Defining a Sampling Strategy for a Continuous Flow Supported by Geostatistical Simulations

The estimation of process plant head grade is not trivial and is a source of constant dispute within the mining industry. Grade estimates informed by the short term mine planning are frequently not reproduced by sampling the continuous flow of ore at the plant. Head grades are normally obtained by periodically sampling the continuous flow during a day or shift, and taking the average of the multiple increments collected. The standard error of this mean grade depends on the ore variability and the frequency (number) of aliquots extracted. The more increments are taken the higher is the precision on the calculated mean. Variographic experiments are normally used to map grade variability along a certain period of time and the extension variance is derived from this variogram. This experiment is time consuming, expensive and cumbersome. Mining industry personnel rarely apply this approach. Additionally, a temporal variogram can vary from day to day as different ores are mined along the life of the mine. This paper investigates a novel approach based on simulating the grades feeding the processing plant. In situ 3D grade models are built using geostatistical simulations. Grade spatial continuity and variability are reproduced in models that mimic the real deposit. These models are used in mine planning and scheduling transforming a 3D block model into an one dimensional string of values feeding the plant (one dimension flow). These grades have the statistical characteristics of the unknown real grades and can be sampled to emulate a variographic experiment. The results showed consistency and adequacy to estimate the sampling error (Gy, 1998 p 31) for various sampling intervals using the simulated grades. Also the results show a variographic experiment can change significantly from time to time, as ore changes and a selected sampling protocol can become obsolete if not adjusted for ore variability.CITATION:Costa, J F C L and Marques, D M, 2012. Defining a sampling strategy for a continuous flow supported by geostatistical simulations, in Proceedings Sampling 2012 , pp 3-8 (The Australasian Institute of Mining and Metallurgy: Melbourne).