BM Normal Score

Use the BM Normal Score option to transform block model data from any distribution so that the transformed values follow a standard (normal) Gaussian distribution. The transformation is performed using the quantile transformation with a target distribution being a Gaussian standard. Unlike the Normal Score option, the transformation is performed using a nominated block model.

This option can also be accessed by selecting the BM Normal Score button from the Gaussian Transformations toolbar.


Instructions

On the Block menu, point to Gaussian Transformations, then click BM Normal Score.

Settings

Follow these steps:

  1. Enter a name for the Specification file, or select it from the drop-down list. The drop-down list displays all files found in the current working directory that have the (.bmn) extension. Click the Browse icon to select a file from another location.

  2. Select a Scenario ID. To create a new ID, click the New icon as shown below, and provide a unique name for the current panel settings.

  3. Select the Block model from the drop-down list. Click the Browse icon to select a file from another location.

  4. Map the correct fields by filling out the Block Model Variables Selection information.

    1. Select the field containing the Grade values.

    2. Select the block model variable that will hold the normal score values using the drop-down list labelled Normal score save at.

  5. Enable Use weights to select the database field containing the weights that can be used to build the grade distribution. Sample values are not changed by the weighting, only the relative importance in the distribution is adjusted. Leave this field blank if you do not want to apply any weighting.

  6. Enter or select a name for the file that will Save transformation table information.

    This field refers to the lookup table with the correspondence between the grade value and it's associated Gaussian transformation. The specified transformation table will be created during the transformation process and its values will be stored in an ASCII mapfile. The resulting file can be used later to back transform Gaussian values into a block a model to the corresponding grade. Refer to the Normal Score Back option for more information.

  7. Select Apply logarithm constant if you want to apply the base logarithm function to all values.

    Important:  In order for the logarithm to be defined all original values must be positive.

    The specified logarithm constant will be added to the calculated logarithm.

  8. Select Cut samples if you want to apply cut-offs to the values used in the transformation. You will need to specify a lower grade cut value (grades lower than this value are set to this value) as well as an upper grade cut value (grades above this value are set to this value).

Reference distribution

Use the Settings pane to define your specification file and scenario ID, as well as primary database information.

Follow these steps:

  1. Select either a database or map file as your sample source.

    • To select a database, enable the option ISIS file, then select the file from the drop-down list. Click folder icon to select a file from another location.

      You can also select an ODBC link for database files found on site servers.

    • To select a map file, enable the option labelled Use Map File, then select the file from the drop-down list. Click the Browse button to select a file from another location.

  2. Enter the name of the Sample Group to be loaded. Wildcards (* multi-character wildcard and % single character wildcard) may be used to select multiple groups. Multiple groups only apply to Isis databases (ASCII mapfiles consist of one group).

  3. Select the grade variable that will be used to construct the transformation lookup table in the field labelled Reference distribution.

  4. Select the database field containing the Weights that can be used to build the reference grade distribution. Sample values are not changed by the weighting, only its relative importance in the distribution is adjusted. Leave this field blank if you do not want to apply any weighting.

Samples Filter

Use this pane to include any restrictions to your data by using the four specialised filters.

Follow these steps:

  1. Include any restrictions to your data by using the four specialised filters.

Block Selection

Use this panel to set up various block selection options.

Follow these steps:

  1. Enter the maximum number of previously simulated grid nodes that will be used together with sample information in order to simulate block model nodes in the Maximum of presimulated blocks field. If the samples were not assigned to nodes through the Simulation parameters section, then the maximum number of samples will be controlled by the parameters specified through the Search region section.

    If the samples were assigned to nodes, then the parameter specified at the sample counts are ignored and the maximum number of presimulated block nodes will apply to samples (already assigned to nodes) and nodes populated by simulation.

  2. Select Use random search path to simulate the nodes in a random order.

  3. Select Use multiple search grid path to perform a grid ordering as a first step before going into the random path. The grid refinement begins by simulating the nodes at a coarse regular grid spacing. The coarse grid spacing can then be further refined to half of the previous spacing.

    Note:  A maximum number of 4 consecutive grid refinements can be requested in the Number of grids field. After the regularly spaced nodes are simulated the remaining nodes are selected using a random path.

Select any of the options to limit the blocks used.