Analysis of gold precipitation based on point and block kriging

  • Authors

    • Nur Ali Amri Universitas Pembangunan Nasional "Veteran" Yogyakarta
    • Winda . Universitas Pembangunan Nasional "Veteran" Yogyakarta
    • Gunawan Nusanto Universitas Pembangunan Nasional "Veteran" Yogyakarta
    2019-07-15
    https://doi.org/10.14419/ijet.v7i4.21496
  • Block Kriging, Gold Vein, Point Kriging, Semivariogram.

  • Abstract

    This paper analyzes the prediction of gold distribution in veins using kriging on various block sizes. The empirical semivariogram parameters used are classical and robust, and the models used are weighted least squares and ordinary least squares based on exponential and spherical semivariogram theory. Fitting accuracy is based on the four smallest root mean square errors (RMSE), which are all obtained from the exponential base. An interesting phenomenon occurs in the theoretical exponential semivariogram-based predictions: the average value of block variance is directly proportional to the size of the widely used block. This relationship is also demonstrated by the inverse values of the validation index generated. While linked to the semivariogram parameters, the effectiveness relationship is that the length of the range of the fitting result is inversely related to the acquisition of the meanprediction.

     

  • References

    1. [1] Embrey PG, Symes RF. 1987. Minerals of Cornwall and Devon. British Museum of Natural History, London.

      [2] Roy D, Butt SD, Frempong PK. 2004. Geostatistical resource estimation for the Poura narrow-vein gold deposit. CIM Bulletin 1077: 47–51.

      [3] Welmer FW, Dalheimer M, Wagner M. 2008.Economic Evaluations in Exploration, Springer-Verlag, Berlin.

      [4] Cressie N. 2015. Statistics for Spatial Data, Revised Edition, Wiley, New York.

      [5] Olea RA. 2006. A six-step practical approach to semivariogram modeling. Stochastic Environmental Research and Risk Assessment, 20(5): 307–318. https://doi.org/10.1007/s00477-005-0026-1.

      [6] Matheron G. 1963. Principles of geostatistics. Journal of Economic Geology, 58(8): 1246–1266. https://doi.org/10.2113/gsecongeo.58.8.1246.

      [7] Cressie N,Hawkins DM. 1980. Robust estimation of the variogram. Journal of Mathematical Geology 12(2): 115–125. https://doi.org/10.1007/BF01035243.

      [8] Masseran N, RazaliAM, Ibrahim K, Zin WZW, Zaharim A. 2012. On spatial analysis of wind energy potential in Malaysia. WSEAS Journal of Transactions on Mathematics, 11(6): 451–461.

      [9] Modis K, Papaodysseus K. 2006. Theoretical estimation of the critical sampling size for homogeneous ore bodies with small nugget effect. JOurnal of Mathematical Geology38 (8): 489–501. https://doi.org/10.1007/s11004-005-9020-x.

      [10] CressieN. 1985. Fitting variogram models by weighted least squares.JOurnalof Mathematical Geology17 (5): 563–586. https://doi.org/10.1007/BF01032109.

      [11] Denison DGT, Adams NM, Holmes CC, HandDJ. 2002. Bayesian partition modelling. Journal of Computational Statistics and Data Analysis38 (4): 475–485. https://doi.org/10.1016/S0167-9473(01)00073-1.

      [12] Sarma DD. 2009. Geostatistics with Applications in Earth Sciences, Second Edition, Springer, New Delhi. https://doi.org/10.1007/978-1-4020-9380-7.

      [13] Schabenberger O, Gotway CA. 2005. Statistical Methods for Spatial Data Analysis, Chapman and Hall, Boca Raton.

      [14] Minnitt RCA.2004. Cut-off grade determination for the maximum value of a small Wits-type gold mining operation. Journal of the South African Institute of Mining and Metallurgy98: 277–283.

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  • How to Cite

    Ali Amri, N., ., W., & Nusanto, G. (2019). Analysis of gold precipitation based on point and block kriging. International Journal of Engineering & Technology, 7(4), 6921-6923. https://doi.org/10.14419/ijet.v7i4.21496

    Received date: 2018-10-10

    Accepted date: 2019-05-30

    Published date: 2019-07-15