The present study has focused on the performance of the evidential belief functions (EBFs) model to predict the three dimensional (3D) mineralization of copper (Cu) at Kahang porphyry Cu deposit (KPCD) in the northeast of Isfahan, Iran. The 3D deposit modeling and reserve estimation are of the most important steps in mineral exploration program. Determining the boundaries of the deposit and estimating the quantity and quality of reserves are among the most essential issues in mining designing. Normally, a 3D model of the sought deposit is prepared based on the mineral grade parameter without considering other parameters and deposit information. In this regard, 3D integration models at the deposit scale facilitate the consideration of other parameters and available underground information. The results of these 3D integration models are mostly used to introduce mineral prospectivity areas. However, in this study, the integrated model was used as an auxiliary parameter in estimation for the geostatistical co-kriging method. Of note is that the accuracy of the 3D model of the deposit improved whereas the estimation error demonstrated a reduction. . The information of 22 boreholes drilled by the National Iranian Copper Industry Company was used in this research. To implement resource modeling, the number of blocks in the eastern, northern and depth directions were 30 × 28 × 145, respectively and the size of each block was designed equal to15 × 15 × 5 m. Auxiliary geospatial models were divided into three main categories of geological, geochemical, and geophysical data sets, and all 3D evidential models were obtained quantitatively. Subsequently, the Dempster-Shafer evidence theory was applied to construct a 3D prospectivity modeling of Kahang deposit. Using this knowledge-driven theory, four 3D models of belief, uncertainty, plausibility, and disbelief were produced, which have substantial impact on more accurate Cu resource modeling. After validation of the prospectivity models with the drilling results, the desired 3D auxiliary model had a correlation of 0.92 with the Cu grades. Thus, the Cu grade was estimated by a co-kriging method using a 3D EBFs model as an auxiliary variable, and output was compared with the one from an ordinary kriging (OK) method. The significance of this study lies in improvement of the performance of the Cu grade modeling through an auxiliary variable, where the root-mean-square error (RMSE) of the estimates reduces from 0.437 to 0.288 when implementing a co-kriging method rather than the ordinary kriging.

本研究集中于证据信念函数（EBFs）模型的性能，以预测伊朗伊斯法罕东北部Kahang斑岩铜矿床（KPCD）铜（Cu）的三维（3D）矿化。3D矿床建模和储量估算是矿产勘探计划中最重要的步骤。确定矿床的边界并估算储量的数量和质量是采矿设计中最重要的问题。通常，在不考虑其他参数和矿床信息的情况下，根据矿物品位参数准备寻找矿床的3D模型。在这方面，矿床规模的3D集成模型有助于考虑其他参数和可用的地下信息。这些3D集成模型的结果主要用于介绍矿产前景地区。但是，在这项研究中，集成模型被用作地统计协同克里格法估计的辅助参数。值得注意的是，矿床的3D模型的准确性有所提高，而估计误差却有所降低。。本研究使用了伊朗国家铜业公司钻探的22个钻孔的信息。为了实现资源建模，东，北和深度方向的块数分别为30×28×145，每个块的大小设计为15×15×5 m。辅助地理空间模型分为地质，地球化学和地球物理数据集三个主要类别，并且定量获得了所有3D证据模型。随后，Dempster-Shafer证据理论被用于构建Kahang矿床的3D前景建模。使用这种知识驱动的理论，生成了四个信念，不确定性，合理性和怀疑性3D模型，这些模型对更准确的Cu资源建模具有重大影响。在用钻孔结果验证了前景模型之后，所需的3D辅助模型与Cu品位的相关性为0.92。因此，使用3D EBFs模型作为辅助变量通过协同克立格法估算铜品位，并将其输出与普通克立格法（OK）进行比较。这项研究的意义在于通过辅助变量改善Cu级建模的性能，其中估计值的均方根误差（RMSE）从0.437降低到0。