Selection of a Resource Estimation Method for Monywa K and L Copper Deposits in Myanmar


Realization of good returns in the mining venture needs careful planning, scheduling, design and optimization of all mining activities which are dependent upon reliable resource estimates. The mineral resource estimation method employed in a deposit thus plays a major role in reduction of risks associated in mining. In this study, indicator kriging, ordinary kriging and inverse distance weighting methods are compared for Monywa K and L deposits. Correlation coefficients in the regression analysis of downhole composites compared with the ordinary kriging estimates for K and L deposits were 0.982 and 0.985 respectively, thus selecting it as the best estimator for the two deposits.

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  1. 1.

    Emery, J., Estimation of Mineral Resources Using Grade Domains: Critical Analysis and a Suggested Methodology, J. S. Afr. Inst. Min. Metall., 2005, vol. 105, no. 4, pp. 247–255.

    Google Scholar 

  2. 2.

    Glacken, I., Snowden, D., and Edwards, A., Mineral Resource Estimation, Mineral Resource and Ore Reserve Estimation—The Ausimm Guide to Good Practice, The Australasian Institute of Mining and Metallurgy, Melbourne, 2001, vol. 23, no. 1, pp. 189–198.

    Google Scholar 

  3. 3.

    Morley, D., Financial Impact of Resource/Reserve Uncertainty, J. S. Afr. Inst. Min. Metall., 1999, vol. 99, no. 6, pp. 293–301.

    Google Scholar 

  4. 4.

    Kiš, I.M., Comparison of Ordinary and Universal Kriging Interpolation Techniques on a Depth Variable (A Case of Linear Spatial Trend), Case Study of the Š and rovac Field, Rudarsko-Geološko-Naftni Zbornik, 2016, vol. 31, no. 2, pp. 41–58.

    Google Scholar 

  5. 5.

    Wackernagel, H., Multivariate Geostatistics, Springer, 2003, pp. 79–88.

  6. 6.

    Ozturk, D. and Kilic, F., Geostatistical Approach for Spatial Interpolation of Meteorological Data, An. Acad. Bras. Ciênc., 2016, vol. 88, no. 4, pp. 2121–2136.

    Article  Google Scholar 

  7. 7.

    Al-Hassan, S. and Boamah, E., Comparison of Ordinary Kriging and Multiple Indicator Kriging Estimates of Asuadai Deposit at Adansi Gold Ghana Limited, Ghana Min. J., 2015, vol. 15, no. 2, pp. 42–49.

    Google Scholar 

  8. 8.

    Sinclair, A.J. and Blackwel, I. G.H., Applied Mineral Inventory Estimation, Cambridge University Press, 2002.

  9. 9.

    Lin, Y.-P., Chang, T.-K., Shih, C.-W., and Tseng, C.-H., Factorial and Indicator Kriging Methods Using a Geographic Information System to Delineate Spatial Variation and Pollution Sources of Soil Heavy Metals, Environ. Geol., 2002, vol. 42, no. 8, pp. 900–909.

    Article  Google Scholar 

  10. 10.

    Rahimi, H., Asghari, O., Hajizadeh, F., and Meysami, F., Assessment the Number of Thresholds on Tonnage-Grade Curve in IK Estimation. Case Study: Qolqoleh Gold Deposit (NW of Iran), 4th Int. Mine & Mining Industries Congr. & Expo, 2016.

  11. 11.

    Mei, G., Xu, L., and Xu, N., Accelerating Adaptive Inverse Distance Weighting Interpolation Algorithm on a Graphics Processing Unit, R. Soc. Open Sci., 2017, vol. 4, no. 9, pp. 1–19.

    Article  Google Scholar 

  12. 12.

    Li, L., Losser, T., Yorke, C., and Piltner, R., Fast Inverse Distance Weighting-Based Spatiotemporal Interpolation: A Web-Based Application of Interpolating Daily Fine Particulate Matter PM2. 5 in the Contiguous US Using Parallel Programming and KD Tree, Int. J. Env. Res. Public Health, 2014, vol. 11, no. 9, pp. 9101–9141.

    Article  Google Scholar 

  13. 13.

    Bronshtein, I.N., Handbook of Mathematics, Springer, 2004.

  14. 14.

    Samal, A.R., Sengupta, R.R., and Fifarek, R.H., Modeling Spatial Anisotropy of Gold Concentration Data Using GIS-Based Interpolated Maps and Variogram Analysis: Implications for Structural Control of Mineralization, J. Earth Syst. Sci., 2011, vol. 120, no. 4, pp. 583–593.

    Article  Google Scholar 

  15. 15.

    Rossi, M.E. and Deutsch, C.V., Mineral Resource Estimation, Springer Netherlands, 2013.

    Google Scholar 

  16. 16.

    Glacken, I. and Blackney, P., A Practitioners Implementation Of Indicator Kriging, Beyond Ordinary Kriging, 1998.

  17. 17.

    Silva, F. and Soares, A., Grade-Tonnage Curve: How Far Can It Be Relied Upon, Annual Conf. of the Int. Association for Math. Geology, Cancún, 2001, pp. 1–11.

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The authors are grateful to the CRSRI Open Research Program, SN : CKWV2018471/KY, the National Nature Science Foundation of China, grants nos. 51804235 and 41672320, and the National Key R&D Plan, grant no. 2018YFC0808405 for their financial support.

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Correspondence to H. Gang.

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Published in Fiziko-Tekhnicheskie Problemy Razrabotki Poleznykh Iskopaemykh, 2020, No. 1, pp. 92–103.

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Mwangi, A.D., Jianhua, Z., Innocent, M.M. et al. Selection of a Resource Estimation Method for Monywa K and L Copper Deposits in Myanmar. J Min Sci 56, 84–95 (2020).

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  • Indicator kriging
  • ordinary kriging
  • inverse distance weighting
  • resource estimation