Grade control is an important task performed at the mine on a daily basis. It is a basic, economic decision that selects the destination of each parcel of material mined. Mistakes at this stage are costly, irreversible, and can be measured in terms of cash flow losses and increased operational costs.
Grade control models are based on a large number of samples. In underground mines, production data is usually a series of tightly drilled holes, channel samples, or short holes to test production stopes. In an open pit environment, blast holes samples are obtained on closely spaced grids, according to blasting requirements. Less frequently, grade control drilling is performed separate from blast hole drilling, for example using dedicated reverse circulation (RC) drilling. In some geologic settings, surface tranches and channel samples are used as well.
Production samples are used to select ore from waste, and are affected by several sampling issues. Often, blast hole samples are not as reliable as samples obtained from exploration or RC drill holes. This is explained by a combination of drilling and field sampling methods. Sometimes, the large quantity of samples available will tend to minimize the impact of the error of a single blast hole sample.
Geologic variables are mapped in the pit or stopes, but are not always used in production control. Procedures for extracting some benefit from the local geology mapped should be implemented. The goal is to find practical ways of mapping and quickly processing geological information. The typical turnaround time for a grade control model in an open pit is 24 to 48 hours.
Conventional grade control methods include defining grade outlines and using inverse distance, polygonal estimation, or more commonly kriging of blast hole grades. These methods do not account for the uncertainty in prediction. Alternatively, simulation of multiple realizations provides the basis for different optimization algorithms, such as the minimum-loss/maximum profit method.
Improvements from the simulation-based methods are evident in more erratic grade distributions and in more marginal mixed ore-type zones. More complicated grade control scenarios, such as those including multiple processing options and stockpiling, will also lend themselves to optimization through simulation based methods.
Reliable reconciliation (production) information is used to optimize the grade control model and also the resource model. Determining the degree of reliability of the reconciliation information is key to understanding to what extent it is possible to optimize and calibrate models, as the precision of such information is sometimes poor. GSI can assess the quality of the reconciliation information and the feasibility of using it for model calibration.