What is Local Kriging Neighbourhood Optimisation or LKNO? Well it stems from the fact that every block estimated in a model is independent of every other block estimate and therefore the parameters used for each block do not need to be identical. LKNO is an alternative approach to optimising estimation parameters and ensures that each block is estimated with the best possible combination of parameters. The way it works is that kriging estimates are performed using a range of parameter options and the estimate that produces the best result, that is the one that produces the highest kriging efficiency and / or slope of regression is the one that is selected.
Adding an LKNO
In order to add an LKNO component you will need to group your data Assay over Domain.
You will also need to have generated your variograms.
Right click on the domain you wish to optimise and select Add | LKNO.
The first step is to specify the block model origin and block size. The default values are taken from the sample data and you can either edit them manually or alternatively if you want to use your loaded block model you can select it from the drop-down menu.
Next step is to ensure that the correct variograms will be used in the estimation are selected.
The default setting is the variogram generated for that particular domain but if you want to use variograms from a different domain you can do so by clicking on the browse button and selecting the appropriate model.
The search angles are read from the variogram model.
You can manually edit the discretisation points in the X, Y and Z directions.
For top cutting you have three options:
- Don’t apply a top cut
- Apply a global top cut by specifying either a grade or metal percentage
- Apply a local top cut by specifying a CV limit for samples used to estimate that block or metal by percentage
The parameter set number will allow you to identify which parameter set was used to estimate each block in the resultant model. You can define up to 25 parameter sets.
You can choose to optimize the Krigging Efficiency, the Slope of Regression, or both.
The tolerance controls whether an estimate is accepted or rejected.
Click on the update button to run the estimate.
The estimation uses ordinary kriging and what is happening is that the initial estimate uses the parameters defined by Parameter set 0 and the results saved to a block model. The estimate is then run again using the parameters defined by Parameter set 1 and if the KE for a particular block is greater than the one stored in the block model then all the results for that particular block are replaced. This happens for each of the parameter sets. The result is each block in the resulting grade model is based on the best combination of search parameters.
You can view the resultant block model in the 3D viewer.
By default the blocks are coloured according to parameter set.
In this example because the search distance increased for each parameter set the blocks within the area of close spaced drilling have been estimated using the parameter set 0.
You can change the parameter the block model is coloured on from the drop down menu.
To export the parameter set table to a csv file right click anywhere within the table either in the 3D viewer or the LKNO tabs and select Export then CSV
There are two options for exporting the results: The first method is to click on the result tab and right click anywhere within the table and select Export and then csv.
The second method is useful if you are wanting to perform model validation within Supervisor as it not only creates the .csv file but automatically loads it into Supervisor as block data. Right click on the LKNO component in the data tree and select Export.
Enter the file name in the window and select Save. The block data will appear in the data tree and from there you can insert a model validation.