Efficiency of Ni Content in Laterite Nickel Deposits through The Least Square Method Approach on Semivariogram

Abstract

Ordinary kriging is one of the geostatistical techniques used for spatial prediction on a spatially distributed random plane. Ordinary kriging is a linear unbiased estimator which is part of a semivariogram system of equations that minimizes errors of variance in estimating mineral resources. The semivariogram model shows optimal results in the estimation using the least square method, the effective minimization method smoothes the data points against the curve on a semivariogram graph, the least square makes the size error efficient in the semivariogram model and has been proven to be effective in reducing errors in the semivariogram model in the case of laterite nickel deposits. at PT. Vale Indonesia Tbk. Thus, conclusively the prediction of unsampled Ni content results is very accurate. This is indicated by the lowest root mean square error (RMSE) in limonite in the exponential model, saprolite in the spherical model, and bedrock in the gaussian model. The greatest value of Ni content in this study was in the saprolite layer.

Application of a semivariogram based on a deep neural network to Ordinary Kriging interpolation of elevation data

“…In OK interpolation, the optimal selection is generally obtained by comparing many semivariogram models [8][9][10][11][12], and then the optimal model is obtained by manual parameter adjustment. To improve the goodness of fit of the semivariogram model, the least square method [13][14][15], the maximum likelihood method [16] and the maximum-minimum method [17] can be used to fit the semivariogram function. At present, there are two problems related to semivariogram fitting methods.…”