# Targeting Workflow Report

### Workflow Study: O_Block_targeting Date: Fri Nov 19 13:12:53 2010 |

The Targeting Workflow is designed to guide the user through a step-by-step process of combining 3D datasets by different processes to obtain a comprehensive exploration interpretation. The result of the combination is an additional quantitative layer of interpretation that is tied with a multi-disciplinary approach to targeting.

The different combination methods can be based on either subjective empirical models or probabilistic models, referred to as knowledge-driven and data-driven approaches, respectively. Knowledge-driven approaches are based on the experience of domain experts and include processes such as Boolean Logic, Index Overlay, Multi-Class Index Overlay, Dempster-Shafer Belief Theory, and Fuzzy Logic. Data-driven approaches require that known occurrences of what we are looking for exist in the earth model, such as a set of deposits, and include the Prospector Model, Weights-of-Evidence, Weighted Logistic Regression, Likelihood Ratio, and Neural Networks, among others. In addition, hybrid models using combinations of two or more different approaches have proven to be effective in many studies. Harris and Sanborn-Barrie (2006)1 provide a comprehensive overview of the various modeling approaches.

The Targeting Workflow provides functionality to perform Boolean Logic, Index Overlay, Multi-Class Index Overlay, and Weights-of-Evidence prediction models. Each uses a combination of exploration criteria to generate a prediction model which can be used for targeting XYZ drillhole target positions to be further investigated for mineral potential. Each of the processes implemented here is based on documentation from Bonham-Carter (1994)2.

## Report Tree

- Targeting Method
- Pre-Processing
  - Evidential Properties
  - Prediction Model Volumes
  - Evidential Property Settings
    - AOI_Dehua_MINFILE_showings_dist
    - Au_stream_drainage
    - O_Block_Dike_bends_dist
    - O_Block_detailed_contacts_dist
    - O_Block_detailed_dikes_dist
    - O_Block_detailed_faults_dist
    - O_block_AOI_dist
    - O_block_contact_bends_dist
    - O_block_fault_bends_dist
    - O_fault_intersections_dist
    - O_mag_anomaly_pinch_outs_dist
    - O_mag_highs_dist
    - O_rad_anomalies_sigma1
    - O_rad_anomalies_sigma2
  - Precombination
  - Evidence Weights
- Processing
  - Prediction Model Generation
- Post-Processing
  - Targeting
    - Target Clusters
    - Drillhole Target Centroids List
    - Drillhole Target Cells List

## Targeting Method

Index Overlay involves a combination of weighted binary properties using a simple intersection algorithm where the binary classes (1 or 0) of each property are multiplied by a single weight factor, summed over all properties being combined and normalized by the sum of all weights following the equation:

Weights are defined by the expert and are based on the significance of the evidential property to the exploration model. The result is a weighted score defining favourability of mineral potential. This method allows for a simple ranking of the contributing evidences as a whole.

Targeting Approach | Knowledge
Approach Logic | Weights of Evidence

## Pre-Processing

## Evidential Properties

Evidential properties represent the exploration criteria in a targeting model. The Targeting Workflow requires that all evidential properties be prepared and stored as properties on a voxet/sgrid object prior to starting the targeting model process. Properties can be binary, multi-class (categorical) (with or without assigned classification) or continuous in type and will be treating accordingly in the reclassification within the Property Settings step. Although it is not mandatory, it is suggested that multi-class properties be associated with a classification where possible in order to simplify the display of unique classes in the reclassification step and to ease interpretation of units during modeling.

Valid | true
Grid/Voxet Name | O_Evidence_Layer

Property | Type
--- | ---
AOI_Dehua_MINFILE_showings_dist | Continuous
Au_stream_drainage | Binary
O_Block_Dike_bends_dist | Continuous
O_Block_detailed_contacts_dist | Continuous
O_Block_detailed_dikes_dist | Continuous
O_Block_detailed_faults_dist | Continuous
O_block_AOI_dist | Continuous
O_block_contact_bends_dist | Continuous
O_block_fault_bends_dist | Continuous
O_fault_intersections_dist | Continuous
O_mag_anomaly_pinch_outs_dist | Continuous
O_mag_highs_dist | Continuous
O_rad_anomalies_sigma1 | Binary
O_rad_anomalies_sigma2 | Binary

## Prediction Model Volumes

The prediction model volume represents the '3D study area' within which to perform the modeling. The volumes are defined by the spatial extents of each of the contributing evidential properties taking into account any no data values and inconsistent data coverage between properties. Each model volume (if more than one) represents the volume where the maximum number of contributing evidential properties overlap.

