Results are assessed based on validation of marginal distribution, spatial correlation and bivariate relationship. The proposed algorithm is applied to a real case study from an Iron deposit with a sharp inequality constraint between Iron and Silica. This study proposes an algorithm based on a hierarchical cosimulation framework integrated with inverse transform sampling, which is designed to model variables within thresholds derived from a linear inequation. ![]() Therefore, this work's motivation is to propose a way to model datasets containing inequality constraints between variables and generate production schedules considering geological uncertainty. The poor reproduction of this feature can lead to an overestimation of the secondary variable, affecting the processing cost. For example, it is common to deal with challenging datasets containing bivariate complexities among variables, such as inequality constraints. Nevertheless, there are still many limitations in commercially used stochastic geostatistical algorithms, particularly multivariate methods. Integration of stochastic geostatistical realizations into mine planning can help to minimize the risk of not meeting the production targets. However, traditional mine planning does not allow the risk management associated with geological uncertainty due to using a single mineral resource model as an input. In that regard, geostatistics and mine planning as disciplines are critical parts of the mining business. ![]() The modern mining industry employs plenty of exploration data digitization and utilizes computational resources for future forecasting and production scheduling.
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