Skip to main content
SpringerLink
Log in

Aspects of Regional and Worldwide Mineral Resource Prediction

  • Special Issue on Digital Geosciences and Quantitative Exploration of Mineral Resources
  • Published:
Journal of Earth Science Aims and scope Submit manuscript

Abstract

The purpose of this contribution is to highlight four topics of regional and worldwide mineral resource prediction: (1) use of the jackknife for bias elimination in regional mineral potential assessments; (2) estimating total amounts of metal from mineral potential maps; (3) fractal/multifractal modeling of mineral deposit density data in permissive areas; and (4) worldwide and large-areas metal size-frequency distribution modeling. The techniques described in this paper remain tentative because they have not been widely researched and applied in mineral potential studies. Although most of the content of this paper has previously been published, several perspectives for further research are suggested.

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

Similar content being viewed by others

References Cited

  • Agterberg, F. P., Chung, C. F., Fabbri, A. G., et al., 1972. Geomathematical Evaluation of Copper and Zinc Potential of the Abitibi Area, Ontario and Quebec. Geological Survey of Canada, 41–71

  • Agterberg, F. P., 1973. Probabilistic Models to Evaluate Regional Mineral Potential. In: Proc. Symposium on Mathematical Methods in the Geosciences, Přibram. 3–38

  • Agterberg, F. P., 2013. Fractals and Spatial Statistics of Point Patterns. Journal of Earth Science, 24(1): 1–11. https://doi.org/10.1007/s12583-013-0305-6

    Article  Google Scholar 

  • Agterberg, F. P., 2014. Geomathematics: Theoretical Foundations, Applications and Future Developments. Springer, Heidelberg. 553

    Google Scholar 

  • Agterberg, F. P., 2017a. Pareto-lognormal Modeling of Known and Unknown Metal Resources. Natural Resources Research, 26: 3–20. https://doi.org/10.1007/s11053-016-9305-4

    Article  Google Scholar 

  • Agterberg, F. P., 2017b. Pareto-Lognormal Modeling of Known and Unknown Metal Resources. II. Method Refinement and Further Applications. Natural Resources Research, 26(3): 265–283. https://doi.org/10.1007/s11053-017-9327-6

    Article  Google Scholar 

  • Agterberg, F. P., 2018b. Statistical Modeling of Regional and Worldwide Size-Frequency Distributions of Metal Deposits. In: Daya Sagar, B. S., Cheng, Q. M., Agterberg, F. P., eds., Handbook of Mathematical Geosciences. Fifty Years of IAMG. Springer, Heidelberg. 505–527

    Chapter  Google Scholar 

  • Agterberg, F. P., 2018c. New Method of Fitting Pareto-Lognormal Size-Frequency Distributions of Metal Deposits. Natural Resources Research 27(1): 265–283

    Google Scholar 

  • Agterberg, F. P., 2020. Multifractal Modeling of Worldwide and Canadian Metal Size-Frequency Distributions. Natural Resources Research, 29(1): 539–550. https://doi.org/10.1007/s11053-019-09460-1

    Article  Google Scholar 

  • Agterberg, F. P., David, M., 1979. Statistical Exploration. In: Weiss, A., ed., Computer Methods for the 80’s. Society of Mining Engineers, New York. 30–115

    Google Scholar 

  • Agterberg, F. P., 2018a. Can Multifractals be Used for Mineral Resource Appraisal?. Journal of Geochemical Exploration, 189: 54–63. https://doi.org/10.1016/j.gexplo.2017.06.022

    Article  Google Scholar 

  • Agterberg, F. P., 1970. Autocorrelation Functions in Geology. In: Merriam, D. F., ed., Geostatistics, Plenum, New York. 113–142

    Chapter  Google Scholar 

  • Bonham-Carter, G. F., 1994. Geographic Information Systems for geoscientists: Modelling with GIS. Pergamon, Oxford. 398

    Google Scholar 

  • Carlson, C. A., 1991. Spatial Distribution of Ore Deposits. Geology, 19(2): 111–114. https://doi.org/10.1130/0091-7613(1991)019<0111:sdood>2.3.co;2

    Article  Google Scholar 

  • Cheng, Q. M., 2007. Mapping Singularities with Stream Sediment Geochemical Data for Prediction of Undiscovered Mineral Deposits in Gejiu, Yunnan Province, China. Ore Geology Reviews, 32(1/2): 314–324. https://doi.org/10.1016/j.oregeorev.2006.10.002