Model Volume Name | Model1
Model Volume Region | model_region_0
Model Volume | 100

## Evidential Property Settings

### AOI_Dehua_MINFILE_showings_dist

Output Property Name | AOI_Dehua_MINFILE_showings_dist_out

#### Evidential Property Reclassification

Use Lower | true
Reclassification Method | Manual
Manual Cutoff(s) | 1000

Class | Class | Lower Range Value | Upper Range Value
--- | --- | --- | ---
1 | 2 | 29.6984 | 1000
2 | 1 | 1000 | 18806.4

### Au_stream_drainage

Output Property Name | Au_stream_drainage_out

#### Evidential Property Reclassification

Use Lower | false
Reclassification Method | Manual
Manual Cutoff(s) | -

Class | Class | Lower Range Value | Upper Range Value
--- | --- | --- | ---
1 | 1 | 0.9999 | 1.5
2 | 2 | 1.5 | 2.0001

### O_Block_Dike_bends_dist

Output Property Name | O_Block_Dike_bends_dist_out

#### Evidential Property Reclassification

Use Lower | true
Reclassification Method | Manual
Manual Cutoff(s) | 210

Class | Class | Lower Range Value | Upper Range Value
--- | --- | --- | ---
1 | 2 | 0.454825 | 210
2 | 1 | 210 | 10611.6

### O_Block_detailed_contacts_dist

Output Property Name | O_Block_detailed_contacts_dist_out

#### Evidential Property Reclassification

Use Lower | true
Reclassification Method | Manual
Manual Cutoff(s) | 210

Class | Class | Lower Range Value | Upper Range Value
--- | --- | --- | ---
1 | 2 | -0.0001 | 210
2 | 1 | 210 | 6201.3

### O_Block_detailed_dikes_dist

Output Property Name | O_Block_detailed_dikes_dist_out

#### Evidential Property Reclassification

Use Lower | true
Reclassification Method | Manual
Manual Cutoff(s) | 210

Class | Class | Lower Range Value | Upper Range Value
--- | --- | --- | ---
1 | 2 | 0.00342105 | 210
2 | 1 | 210 | 8356.84

### O_Block_detailed_faults_dist

Output Property Name | O_Block_detailed_faults_dist_out

#### Evidential Property Reclassification

Use Lower | true
Reclassification Method | Manual
Manual Cutoff(s) | 210

Class | Class | Lower Range Value | Upper Range Value
--- | --- | --- | ---
1 | 2 | 0.0195287 | 210
2 | 1 | 210 | 7163.17

### O_block_AOI_dist

Output Property Name | O_block_AOI_dist_out

#### Evidential Property Reclassification

Use Lower | true
Reclassification Method | Manual
Manual Cutoff(s) | 210

Class | Class | Lower Range Value | Upper Range Value
--- | --- | --- | ---
1 | 2 | 2.5364 | 210
2 | 1 | 210 | 9126.08

### O_block_contact_bends_dist

Output Property Name | O_block_contact_bends_dist_out

#### Evidential Property Reclassification

Use Lower | true
Reclassification Method | Manual
Manual Cutoff(s) | 210

Class | Class | Lower Range Value | Upper Range Value
--- | --- | --- | ---
1 | 2 | 2.39552 | 210
2 | 1 | 210 | 6882.63

### O_block_fault_bends_dist

Output Property Name | O_block_fault_bends_dist_out

#### Evidential Property Reclassification

Use Lower | true
Reclassification Method | Manual
Manual Cutoff(s) | 210

Class | Class | Lower Range Value | Upper Range Value
--- | --- | --- | ---
1 | 2 | 3.79449 | 210
2 | 1 | 210 | 7308.93

### O_fault_intersections_dist

Output Property Name | O_fault_intersections_dist_out

#### Evidential Property Reclassification

Use Lower | true
Reclassification Method | Manual
Manual Cutoff(s) | 210

Class | Class | Lower Range Value | Upper Range Value
--- | --- | --- | ---
1 | 2 | 6.99058 | 210
2 | 1 | 210 | 12465.6

