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A Simulated Annealing-Based Algorithm to Locate Additional Drillholes for Maximizing the Realistic Value of Information

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Abstract

Locating additional drillholes based on information gathered from the initial drilling is a very difficult decision-making step in the process of detailed explorations. The most appropriate locations for additional drillholes are those wherein the information gathered from drilling has more value compared to that from other locations. From among the common methods proposed in information systems for measuring the information value, use of the “realistic value” is a very practical one. The realistic value of information is derived from measuring the differences in the decision makers’ performances when provided with different information sets. On this basis, a mathematical model has been proposed in this paper for optimal location of additional drillholes where the information gathered from drillholes has the highest possible value. Due to the combinatorial nature of this model, use has been made of a simulated annealing-based algorithm for its solution. The proposed model has been applied in Sungun copper deposit for locating additional drillholes; results have revealed that the model is valid.

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References

  • Botin, J. A. (2009). Sustainable management of mining operations. Littleton, CO: Society for Mining, Metallurgy and Exploration.

    Google Scholar 

  • CMMI. (1996). Mineral resource/reserve classification: Categories, definitions and guidelines. A. H. C. Report, CIM Bulletin, Vol. 89, pp. 39–44.

  • Debba, P., Carranza, E. J. M., Stein, A., & Van der Meer, F. D. (2009). Deriving optimal exploration target zones on mineral prospectivity maps. Mathematical Geosciences, 41, 421–446.

    Article  Google Scholar 

  • Froyland, G., Menabde, M., et al. (2007). The value of additional drilling to open pit mining projects. In R. Dimitrakopoulos (Ed.), Orebody modelling and strategic mine planning (pp. 225–232). Melbourne: The Australasian Institute of Mining and Metallurgy.

    Google Scholar 

  • Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721–741.

    Article  Google Scholar 

  • Gershon, M., Allen, L. E., et al. (1988). Application of a new approach for drillholes location optimization. International Journal of Surface Mining, Reclamation, and Environment, 2(1), 27–31.

    Article  Google Scholar 

  • JORC. (1999). Australasian code for reporting of mineral resources and ore reserves. Resource document. Report of the joint committee of the AusIMM and AIG and Mineral council of Australia. Retrieved December 2012 from www.jorc.org/docs/jorc_code2012.pdf.

  • Kirkpatrick, S., Gelatt, C. D., et al. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.

    Article  Google Scholar 

  • Mwasinga, P. P. (2001). Approaching resource classification: General practices and the integration of geostatistics. In H. M. Xie & Y. Wang (Eds.), Computer applications in the minerals industry (pp. 97–104). London: Taylor & Francis Group.

    Google Scholar 

  • Nahar, S., Sahni, S., & Shragowitz, E., (1985). Experiments with simulated annealing. In H. Ofek, L. A. O’Neill (Eds.), 22th ACM/IEEE Conference on Design Automation (pp. 748–752). Nevada, United States.

  • Saikia, K., & Sakara, B. C. (2006). Exploration drilling optimization using geostatistics: A case in Jharia Coalfield, India. Applied Earth Science, 115(1), 13–22.

    Article  Google Scholar 

  • SAMAREC. (2000). South African code for reporting mineral resource and ore reserves. Resource document. South African Institute of Mining and Metallurgy. Retrieved June 2009 from www.samcode.co.za.

  • Scheck, D., & Chou, D.-R. (1983). Optimum locations for exploratory drill holes. International Journal of Mining Engineering, 1(4), 343–355.

    Article  Google Scholar 

  • Sinclair, A. J., & Blackwell, G. H. (2000). Resource/reserve classification and the qualified person. Bulletin of the Canadian Institute of Mining and Metallurgy, 93(1038), 29–35.

    Google Scholar 

  • Soltani, S., & Hezarkhani, A. (2009). Additional exploratory boreholes optimization based on three-dimensional model of ore deposit. Archives of Mining Science, 54(3), 495–506.

    Google Scholar 

  • Soltani, S., & Hezarkhani, A. (2011). Determination of realistic and statistical value of the information gathered from exploratory drilling. Natural Resources Research, 20(4), 207–216.

    Article  Google Scholar 

  • Soltani, S., & Hezarkhani, A. (2013). Proposed algorithm for optimization of directional additional exploratory drill holes and computer coding. Arabian Journal of Geosciences, 6(2), 455–462.

    Article  Google Scholar 

  • Soltani, S., Hezarkhani, A., et al. (2011a). Optimally locating additional drill holes in three dimensions using grade and simulated annealing. Journal of the Geological Society of India, 80(5), 700–706.

    Google Scholar 

  • Soltani, S., Hezarkhani, A., et al. (2011b). Use of genetic algorithm in optimally locating additional drill holes. Journal of Mining Science, 47(1), 62–72.

    Article  Google Scholar 

  • Szidarovszky, F. (1983). Multi-objective observation network design for regionalized variables. International Journal of Mining Engineering, 1(4), 331–342.

    Article  Google Scholar 

  • UN-ECE. (1996). United Nations international framework classification for reserves/resources solid fuels and mineral commodities. Genève: United Nations Publications.

  • van Groenigen, J. W., & Stein, A. (1998). Constrained optimization of spatial sampling using continuous simulated annealing. Journal of Environmental Quality, 27(5), 1078–1086.

    Article  Google Scholar 

  • Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists. Chichester: Wiley.

    Book  Google Scholar 

  • Wellmer, F. W. (1983). Classification of ore reserves by geostatistical methods. Erzmetall, 36(7/8), 315–321.

    Google Scholar 

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Acknowledgments

We are indebted to Dr. John Carranza at James Cook University for his critical and careful review of a preliminary version of this manuscript. We also appreciate constructive comments from one anonymous reviewer.

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Correspondence to Saeed Soltani-Mohammadi.

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Soltani-Mohammadi, S., Hezarkhani, A. A Simulated Annealing-Based Algorithm to Locate Additional Drillholes for Maximizing the Realistic Value of Information. Nat Resour Res 22, 229–237 (2013). https://doi.org/10.1007/s11053-013-9212-x

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  • DOI: https://doi.org/10.1007/s11053-013-9212-x

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