Process Name	Menu Path	Link to Command Table
DECLUST	Command line only	Click here

DECLUST Process

To access this process:

ribbon >> Decluster >> Simple.

See this process in the Command Table.

Process Overview

Note: This is a superprocess and running it may have an effect on other Datamine files in the project.

The DECLUST process declusters a set of sample data.

It is common practice to take more samples in high grade areas in order to improve the level of confidence in the estimation. However, when data are not on a regular grid then using the full data set will give a biased estimate of the mean, variance and histogram. In addition, clustered data can affect an indicator variogram.

Note: This process supports retrieval criteria.

Declustering is the process of adjusting the full data set i.e. by removing or weighting data points in densely sampled areas, to give a more representative and evenly spaced set of samples. The DECLUST process provides two methods of doing this, with both methods requiring a regular 3D grid to be placed over the sample data. The two methods are:

Sample Selection - select a single sample from each grid cell.
Declustered Weight - assign every sample a weight based on the number of samples in the grid cell

If the Sample Selection method is chosen then DECLUST provides a choice of four ways of assigning the value to the grid cell:

Select a sample at random within each grid cell. A new random sample is generated for each run.
Select a sample at random within each grid cell. The same 'random' sample is generated for each run.
Select the sample nearest to the grid cell centre.
Calculate the average value of all samples within the grid cell.

If the Declustered Weight method is chosen then the weight, DCWEIGHT, for a sample is calculated as:

DCWEIGHT = NDATA / NCELLS / NPERCELL

where:

NDATA is the total number of samples
NCELLS is the number of grid cells containing one or more samples
NPERCELL is the number of samples in the grid cell

The sum of the weights over all samples equals the total number of samples (NDATA). Therefore if a sample lies in a high density area it will have a weight of less than 1, and if it is in a low density area it will have a weight of more than 1. The output file from the Declustered Weight method can be used to transform data into a normal distribution, for input to the NSCORE or SGSIM processes.

One of the problems with the declustering method is that different grid sizes will generate different statistics. However, in general a regular grid about the size of the average sample spacing is suggested.

The process writes a summary table for each method to the Output control bar.

Sample Selection: shows statistical parameters for each numeric field in the output file. These statistics can be saved to a file if the STAT_TBL output file is specified.
Declustered Weight: shows the declustered weight as a function of the number of samples per grid cell. These statistics can be saved to a file if the WGTS_TBL output file is specified.

Note: The process DECLUST has a limit of only being able to use input sample files with a maximum of 53 fields. If the process is run with an input sample file of greater than 53 fields, a warning message is displayed which prompts you to reduce the number of fields.

Input Files

Name	Description	I/O Status	Required	Type
IN	Input sample data file. This must contain a set of 3D coordinates (eg X,Y,Z) and at least one other field.	Input	Yes	Undefined

Output Files

Name	I/O Status	Required	Type	Description
OUT	Output	No	Undefined	Output file containing declustered samples. At least one of the two output files OUT or WTOUT must be selected.
WTOUT	Output	No	Undefined	Output file containing declustered weights. This will be a copy of the IN file, but will also include the field DCWEIGHT. At least one of the two output files OUT or WTOUT must be selected.
WGTS_TBL	Output	No	Undefined	Output file containing summary statistics for declustered weights.
STAT_TBL	Output	No	Undefined	Output file containing summary statistics for declustered and clustered WTFIELD samples.

Fields

Name	Description	Source	Required	Type	Default
X	X coordinate of sample data	IN	Yes	Numeric	X
Y	Y coordinate of sample data	IN	Yes	Numeric	Y
Z	Z coordinate of sample data	IN	Yes	Numeric	Z
WTFIELD	Field to be used for calculating declustered weights. This is only relevant if a WTOUT file has been specified and one or more of the grade fields in the IN file contain absent data values. Specifying a WTFIELD field ensures that records containing absent data values for that field will be ignored. If a WTFIELD field is not specified but a WTOUT file has been selected then the Z field is used.	IN	No	Numeric	Undefined

Parameters

Name	Description	Required	Default	Range	Values
METHOD	Declustering method if OUT file specified: 1 = random selection within grid (different selection each run) 2 = pseudo random selection within grid (repeatable) 3 = nearest to grid centre 4 = average of samples within grid	No	1	1,4	1,2,3,4
XGRID	Grid size in X	Yes	Undefined	0.00001,+	Undefined
YGRID	Grid size in Y	Yes	Undefined	0.00001,+	Undefined
ZGRID	Grid size in Z	Yes	Undefined	0.00001,+	Undefined
XORIG	X coordinate of grid origin	No	0	Undefined	Undefined
YORIG	Y coordinate of grid origin	No	0	Undefined	Undefined
ZORIG	Z coordinate of grid origin	No	0	Undefined	Undefined
CENTRE	Flag to show whether the X, Y and Z coordinates of the grid centre should be included in the OUT file. If selected the names of the fields in the file will be XCENTRE, YCENTRE and ZCENTRE:	No	0	0,1	0,1

Example

!DECLUST &IN(sampleb),&OUT(dsampleb),&OUT(dcwts),

*X(X),*Y(Y),*Z(Z),@METHOD=2,

@XGRID=10,@YGRID=10,@ZGRID=10,@XORIG=0,@YORIG=0,@ZORIG=0,@CENTRE=0

Error and Warning Messages

Message	Solution
The input sample file has greater than 53 fields.	Reduce the number of fields in the input sample file e.g. by using SELCOP or SELDEL to generate a new file.