### O_mag_anomaly_pinch_outs_dist

Output Property Name | O_mag_anomaly_pinch_outs_dist_out

#### Evidential Property Reclassification

Use Lower | true
Reclassification Method | Manual
Manual Cutoff(s) | 210

Class | Class | Lower Range Value | Upper Range Value
--- | --- | --- | ---
1 | 2 | 1.71862 | 210
2 | 1 | 210 | 6453.42

### O_mag_highs_dist

Output Property Name | O_mag_highs_dist_out

#### Evidential Property Reclassification

Use Lower | true
Reclassification Method | Manual
Manual Cutoff(s) | 210

Class | Class | Lower Range Value | Upper Range Value
--- | --- | --- | ---
1 | 2 | 2.54293 | 210
2 | 1 | 210 | 6011.61

### O_rad_anomalies_sigma1

Output Property Name | O_rad_anomalies_sigma1_out

#### Evidential Property Reclassification

Use Lower | false
Reclassification Method | Manual
Manual Cutoff(s) | -

Class | Class | Lower Range Value | Upper Range Value
--- | --- | --- | ---
1 | 1 | 0.9999 | 1.5
2 | 2 | 1.5 | 2.0001

### O_rad_anomalies_sigma2

Output Property Name | O_rad_anomalies_sigma2_out

#### Evidential Property Reclassification

Use Lower | false
Reclassification Method | Manual
Manual Cutoff(s) | -

Class | Class | Lower Range Value | Upper Range Value
--- | --- | --- | ---
1 | 1 | 0.9999 | 1.5
2 | 2 | 1.5 | 2.0001

## Precombination

This is an optional step which allows the user to pre-combine two or more evidential properties prior to computing the prediction model. Pre-combining datasets will account for evidential properties which are too strongly correlated and would result in an over-biased prediction result.

Generated | true
Perform Data Precombination? | true

Combination
O_Block_detailed_contacts_and_dikes
O_Block_bends_and_pinch_outs

## Evidence Weights

In binary and multi-class Index Overlay models, each evidential property is assigned a weight factor determined by its importance to the exploration model. Multi-Class Index Overlay further weights evidential properties by assigning scores to each of the property classes. Weights and scores are determined by the expert. They should be determined by relative importance (most important evidential property/class assigned the highest weight/score value) and are not restricted to integer values. The user is advised to use similar scoring schemes since scores are not normalized in the Multi-Class Index Overlay algorithm (i.e. scores should not range from 0-1 on one property and 0-10 on another within the same prediction model).

Scores_Out | -

Property | Weight
--- | ---
AOI_Dehua_MINFILE_showings_dist_out | 1.5
Au_stream_drainage_out | 1.5
O_Block_detailed_faults_dist_out | 1.2
O_fault_intersections_dist_out | 1.1
O_mag_highs_dist_out | 1.1
O_rad_anomalies_sigma1_out | 1.15
O_rad_anomalies_sigma2_out | 1.3
O_Block_bends_and_pinch_outs | 1.1
O_Block_detailed_contacts_and_dikes | 1.1

## Processing

## Prediction Model Generation

At this step in the workflow the knowledge-driven data combination algorithm is executed on the selected evidential properties, taking into account weights and scores if applicable, generating the final prediction model. Multiple prediction models can be generated at this step by selecting various combinations of evidential properties.

ModelName | Model1, O_Targeting_Model_Final

Model | Prediction Model Properties
--- | ---
Model1 | AOI_Dehua_MINFILE_showings_dist_out, Au_stream_drainage_out, O_Block_Dike_bends_dist_out, O_Block_detailed_contacts_dist_out, O_Block_detailed_dikes_dist_out, O_Block_detailed_faults_dist_out, O_block_AOI_dist_out, O_block_contact_bends_dist_out, O_block_fault_bends_dist_out, O_fault_intersections_dist_out, O_mag_anomaly_pinch_outs_dist_out, O_mag_highs_dist_out, O_rad_anomalies_sigma1_out, O_rad_anomalies_sigma2_out, O_Block_detailed_contacts_and_dikes, O_Block_bends_and_pinch_outs
O_Targeting_Model_Final | AOI_Dehua_MINFILE_showings_dist_out, Au_stream_drainage_out, O_Block_detailed_faults_dist_out, O_fault_intersections_dist_out, O_mag_highs_dist_out, O_rad_anomalies_sigma1_out, O_rad_anomalies_sigma2_out, O_Block_detailed_contacts_and_dikes, O_Block_bends_and_pinch_outs

## Post-Processing

## Targeting

Once the prediction model is generated, the workflow allows for an advanced targeting approach by allowing the user to refine and interpret the model down to the drillhole targets level. The prediction model can be refined by looking only at cells within a specific sub-region of the model, for example, in only the undrilled portion of the model, or within the spatial extents of a claim block area, etc.

