Estimation of Ag and Cu grades using Au assay data through statistical and geostatistical approaches for three-dimensional grade modelling

In this study, the estimation of Ag and Cu grades was performed using Au assay data in the Zarzima deposit, Kurdistan, Iran. To achieve the goal, statistical approaches including non-parametric (Cubic Spline interpolation) and parametric (regression) curve fitting and geostatistical cokriging were employed. Based on the results, grade estimation using the Cubic Spline interpolation demonstrates higher accuracy than the regression approach. Therefore, 3D grade modelling of Ag and Cu was subsequently carried out after data completion using Spline and cokriging methods. A comparison of the 3D Ag grade models indicates that the overall trend of grade variations is consistent between the two models; however, significant discrepancies are observed between them, particularly in the near-surface sections of the deposit. Furthermore, the 3D Cu grade models reveal that the geometric configuration and the spatial extent of the mineralised zone differ between the two models. Through the application of the employed approaches, the increase in the number of known data points enabled the development of robust 3D models for Ag and Cu. In general, when a large number of auxiliary data are available, as in the present study, cokriging estimation – by accounting for the spatial and distance-based relationships among the data – provides a higher level of reliability. As a basis for comparison, the variances of the estimated Ag and Cu data obtained using the statistical interpolation method show increases of 6.25% and 2.87%, respectively, relative to those obtained by cokriging. These models also demonstrate the potential for generalisation and applicability to other similar mineral deposits.