Comparison of indicator kriging, conditional indicator simulation and multiple-point statistics used to model slate deposits

Abstract

The resources in an ornamental slate deposit can be estimated using geostatistical estimation techniques applied to information collected from drill cores. The result, however, is a smooth approximation that fails to take account of the natural variability in mineralization, which is fundamental to proper design and evaluation of the financial viability of a mining deposit. Geostatistical simulation techniques are more useful in this respect, as they reflect different realizations of the reality and better reflect natural mineral dispersion. In this work, we evaluate the resources of a slate quarry by comparing the results obtained using two geostatistical techniques—indicator kriging (ik) and sequential indicator simulation (sisim)—with the results obtained using the single normal equation simulation (snesim) technique based on multiple-point statistics (mps), analyzing their usefulness in evaluating the financial risk derive from uncertainty in regard to knowledge of the deposit. Our results indicate that although the multiple-point statistics approach produces models that are closer to reality than the models produced by the geostatistical techniques, the simulation relies in part on information obtained via indicator kriging.