A target value cutoff should be applied here which will show only the top percentage of the target result. From this top, typically 1-5%, of data, clusters of values can be generated using a connectivity type factor and ranked based on size. The cells of each cluster can be analyzed separately and the top cells within those clusters can be exported as a set of points representing the XYZ values of your drillhole targets.

Prediction Model Name | Model1
Target Cutoff | 0.305677977519905 (85.0954%)
Cluster Connectivity Type | corners

### Target Clusters

Target_Rank | Target_Volume | Target_Cells | Target_Min | Target_Max | Target_Mean | Target_Median
--- | --- | --- | --- | --- | --- | ---
1 | 0 | 422 | 0.4343891 | 0.5520362 | 0.4347956 | 0.4343891
2 | 0 | 4 | 0.4253394 | 0.5248869 | 0.4502262 | 0.4253394
3 | 0 | 5 | 0.520362 | 0.520362 | 0.520362 | 0.520362
4 | 0 | 2 | 0.4253394 | 0.4253394 | 0.4253394 | 0.4253394
5 | 0 | 1 | 0.4253394 | 0.4253394 | 0.4253394 | 0.4253394
6 | 0 | 31 | 0.4253394 | 0.6289593 | 0.5241572 | 0.5294118
7 | 0 | 2 | 0.5067874 | 0.5067874 | 0.5067874 | 0.5067874
8 | 0 | 3 | 0.4253394 | 0.4253394 | 0.4253394 | 0.4253394
9 | 0 | 2 | 0.520362 | 0.520362 | 0.520362 | 0.520362
10 | 0 | 6 | 0.4253394 | 0.5248869 | 0.4419307 | 0.4253394
11 | 0 | 4 | 0.4253394 | 0.4253394 | 0.4253394 | 0.4253394
12 | 0 | 43 | 0.4253394 | 0.6289593 | 0.4601704 | 0.4253394
13 | 0 | 2 | 0.5248869 | 0.5248869 | 0.5248869 | 0.5248869
14 | 0 | 3 | 0.4253394 | 0.4253394 | 0.4253394 | 0.4253394
15 | 0 | 14 | 0.4253394 | 0.6289593 | 0.5420168 | 0.5294118
16 | 0 | 500 | 0.4253394 | 0.6606335 | 0.4661527 | 0.4343891
17 | 0 | 42 | 0.4253394 | 0.6244344 | 0.5337213 | 0.5067874
18 | 0 | 1 | 0.5248869 | 0.5248869 | 0.5248869 | 0.5248869
19 | 0 | 158 | 0.4253394 | 0.6289593 | 0.5022051 | 0.5067874
20 | 0 | 3 | 0.4253394 | 0.5248869 | 0.4917044 | 0.5248869
21 | 0 | 22 | 0.520362 | 0.520362 | 0.520362 | 0.520362
22 | 0 | 57 | 0.4253394 | 0.7285068 | 0.5706122 | 0.5294118
23 | 0 | 31 | 0.4253394 | 0.4253394 | 0.4253395 | 0.4253394
24 | 0 | 16 | 0.4253394 | 0.5248869 | 0.5186653 | 0.5248869
25 | 0 | 21 | 0.5067874 | 0.5067874 | 0.5067874 | 0.5067874
26 | 0 | 43 | 0.5067874 | 0.6244344 | 0.5221511 | 0.5067874
27 | 0 | 1 | 0.5067874 | 0.5067874 | 0.5067874 | 0.5067874
28 | 0 | 11 | 0.5248869 | 0.6244344 | 0.5339367 | 0.5248869
29 | 0 | 28 | 0.4253394 | 0.6289593 | 0.4857791 | 0.5248869
30 | 0 | 39 | 0.4253394 | 0.5248869 | 0.5223345 | 0.5248869
31 | 0 | 5 | 0.4253394 | 0.5248869 | 0.5049773 | 0.5248869
32 | 0 | 3 | 0.5248869 | 0.5248869 | 0.5248869 | 0.5248869
33 | 0 | 79 | 0.4253394 | 0.5248869 | 0.4767742 | 0.5067874
34 | 0 | 7 | 0.4253394 | 0.4253394 | 0.4253394 | 0.4253394
35 | 0 | 9 | 0.4253394 | 0.5248869 | 0.4695827 | 0.4298643
36 | 0 | 2 | 0.5248869 | 0.5248869 | 0.5248869 | 0.5248869
37 | 0 | 11 | 0.4253394 | 0.5294118 | 0.4549568 | 0.4298643
38 | 0 | 39 | 0.4253394 | 0.6244344 | 0.5163015 | 0.5067874
