Skip to main content
Log in

Spatial Prediction of Lateral Variability of a Laterite-Type Bauxite Horizon Using Ancillary Ground-Penetrating Radar Data

  • Published:
Natural Resources Research Aims and scope Submit manuscript


Tropical laterite-type bauxite deposits often pose a unique challenge for resource modelling and mine planning due to the extreme lateral variability at the base of the bauxite ore unit within the regolith profile. An economically viable drilling grid is often rather sparse for traditional prediction techniques to precisely account for the lateral variability in the lower contact of a bauxite ore unit. However, ground-penetrating radar (GPR) offers an inexpensive and rapid method for delineating laterite profiles by acquiring fine-scale data from the ground. These numerous data (secondary variable) can be merged with sparsely spaced borehole data (primary variable) through various statistical and geostatistical techniques, provided that there is a linear relation between the primary and secondary variables. Four prediction techniques, including standard linear regression, simple kriging with varying local means, co-located cokriging and kriging with an external drift, were used in this study to incorporate exhaustive GPR data in predictive estimation the base of a bauxite ore unit within a lateritic bauxite deposit in Australia. Cross-validation was used to assess the performance of each technique. The most robust estimates are produced using ordinary co-located cokriging in accordance with the cross-validation analysis. Comparison of the estimates against the actual mine floor indicates that the inclusion of ancillary GPR data substantially improves the quality of the estimates representing the bauxite base surface.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

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
Figure 15

Similar content being viewed by others


  • Abzalov, M. Z., & Bower, J. (2009). Optimisation of drill grid at the Weipa bauxite deposit using conditional simulation. Paper presented at the Seventh International Mining Geology Conference, Melbourne.

  • Ahmed, S., & de Marsily, G. (1987). Comparison of geostatistical methods for estimating transmissivity using data on transmissivity and specific capacity. Water Resources Research, 23, 1717–1737.

    Article  Google Scholar 

  • Armstrong, M. (1984). Problems with universal kriging. Mathematical Geology, 16(1), 101–108.

    Article  Google Scholar 

  • ASTM D 5923-96. (2004). Standard guide for selection of kriging methods in geostatistical site investigation (pp. 443–446). West Conshohocken, PA: ASTM International.

  • ASTM D 5924-96. (2004). Standard guide for selection of simulation approaches in geostatistical site investigations (pp. 447–449). West Conshohocken, PA: ASTM International.

  • ASTM D 6432-99. (2005). Standard guide for using the surface ground penetrating radar method for subsurface investigation (pp. 984–1000). West Conshohocken, PA: ASTM International.

  • Baker, G. S., Jordan, T. E., & Pardy, J. (2007). An introduction to ground penetrating radar (GPR). In G. S. Baker, & H. M. Jol (Eds.), Stratigraphic analysis using GPR (Vol. 432, pp. 1–18). Geological Society of America Special Paper. Boulder, CO: Geological Society of America.

  • Bardossy, G., & Aleva, G. J. J. (1990). Lateritic bauxites. Amsterdam: Elsevier.

    Google Scholar 

  • Bardossy, A., Bogardi, I., & Kelly, W. E. (1986). Geostatistical analysis of geoelectric estimates for specific capacity. Journal of Hydrology, 84, 81–95.

    Article  Google Scholar 

  • Barsottelli-Botelho, M. A., & Luiz, J. G. (2011). Using GPR to detect bauxite horizons in laterite deposits of Amazon Basin, Brazil. Paper presented at the 73rd European Association of Geoscientists and Engineers, Vienna.

  • Bourennane, H., & King, D. (2003). Using multiple external drifts to estimate a soil variable. Geoderma, 114, 1–18.

    Article  Google Scholar 

  • Chihi, H., Galli, A., Ravenne, C., Tesson, M., & de Marsily, G. (2000). Estimating the depth of stratigraphic units from marine seismic profiles using nonstationary geostatistics. Natural Resources Research, 9(1), 77–95.

