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Original Articles

How different data supports affect geostatistical modelling: the new aggregation method and comparison with the classical regularisation and the theoretical punctual model

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Pages 34-54 | Received 31 May 2018, Accepted 01 Aug 2018, Published online: 06 Sep 2018

References

  • D.A. Tarasov, A.G. Buevich, and A.V. Shichkinab, High variation topsoil pollution forecasting in the Russian Subarctic: Using artificial neural networks combined with residual kriging, J. Appl. Geochem. 88 (2018), pp. 188–197. doi:10.1016/j.apgeochem.2017.07.007
  • H. Mohammadi, A. Seifi, and T. Foroud, A robust Kriging model for predicting accumulative outflow from a mature reservoir considering a new horizontal well, J. Pet Sci. Eng. 82–83 (2012), pp. 113–119. doi:10.1016/j.petrol.2012.01.004
  • S. Choudhury, Comparative study on linear and non-linear geo-statistical estimation methods: A case study on iron deposit, J. PROCEDIA Earth Planet Sci. 11 (2015), pp. 131–139. doi:10.1016/j.proeps.2015.06.017[4]
  • J.P. Chiles and P. Delfiner, Geostatistics Modeling Spatial Uncertainty, 2th ed., ISBN: 978-0-470-18315-1, WILEY, Hoboken, NJ, 2012.
  • G. Matheron, The Theory Of Regionalized Variables And Its Application. Mathematical Statistics, École Nationale Supérieure des Mines, Paris, 1971.
  • M. Guarascio and G. Raspa, Valuation and production optimization of a metal mine, Proceedings of the 12 International APCOM Symposium, Golden, Colorado, 2: 50–64, 1974.
  • I. Clark, Regularization of a semivariograms, J. Comput. Geosci 3 (1977), pp. 341–346. doi:10.1016/0098-3004(77)90010-3
  • P. Atkinson and N. Tate, Spatial scale problems and geostatistical solutions: A review, J. Prof. Geogr. 52 (2000), pp. 607–623. doi:10.1111/0033-0124.00250
  • P. Carrasco, J.P. Chiles, and S. Seguret, Additivity, Metallurgical Recovery and Grade, 8th international Geostatistics Congress, Santiago, Chile, 2008.
  • S. Kasmaee and F. Torab, Risk reduction in Sechahun iron ore deposit by geological boundary modification using multiple indicator Kriging, J. Cent. South. Univ. 21, 5 (2014), pp. 2011–2017. doi:10.1007/s11771-014-2150-x
  • M.A. Bassani and J. Costa, Grade estimation with samples of different length with samples of different length, J. Appl. Earth Sci. 125, 4 (2016), pp. 1–6. doi:10.1080/03717453.2016.1194947
  • A.G. Journel and C.J. Huijbregts, Mining Geostatistics, Academic Press, London, 1991.
  • M. Armstrong, Basic Linear Geostatistics, Springer Berlin Heidelberg, Berlin, 1998.
  • H. Wackernagel, Multivariate Geostatistics An Introduction With Applications, Springer Berlin Heidelberg, Berlin, 2003.
  • C.A. Gotway and L.J. Young, Combining incompatible spatial data, J. Am. Stat. Assoc. 97 (459) (2002), pp. 632–648. doi:10.1198/016214502760047140
  • C.A. Gotway and L.J. Young, A geostatistical approach to linking geographically-aggregated data from different sources, Technical report, Department of Statistics, University of Florida, 2004, pp. 012
  • P.A. Kyriakidis, Geostatistical framework for area-to-point spatial interpolation, Geogr. Anal. 36 (2) (2004), pp. 259–289. doi:10.1111/j.1538-4632.2004.tb01135.x
  • P. Goovaerts, Geostatistical analysis of disease data: Accounting for spatial support and population density in the isopleth mapping of cancer mortality risk using area-to-point Poisson kriging, Int. J. Health Geogr. 5, 52 (2006), doi:10.1186/1476-072X-5-52
  • C.J. Morgan, Theoretical and practical aspects of variography: In particular, estimation and modelling of semi-variograms over areas of limited and clustered or widely spaced data in a two dimensional South African gold mining context, Ph.D. thesis, University of the Witwatersrand, Johannesburg, 2011.
  • P. Goovaerts, Kriging and semivariogram deconvolution in the presence of irregular geographical units, Math. Geol. 40 (1) (2008), pp. 101–128.
  • M.A. Bassani, J. Costa, W. Guaglianoni, and R. Rubio, Comparison between the indirect approach and kriging with samples of different support for estimation using samples of different length, Stoch. Environ. Res. Risk Assess. 32 (2018), pp. 785–797. doi:10.1007/s00477-017-1398-8
  • D. Marcotte, Generalized cross-validation for covariance model selection and parameter estimation, Math. Geol. 27 (6) (1995), pp. 749–762. doi:10.1007/BF02273536
  • R. Pan, T. Yang, J. Cao, K. Lu, and Z. Zhang, Missing data imputation by K nearest neighbours based on grey relational structure and mutual information, Appl. Intell. 43 (3) (2015), pp. 614–632.
  • J. Rivoirard, Two key parameters when choosing the kriging neighborhood, J. Math. Geo. 19 (8) (1987), pp. 851–856. doi:10.1007/BF00893020
  • I. Clark, The Art of Cross Validation in Geostatistical Applications, Society of Mining Engineers, Inc, Littleton Colorado, 1986. Chapter 20, 211–220.
  • M. Oliver and R. Webster, A tutorial guide to geostatistics: Computing and modelling variograms and kriging, J. Catena. 113 (2014), pp. 56–69. doi:10.1016/j.catena.2013.09.006
  • C.H. Koelbel, High Performance Fortran Handbook, FORTRAN, MIT Press, Cambridge,  ISBN. 978-0-262-29123-1, 1993.
  • P. Darling, SME Mining Engineering Handbook, 3rd ed., Vol. V1, Society for Mining, Metallurgy and Exploration, Englewood, Colorado, 2001.

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