39 | 0 | 12 | 0.5067874 | 0.5067874 | 0.5067874 | 0.5067874
40 | 0 | 66 | 0.4253394 | 0.6244344 | 0.5210477 | 0.5248869
41 | 0 | 34 | 0.4253394 | 0.5248869 | 0.4924142 | 0.5067874
42 | 0 | 12 | 0.4253394 | 0.6289593 | 0.5780542 | 0.6289593
43 | 0 | 7 | 0.4253394 | 0.5248869 | 0.5106658 | 0.5248869
44 | 0 | 7 | 0.5067874 | 0.5067874 | 0.5067874 | 0.5067874
45 | 0 | 1 | 0.4253394 | 0.4253394 | 0.4253394 | 0.4253394
46 | 0 | 3 | 0.4253394 | 0.4253394 | 0.4253394 | 0.4253394
47 | 0 | 15 | 0.4253394 | 0.5248869 | 0.511614 | 0.5248869
48 | 0 | 1 | 0.5248869 | 0.5248869 | 0.5248869 | 0.5248869
49 | 0 | 36 | 0.4253394 | 0.5248869 | 0.4751133 | 0.4253394
50 | 0 | 2 | 0.5248869 | 0.5248869 | 0.5248869 | 0.5248869
51 | 0 | 13 | 0.4253394 | 0.5294118 | 0.4504003 | 0.4253394
52 | 0 | 92 | 0.4253394 | 0.6289593 | 0.4901141 | 0.520362
53 | 0 | 35 | 0.4253394 | 0.6289593 | 0.5137687 | 0.5248869
54 | 0 | 9 | 0.4253394 | 0.5248869 | 0.447461 | 0.4253394
55 | 0 | 15 | 0.4253394 | 0.6289593 | 0.5140272 | 0.5294118
56 | 0 | 74 | 0.4253394 | 0.6289593 | 0.5313075 | 0.5248869
57 | 0 | 3 | 0.4253394 | 0.4253394 | 0.4253394 | 0.4253394
58 | 0 | 55 | 0.4253394 | 0.5294118 | 0.4566022 | 0.4253394
59 | 0 | 12 | 0.4253394 | 0.4253394 | 0.4253394 | 0.4253394
60 | 0 | 2 | 0.4253394 | 0.5248869 | 0.4751132 | 0.4253394
61 | 0 | 1 | 0.4253394 | 0.4253394 | 0.4253394 | 0.4253394
62 | 0 | 11 | 0.4253394 | 0.5248869 | 0.4434389 | 0.4253394
63 | 0 | 1 | 0.4253394 | 0.4253394 | 0.4253394 | 0.4253394
64 | 0 | 63 | 0.4253394 | 0.5248869 | 0.5079368 | 0.5067874
65 | 0 | 10 | 0.4253394 | 0.4253394 | 0.4253394 | 0.4253394
66 | 0 | 12 | 0.5067874 | 0.6244344 | 0.5165913 | 0.5067874
67 | 0 | 6 | 0.5067874 | 0.5067874 | 0.5067874 | 0.5067874
68 | 0 | 3 | 0.4253394 | 0.5248869 | 0.4917044 | 0.5248869
69 | 0 | 1 | 0.4253394 | 0.4253394 | 0.4253394 | 0.4253394
70 | 0 | 2 | 0.4253394 | 0.4253394 | 0.4253394 | 0.4253394
71 | 0 | 45 | 0.4253394 | 0.6244344 | 0.5100051 | 0.5248869
72 | 0 | 2 | 0.4253394 | 0.4253394 | 0.4253394 | 0.4253394
73 | 0 | 9 | 0.4253394 | 0.5248869 | 0.4364003 | 0.4253394
74 | 0 | 12 | 0.4253394 | 0.5248869 | 0.4585219 | 0.4253394
75 | 0 | 144 | 0.4253394 | 0.6289593 | 0.4907302 | 0.5248869
76 | 0 | 13 | 0.4253394 | 0.6244344 | 0.5081796 | 0.5248869
77 | 0 | 243 | 0.4253394 | 0.6651584 | 0.5165262 | 0.5248869
78 | 0 | 4 | 0.520362 | 0.520362 | 0.520362 | 0.520362
79 | 0 | 6 | 0.4524887 | 0.4524887 | 0.4524887 | 0.4524887
80 | 0 | 2 | 0.4524887 | 0.4524887 | 0.4524887 | 0.4524887
81 | 0 | 83 | 0.4343891 | 0.6561086 | 0.5024263 | 0.520362
82 | 0 | 1 | 0.520362 | 0.520362 | 0.520362 | 0.520362
83 | 0 | 64 | 0.4343891 | 0.5520362 | 0.4455599 | 0.4343891
84 | 0 | 27 | 0.4343891 | 0.5520362 | 0.4431037 | 0.4343891
85 | 0 | 90 | 0.4343891 | 0.6561086 | 0.4524387 | 0.4343891
86 | 0 | 55 | 0.4343891 | 0.5520362 | 0.4450843 | 0.4343891
87 | 0 | 14 | 0.4343891 | 0.4343891 | 0.4343891 | 0.4343891

