Authors Arman Mohammadi Gonbadi, Seyed Hasan Tabatabaei, Nader Fathianpour
Journal Computers & Geosciences Volume 129 Pages 69-81 Publisher Pergamon
Description: Ore grade estimation plays an important role in recoverable resource calculations and mining projects. Classic geostatistical estimation methods are based on two-point statistics and cannot make use of multiple-point and higher-order statistics of the data. Very few studies have been conducted on determination of the possible effects of these features on the results of ore grade estimation. In view of this, the present study introduced a new multiple-point interpolation method based on the implicit Volterra series. In this regard, least square support vector machines (LS-SVM) were used to implicitly estimate the Volterra series coefficients. Regularized risk minimization in the LS-SVM can increase generalization of the Volterra series and reduce sensitivity of these series to data noise. Also, conjugate gradient iterative algorithm was used to solve the linear equations of the implicit Volterra series estimation problem. This …