Stochastic modeling of geological domains using a truncated Gaussian collocated co-simulation approach

To properly evaluate the quantity of mineral resources in a deposit, modeling the rock units of the deposit is essential since it informs the deposit’s mineralization controls. The deterministic methods of modeling include geological assumptions, especially between drill holes, and therefore, might predict the deposit’s complexity biasedly. Additionally, the uncertainty of rock units at unsampled locations cannot be quantified. Truncated/plurigaussian Gaussian and sequential indicator simulations are two most frequent stochastic approaches better suited to address these shortcomings. The ability to precisely depict the spatial correlation between rock units and quantify their uncertainties has made the truncated Gaussian simulation an essential technique for delineating rock units. However, using this method, long-range geological features like veins, faults, and fractures are often loosely modeled thus necessitating the use of soft data such as deterministic interpretive geological model to avert this inaccuracy. In this paper, the truncated Gaussian simulation is used to assess the spatial variability of rock units and quantify the uncertainty in their occurrence in a vein-dominated gold deposit. A novel approach is presented to model the rock units, particularly the veins, by incorporating a collocated co-simulation algorithm via a global correlation coefficient parameter. The realizations are conditioned to drill hole data and a collocated interpretive geological model created by a machine learning algorithm. The outcome shows realistic reproduction of the veins and other rock units, as representing the drill hole data and assessing their uncertainties thereof.