A non-stationary geostatistical approach to multigaussian kriging for local reserve estimation |
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| Authors: | Email authorView author&#;s OrcID profile" target="_blank">Mainak?ThakurEmail authorView author&#;s OrcID profile Biswajit?Samanta Debashish?Chakravarty |
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| Institution: | 1.Institut de recherche mathématique de Rennes (IRMAR - UMR CNRS 6625),Université de Rennes 1 Beaulieu,Rennes,France;2.Department of Mining Engineering,Indian Institute of Technology,Kharagpur,India |
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| Abstract: | Multigaussian kriging technique has many applications in mining, soil science, environmental science and other fields. Particularly, in the local reserve estimation of a mineral deposit, multigaussian kriging is employed to derive panel-wise tonnages by predicting conditional probability of block grades. Additionally, integration of a suitable change of support model is also required to estimate the functions of the variables with larger support than that of the samples. However, under the assumption of strict stationarity, the grade distributions and important recovery functions are estimated by multigaussian kriging using samples within a supposedly spatial homogeneous domain. Conventionally, the underlying random function model is required to be stationary in order to carry out the inference on ore grade distribution and relevant statistics. In reality, conventional stationary model often fails to represent complicated geological structure. Traditionally, the simple stationary model neither considers the obvious changes in local means and variances, nor is it able to replicate spatial continuity of the deposit and hence produces unreliable outcomes. This study deals with the theoretical design of a non-stationary multigaussian kriging model allowing change of support and its application in the mineral reserve estimation scenario. Local multivariate distributions are assumed here to be strictly stationary in the neighborhood of the panels. The local cumulative distribution function and related statistics with respect to the panels are estimated using a distance kernel approach. A rigorous investigation through simulation experiments is performed to analyze the relevance of the developed model followed by a case study on a copper deposit. |
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