    Article  Google Scholar 

  • Efron, B., 1982. The Jackknife, the Bootstrap and Other Resampling Plans: SIAM, Philadelphia. 93

    Book  Google Scholar 

  • Kleiber, C., Kotz, S., 2003. Statistical Distributions in Economics and Actuarial Sciences. Wiley, Hoboken. 339

    Book  Google Scholar 

  • Lydon, J. W., 2007. An Overview of Economic and Geological Contexts of Canada’s Major Mineral Deposit Types. In: Goodfellow, M. D., ed., Mineral Deposits of Canada: A Synthesis of Major Deposit Types, District Metallogeny, the Evolution of Geological Provinces & Exploration Methods. Geological Association of Canada, Mineral Deposits Division, Special Publication No. 5, Montreal. 3–48

  • Mandelbrot, B. B., 1975. Les Objects Fractals: Forme, Hazard et Dimension. Flammarion, Paris. 346

    Google Scholar 

  • Patiño Douce, A. E., 2016a. Metallic Mineral Resources in the Twenty-First Century. I. Historical Extraction Trends and Expected Demand. Natural Resources Research, 25(1): 71–90. https://doi.org/10.1007/s11053-015-9266-z

    Article  Google Scholar 

  • Patiño Douce, A. E., 2016b. Metallic Mineral Resources in the Twenty First Century. II. Constraints on Future Supply. Natural Resources Research, 25: 97–124. https://doi.org/10.1007/s11053-015-9265-0

    Article  Google Scholar 

  • Patiño Douce, A. E., 2016c. Statistical Distribution Laws for Metallic Mineral Deposit Sizes. Natural Resources Research, 25: 365–387. https://doi.org/10.1007/s11053-016-9297-0

    Article  Google Scholar 

  • Patiño Douce, A. E., 2017. Loss Distribution Model for Metal Discovery Probabilities. Natural Resources Research, 26: 241–263. https://doi.org/10.1007/s11053-016-9315-2

    Article  Google Scholar 

  • Quandt, R. E., 1966. Old and New Methods of Estimation and the Pareto Distribution. Metrica, 10: 55–82

    Article  Google Scholar 

  • Quenouille, M., 1949. Approximate Tests of Correlation in Time Series. Journal of the Royal Statistical Society, Series B, 27: 395–449

    Google Scholar 

  • Reed, W. J., 2003. The Pareto Law of Increases: An Explanation and an Extension. Physica A., 319: 579–597

    Article  Google Scholar 

  • Reed, W. J., Jorgensen, M., 2003. The Double Pareto-Lognormal Distribution. A New Parametric Model for Size Distributions. Computational Statistics: Theory and Methods, 33(8): 1733–1753

    Google Scholar 

  • Ripley, B. D., 1976. The Second-Order Analysis of Stationary Point Processes. Journal of Applied Probability, 13: 255–266

    Article  Google Scholar 

  • Singer, D., Menzie, W. D., 2010. Quantitative Mineral Resource Assessments: An Integrated Approach. Oxford University Press, New York

    Book  Google Scholar 

  • Tukey, J. W., 1970. Some Further Inputs. In: Merriam, D. F., ed., Geostatistics. Plenum, New York. 163–174

    Chapter  Google Scholar 

  • USGS, 2015. Mineral Commodity Summaries 2015. U.S. Geological Survey, Reston

    Google Scholar 

  • Zhao, P., Hu, W., Li, Z., 1983. Statistical Prediction of Mineral Deposits. Geological Publishing House, Beijing (in Chinese)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Geological Survey of Canada, 601 Booth Street, Ottawa, K1A 0E8, Canada

    Frits Agterberg

Authors
  1. Frits Agterberg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Frits Agterberg.

Additional information

Acknowledgments

Thanks are due to Prof. Yongqing Chen and anonymous reviewers for helpful comments. The final publication is available at Springer via https://doi.org/10.1007/s12583-020-1397-4.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Agterberg, F. Aspects of Regional and Worldwide Mineral Resource Prediction. J. Earth Sci. 32, 279–287 (2021). https://doi.org/10.1007/s12583-020-1397-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12583-020-1397-4

Key Words

Search

Navigation