    Article  Google Scholar 

  • Chiles, J. P. (1984). Simulation of a nickel deposit: Problems encountered and practical solutions. In G. Verly (Ed.), Geostatistics for natural resources characterisation (pp. 1015–1030). Boston: D. Reidel.

    Chapter  Google Scholar 

  • Chiles, P. J., & Delfiner, P. (1999). Geostatistics modelling spatial uncertainty. New York: Wiley.

    Book  Google Scholar 

  • Davis, J. C. (2002). Statistics and data analysis in geology. New York: Wiley.

    Google Scholar 

  • Davis, J. L., & Annan, A. P. (1989). Ground-penetrating radar for high-resolution mapping of soil and rock stratigraphy. Geological Prospecting, 37, 531–551.

    Article  Google Scholar 

  • Dowd, P. A., & Pardo-Iguzquiza, E. (2006). Core–log integration: Optimal geostatistical signal reconstruction from secondary information. Applied Earth Science (Transactions of the Institution of Mining and Metallurgy Section B), 115(2), 59–70.

    Article  Google Scholar 

  • Doyen, P. (1988). Porosity from seismic data: A geostatistical approach. Geophysics, 53(10), 1263–1275.

    Article  Google Scholar 

  • Eggleton, T., & Taylor, G. (2005). Bioturbation of the Weipa bauxite. Paper presented at the Regolith 2005—Ten Years of CRC LEME, Canberra.

  • Erten, O. (2012). Profiling and mining control to mitigate dilution effect from SiO 2 at the base of a bauxite deposit. Brisbane: The University of Queensland.

    Google Scholar 

  • Evans, H. J. (1965). Bauxite deposits of Weipa. Paper presented at the 8th Commonwealth Mining & Metallurgical Congress Australia and New Zealand, Melbourne.

  • Evans, H. J. (1975). Weipa bauxite deposit, Queensland. Paper presented at the Economic Geology of Australia and Papua New Guinea, Melbourne.

  • Francke, J. C. (2012a). A review of selected ground penetrating radar applications to mineral resource evaluations. Journal of Applied Geophysics, 81, 29–37.

    Article  Google Scholar 

  • Francke, J. C. (2012b). The role of ground penetrating radar in bauxite resource evaluations. Paper presented at the 14th International Conference on Ground Penetrating Radar (GPR), Shanghai.

  • Francke, J. C., & Nobes, D. C. (2000). A preliminary evaluation of GPR for nickel laterite exploration. Paper presented at the Eighth International Conference on Ground Penetrating Radar, San Diego.

  • Francke, J. C., & Parkinson, G. (2000). The new role of geophysics in nickel laterite exploitation and development. Paper presented at the Mining Millennium/PDAC Convention, Toronto.

  • Francke, J. C., & Utsi, V. (2009). Advances in long-range GPR systems and their applications to mineral exploration, geotechnical and static correction problems. First Break, 27, 85–93.

    Google Scholar 

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

    Google Scholar 

  • Goovaerts, P. (1999). Geostatistics in soil science: State-of-the-art and perspectives. Geoderma, 89, 1–45.

    Article  Google Scholar 

  • Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228, 113–129.

    Article  Google Scholar 

  • Goovaerts, P., & Kerry, R. (2010). Using ancillary data to improve prediction of soil and crop attributes in prediction agriculture. In M. A. Oliver (Ed.), Geostatistical applications for precision agriculture (pp. 167–193). New York: Springer.

    Chapter  Google Scholar 

  • Grimes, K. G. (1979). The stratigraphic sequence of old land surfaces in northern Queensland. Bureau of Mineral Resources Australian Journal of Geology and Geophysics, 4, 33–46.

    Google Scholar 

  • Grubb, P. L. C. (1971). Genesis of the Weipa bauxite deposits, N.E. Australia. Mineralium Deposita, 6, 265–274.

