# Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction
## 3 Results and Discussion
### Block Model Creation and Validation for Resource Estimation  
This section describes generating and validating block models for resource estimation in gold mineralization deposits. The block model was created by dividing the deposit into small, similar-sized blocks using empty block models within closed wireframe models of the mining bodies. To prevent overestimation, sub-blocking was employed. The SURFAC program incorporated constraints into the block model, such as specific gravity and deposit geology, to ensure an accurate estimate of the target tonnage and minimize potential inaccuracies. The validity of the block model provided quantitative evidence for the accuracy of the estimating process. Because it results in the slightest standard error, a minor confidence interval, and the highest level of confidence or the least amount of risk, kriging was selected as the method for performing the estimation [63]. Ordinary kriging was utilized to estimate the deposit grade, as it has been found to perform effectively in previous models. Moreover, the spatial correlation of the binary data can be leveraged to estimate the likelihood of exceeding a particular grade cut-off at unsampled locations through indicator kriging. This information can help identify areas with a high potential for mineralization, which can help guide future exploration and mining efforts. The block model's 20 \(\times\) 20 \(\times\) 5 m parent blocks encompassed the mineralization domain. Compositing, drilling spacing, and geological structure all factored into the decision-making process for selecting the properties of the parent blocks. Sub-blocking ensured volume representation was accurate for resource estimation [64]. All blocks within the deposit were assigned an average specific gravity of 2.67 to facilitate tonnage calculations based on the deposit's geology. The resource domain was used to generate ordinary and indicator kriging models, which were then utilized to estimate the grade of each block. Building and validating a block model are crucial aspects of the resource estimation process since they provide a solid foundation for generating reliable mineralization estimates for subsequent exploration and mining efforts. Restrictions and sub-blocking variables were employed to prevent overestimation and ensure that the block model accurately represents the deposit's mineralization. In this study, mathematical metrics and prediction errors were used to assess the performance of various approaches. These methods can provide valuable insights into the effectiveness and suitability of different models or techniques for estimating gold content. Table 6 shows that the model's predictions are inaccurate for the kriging methods (OK and IK). A low R (Figure 7) means that the predicted and actual values do not have a strong linear relationship, and a low R\({}^{2}\) means that the predicted values only explain a small amount of the variation in the actual values. A large MSE and RMSE suggest that the estimates are, on average, far from the observed values. Based on the study's findings, the gold content in the research region exhibits significant skewness and large coefficients of variation. This may explain why the models perform poorly in predicting the gold content, particularly compared to the results of variogram modeling. The variogram modeling revealed rising nugget/sill values for each model. However, the R\({}^{2}\) values for all models used in the research were less than 0.5, indicating poor performance. This can be attributed to two main factors. Firstly, the poor spatial correlation between the gold dataset and the study region may have affected the accuracy of the models. Secondly, the skewed data distribution may have impacted the accuracy of the model's predictions. The study anticipated poor results from using ordinary kriging to forecast gold content due to its tendency to produce locally linear estimates and smoothing effects.