Topcutting

Supervisor has two methods of topcutting (also know as capping) data.

1. Global Topcut Analysis – Limit extreme values in a dataset.
2. Local Topcut Transition Model – Gradually change the top cut value for each sample based on the distance of the sample to the centre of a block.

Difference Between Global Topcut and Local Topcut

Global Topcut Analysis is used to reduce the effect of extreme values in a skewed dataset. Extreme values are reduced to the Global Topcut value, regardless of their distance from the block being estimated. No data is removed from the dataset.

Local Topcut Transition Models are used during resource estimation to gradually change the top cut value based on the distance of the sample to the centre of the block being estimated. The range at which the topcut transitions, the transition behaviour, and the topcut value itself can all be adjusted according to the dataset.