The Practice of Sequential Gaussian Simulation

The theory of simulation is relatively well documented but not its practice, which is a problem since simulation is not as robust as linear estimation. As a result, many costly mistakes probably go undetected. In this paper, a process for simulation is introduced with the objective of reducing the likelihood of such mistakes. The context is sequential Gaussian simulation within the mining industry. However, a significant part of the process can be applied in other simulation framework.

Four of the most important aspects of the process are discussed in detail. A gradual trend adjustment is suggested as a post-simulation step. A modified bootstrap approach is presented to deal with the grade uncertainty that accounts for spatial dependence between the samples. A number of pre- and post-simulation checks are also discussed. Some post-simulation adjustments of the simulated values are suggested to improve on the quality of the simulation.

All of the approaches, solutions and checks presented in this paper are simple, flexible, and can be easily implemented by a practitioner.