    Article  Google Scholar 

  • Hartman, H. L., & Mutmansky, J. M. (2002). Introductory mining engineering. New York: Wiley.

    Google Scholar 

  • Isaaks, E. H., & Srivastava, R. H. (1989). An introduction to applied geostatistics. New York: Oxford University Press.

    Google Scholar 

  • Journel, A. G. (1974). Geostatistics for conditional simulations of ore bodies. Economical Geology, 69(5), 673–687.

    Article  Google Scholar 

  • Journel, A. G., & Huijbregts, C. J. (1978). Mining geostatistics. London: Academic press.

    Google Scholar 

  • Journel, A. G., Kyriakidis, P. C., & Mao, S. (2000). Correcting the smoothing effect of estimators: a spectral postprocessor. Mathematical Geology, 32(7), 787–813.

    Article  Google Scholar 

  • Keary, P., & Brooks, M. (1994). An introduction to geophysical exploration (2nd ed.). Oxford: Blackwell.

    Google Scholar 

  • Loughnan, F. C., & Bayliss, P. (1961). The mineralogy of the bauxite deposits near Weipa, Queensland. The American Mineralogist, 46, 209–217.

    Google Scholar 

  • Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58, 1246–1266.

    Article  Google Scholar 

  • Matheron, G. (1971). The theory of regionalised variables and its applications. Cahiers du Centre de Morphologie Mathématique, 5, 211.

    Google Scholar 

  • McLennan, J. A., Ortiz, J. M., & Deutsch, C. V. (2006). Geostatistical optimum mining elevations for nickel laterite deposits. CIM Magazine, 1(6), 1–9.

    Google Scholar 

  • Morgan, C. M. (1992). Geology of the spheres at Weipa. Paper presented at the 7th International Congress of Icsoba, Balatonalmadi.

  • Morgan, M. (1995). An investigation into the application of ground penetrating radar to the Weipa mining operation. Weipa: Comalco Minerals and Alumina.

    Google Scholar 

  • Morgan, M. (1996). The geology of the Comalco bauxite resource. A beginner’s guide. Weipa: Comalco Minerals and Alumina.

    Google Scholar 

  • Odeh, I. O. A., McBratney, A. B., & Chittleborough, D. J. (1995). Further results on prediction of soil properties from terrain attributes: Heterotopic cokriging and regression-kriging. Geoderma, 67, 215–226.

    Article  Google Scholar 

  • Rivoirard, J. (2002). On the structural link between variables in kriging with external drift. Mathematical Geology, 34(7), 797–808.

    Article  Google Scholar 

  • Taylor, G., & Eggleton, R. A. (2008). Genesis of pisoliths and of the Weipa bauxite deposit, northern Australia. Australian Journal of Earth Sciences, 55, 87–103.

    Article  Google Scholar 

  • Wackernagel, H. (2002). Multivariate geostatistics. Berlin: Springer.

    Google Scholar 

  • Watts, A. (1997). Exploring for nickel in the 90s, or ‘Til depth us do part’. Paper presented at the Exploration 97: Fourth Decennial International Conference on Mineral Exploration, Toronto.

  • Xu, W., Tran, T. T., Srivastava, R. M., & Journel, A. G. (1992). Integrating seismic data in reservoir modelling: the collocated cokriging alternative. In 67th Annual Technical Conference and Exhibition. Society of Petroleum Engineers, Washington, DC.

  • Yamamoto, J. K. (2008). Estimation or simulation? That is the question. Computers & Geosciences, 12, 573–591.

    Article  Google Scholar 

Download references


The case study reported in this article was funded by the Rio Tinto Alcan, Australia. The authors thank Jan Francke, Michael Mills and Glenn White for their advice and support.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Oktay Erten.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Erten, O., Kizil, M.S., Topal, E. et al. Spatial Prediction of Lateral Variability of a Laterite-Type Bauxite Horizon Using Ancillary Ground-Penetrating Radar Data. Nat Resour Res 22, 207–227 (2013).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: