Anisotropic Inverse Distance Weighting Method: An Innovative Technique for Resource Modeling of Vein-type Deposits

Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:

JR_JMAE-13-4_003

تاریخ نمایه سازی: 12 بهمن 1401

Abstract:

Geological modeling is an important step for the evaluation of natural resources. One option is to use a common geo-statistical modeling method such as Indicator Kriging (IK). However, there are specific problems associated with IK, the worthiest of attention is an order relation violation. Alternatively, some studies propose to use the Inverse Distance Weighting (IDW) method. Though again, there are certain limitations associated with the IDW geo-domain modeling application. In fact, the current IDW methodology does not cover the subject of anisotropic geo-domain modeling; thus it is only applicable for the isotropic cases. Therefore, this work proposes a previously unused geo-domain modeling–Anisotropic IDW, which underlies the concept of indicator variogram, allowing one to consider the spatial correlation of the domains. The experimental part in this work includes the comparison of anisotropic IDW, IK, and traditional IDW over the synthetic case study, which imitates a highly anisotropic geological behavior, and a more complicated real case study over a vein-type gold deposit from Kazakhstan. The case studies’ results illustrate that the anisotropic IDW can model the geo-domains more accurately than IK and the traditional IDW.

Keywords:

Anisotropic Inverse Distance Weighting , IDW , Categorical Variable , Geological modeling , Implicit Geomodelling

Authors

Ilyas Ongarbayev

School of Mining and Geosciences, Nazarbayev University, Nur-Sultan city, Kazakhstan

Nasser Madani

School of Mining and Geosciences, Nazarbayev University, Nur-Sultan city, Kazakhstan

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  • . Mallet, J.L. (۲۰۰۲). Geomodeling. Oxford University Press ...
  • . Thornton, J.M., Mariethoz, G., and Brunner, P. (۲۰۱۸). A ...
  • . Sides, E.J. (۱۹۹۷). Geological modelling of mineral deposits for ...
  • . Cowan, E.J., R.K. Beatson, H.J. Ross, W.R. Fright, T.J. ...
  • . Turner, A.K. (۲۰۰۶). Challenges and trends for geological modelling ...
  • . Shepard, D. (۱۹۶۸). A two-dimensional interpolation function for irregularly-spaced ...
  • . Journel, A.G. (۱۹۸۳). Nonparametric estimation of spatial distributions. Journal ...
  • . Broomhead, D.S. and Lowe, D. (۱۹۸۸). Radial basis functions, ...
  • . Deutsch, C.V. and Journel, A.G. (۱۹۹۸). GSLib. Geostatistical software ...
  • . Madani, N., Maleki, M., and Sepidbar, F. (۲۰۲۱). Integration ...
  • . Marinoni, O. (۲۰۰۳). Improving geological models using a combined ...
  • . Babak, O. (۲۰۱۴). Inverse distance interpolation for facies modeling. ...
  • . Yasrebi, A.B., Afzal, P., Wetherelt, A., Foster, P., Madani, ...
  • . Yasrebi, A.B., Hezarkhani, A., Afzal, P., and Madani, N. ...
  • . Pyrcz, M.J. and Deutsch, C.V. (۲۰۱۴). Geostatistical reservoir modeling. ...
  • Goovaerts, P. (۱۹۹۷). Geostatistics for natural resources evaluation. Oxford University ...
  • . Armstrong, M., Galli, A., Beucher, H., Loc'h, G., Renard, ...
  • . Madani N. (۲۰۲۱) Plurigaussian Simulations. In: Daya Sagar B., ...
  • . Rossi, M.E. and Deutsch, C.V. (۲۰۱۳). Mineral resource estimation. ...
  • . Maleki, M. and Emery, X. (۲۰۲۰). Geostatistics in the ...
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