New efficient methods for conditional simulations of large orebodies

Abstract

The application of conditional simulation techniques for modelling orebodies requires efficient algorithms, particularly due to the large number of grid nodes required, often in order of tens of millions. In this paper, two new efficient conditional simulation methods are reviewed: the generalised sequential Gaussian simulation (GSGS) and the direct block simulation (DBSIM). Both methods gain computational efficiency by simulating groups of nodes simultaneously, using a local neighbourhood as the conditioning data set. The relationship between the group and local neighbourhood sizes used is found to be important to both the accuracy of results and processing efficiency, and it is assessed numerically through a measure of the loss of accuracy. Practical aspects of the GSGS are demonstrated and assessed in a case study at a porphyry copper deposit. Computational efficiency is demonstrated in the case study involving orebody models with up to 14,000,000 grid nodes, where the method is up to 20 times faster than the well-established sequential Gaussian simulation. At the same time, GSGS maintains a high level of accuracy. The practical aspects of DBSIM are demonstrated in simulating the same copper deposit in a comparable way to GSGS. In the case study, the computational efficiency of DBSIM is marginally better than GSGS; however, there are two major improve- ments. First, the application of DBSIM results in a substantial reduction of storage requirements and leads to improved data management. Second, the validation of the reproduction of variogram models is performed at the block support scale, which leads to a substantially more efficient variogram validation process than at the point support scale. Both methods, GSGS and DBSIM, provide efficient and reliable tools for practitioners to assess geological uncertainty in large mining applications.