Multivariate Geostatistical Simulation at Red Dog Mine, Alaska, USA

Red Dog mine is the world's largest Zn producer. The deposit consists of sulphide ore zones in sedimentary exhalative (sedex) deposits, and is characterized by the presence of multiple metals and multiple ore types. The objective of the case study is to characterize seven different minerals, Zn, Pb, Fe, Ba, sPb, Ag and TOC, within eight different rock types. Geostatistical models were constructed for each variable within the eight rock types, and subsequently assembled to give 40 realizations for six 25ft benches. The stepwise conditional transformation was used to account for the complex multivariate relations. In addition to reproducing the input data, histogram and variogram, the resulting models also respect multivariate relations locally and globally. This paper documents the methodology to construct these models. A thorough validation procedure was implemented. Comparisons with blasthole data show that the conditional simulations and the long-term models agree in expected value. The main advantages of the conditional simulations are (1) the explicit accounting of multivariate relations between the data in model construction, and (2) the ability to quantify variability and uncertainty at any scale. A small exercise comparing the profit from a kriged and a simulated model is also examined. Results show that the simulation approach showed a 3% increase in profit, relative to the conventional estimation approach.