(untitled) (Rudarsko-geološko-naftni zbornik, 2023)
Resource estimation is one of the most important steps in the mining process. Precise resource estimation has a significant influence on the optimization of subsequent mining steps, i.e. mine planning and scheduling. The previous resource estimation in the Angouran Mine was conducted based on the provided information in the local coordinate system which causes considerable errors in estimations. Therefore, an attempt is made in this research to optimize the block model of the Angouran Mine and resource estimation based on the information in the UTM global coordinate system. For this purpose, exploratory data is firstly transferred from the local coordinate system to the UTM environment. Then, block model optimization is conducted using indicator kriging (IK) in which the waste blocks are removed and the block model was optimized. Finally, resource estimation is performed using the inverse distance weighting (IDW) and simple kriging (SK) methods. After variogram analyses in different directions, it was found that the mine deposit is anisotropic. Also, validation results showed that the acquired correlation coefficient in the carbonate and sulfide sections for IDW, SK and IK is 0.86, 0.87 and 0.92, and 0.88, 0.87 and 0.92, respectively. Finally, the obtained grades and tonnages are compared with the actual data of the exploratory boreholes, mined blocks and previous resource estimation in the mine. Comparative results showed that the obtained grades and tonnages from both previous and new models are over-estimated and higher than the actual values. The minimum errors of grade estimation equal 46% and 23.1% for previous and new resource estimations (before and after the waste removal), respectively. Also, the mining errors of tonnage estimation are 50.29% and 28.37% for previous and new models, respectively. This field comparison proved that transferring the exploratory data to the UTM system, utilization of the IK to remove the waste blocks and applying the SK for resource estimation lead to the optimization of the block model and a reduction in the estimation error compared to the previous estimations for the mine.