/j.gexplo.2017.06.021.

    Google Scholar 

  • Zhai, M. (2017). Granites: Leading study issue for continental evolution. Acta Petrologica Sinica, 33, 1369–1380. (in Chinese with English abstract).

    Google Scholar 

  • Zhang, Q., Jiang, S., Li, C., & Chen, W. (2017). Granite and continental tectonics, magma thermal field and metallogenesis. Acta Petrologica Sinica, 33, 1524–1540. (in Chinese with English abstract).

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Zhang, Z., & Zuo, R. (2015). Tectonic evolution of southwestern Fujian Province and spatial-temporal distribution regularity of mineral deposits. Acta Petrologica Sinica, 31, 217–229. (in Chinese with English abstract).

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Zhang, Z., Zuo, R., & Cheng, Q. (2015b). Geological features and formation processes of the Makeng Fe deposit, China. Resource Geology, 65, 266–284.

    Article  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, 556–572.

    Article  Google Scholar 

  • Zhao, J., Chen, S., & Zuo, R. (2016). Identifying geochemical anomalies associated with Au–Cu mineralization using multifractal and artificial neural network models in the Ningqiang district, Shaanxi, China. Journal of Geochemical Exploration, 164, 54–64.

    Article  Google Scholar 

  • Zhou, L., Pan, S., Wang, J., & Vasilakos, V. A. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350–361.

    Article  Google Scholar 

  • Zuo, R. (2016). A nonlinear controlling function of geological features on magmatic-hydrothermal mineralization. Scientific Reports, 6, 27127.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Zuo, R., Carranza, E. J. M., & Wang, J. (2016). Spatial analysis and visualization of exploration geochemical data. Earth-Science Reviews, 158, 9–18.

    Article  Google Scholar 

  • Zuo, R., & Cheng, Q. (2008). Mapping singularities: A technique to identify potential Cu mineral deposits using sediment geochemical data, an example for Tibet, west China. Mineralogical Magazine, 72, 531–534.

    Article  Google Scholar 

  • Zuo, R., Cheng, Q., Agterberg, F. P., & Xia, Q. (2009). Application of singularity mapping technique to identify local anomalies using stream sediment geochemical data, a case study from Gangdese, Tibet, western China. Journal of Geochemical Exploration, 101, 225–235.

    Article  Google Scholar 

  • Zuo, R., & Wang, J. (2016). Fractal/multifractal modeling of geochemical data: A review. Journal of Geochemical Exploration, 164, 33–41.

    Article  Google Scholar 

  • Zuo, R., Wang, J., Chen, G., & Yang, M. (2015a). Identification of weak anomalies: A multifractal perspective. Journal of Geochemical Exploration, 148, 12–24.

    Article  Google Scholar 

  • Zuo, R., Xia, Q., & Zhang, D. (2013). A comparison study of the C–A and S–A models with singularity analysis to identify geochemical anomalies in covered areas. Applied Geochemistry, 33, 165–172.

    Article  Google Scholar 

  • Zuo, R., Zhang, Z., Zhang, D., Carranza, E. J. M., & Wang, H. (2015b). 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 

Download references

Acknowledgments

We thank John Carranza (Editor-in-Chief) for his edits and comments and three reviewers for their comments and suggestions, which helped to improve this study. This research benefited from the joint financial support from the National Key Research and Development Program of China (2016YFC0600508), the National Natural Science Foundation of China (41772344 and 41522206), the Natural Science Foundation of Hubei Province (2017CFA053), and the MOST Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences (MSFGPMR03-3).

Author information

Authors and Affiliations

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

    Renguang Zuo & Yihui Xiong

Authors
  1. Renguang Zuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yihui Xiong
    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

Zuo, R., Xiong, Y. Big Data Analytics of Identifying Geochemical Anomalies Supported by Machine Learning Methods. Nat Resour Res 27, 5–13 (2018). https://doi.org/10.1007/s11053-017-9357-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-017-9357-0

Keywords

Search

Navigation