### Drillhole Target Centroids List

The following table displays a list of targets within the Gocad pointset object generated by the 'Add to Centroid Target List' command. These points represent the centroid of the selected Target Cluster. They are ranked by the order they were selected in the Target Clusters Table.

Centroid_Id | Centroid_X | Centroid_Y | Centroid_Z
--- | --- | --- | ---
0 | 381346 | 6.92193e+06 | 1401
1 | 386955 | 6.93279e+06 | 1401
2 | 386568 | 6.93266e+06 | 1401
3 | 386880 | 6.93246e+06 | 1401
4 | 387060 | 6.93213e+06 | 1401
5 | 387360 | 6.9317e+06 | 1401
6 | 393660 | 6.93138e+06 | 1401
7 | 387940 | 6.93139e+06 | 1401
8 | 389310 | 6.93123e+06 | 1401
9 | 387420 | 6.93102e+06 | 1401
10 | 391650 | 6.93078e+06 | 1401
11 | 392353 | 6.93078e+06 | 1401
12 | 388650 | 6.93057e+06 | 1401
13 | 392400 | 6.93049e+06 | 1401
14 | 388024 | 6.93058e+06 | 1401
15 | 394700 | 6.93118e+06 | 1401
16 | 393584 | 6.93052e+06 | 1401
17 | 391980 | 6.93027e+06 | 1401
18 | 390756 | 6.93065e+06 | 1401
19 | 389580 | 6.93021e+06 | 1401
20 | 385415 | 6.93024e+06 | 1401
21 | 393031 | 6.93027e+06 | 1401
22 | 391575 | 6.93021e+06 | 1401
23 | 390038 | 6.93003e+06 | 1401
24 | 388311 | 6.93008e+06 | 1401
25 | 393853 | 6.92998e+06 | 1401
26 | 388380 | 6.92985e+06 | 1401
27 | 393436 | 6.92982e+06 | 1401
28 | 389250 | 6.93004e+06 | 1401
29 | 388671 | 6.93006e+06 | 1401
30 | 387504 | 6.92978e+06 | 1401
31 | 388820 | 6.92963e+06 | 1401
32 | 392365 | 6.92981e+06 | 1401
33 | 388166 | 6.92951e+06 | 1401
34 | 393227 | 6.92944e+06 | 1401
35 | 389670 | 6.92931e+06 | 1401
36 | 391445 | 6.92921e+06 | 1401
37 | 390288 | 6.92937e+06 | 1401
38 | 388550 | 6.92914e+06 | 1401
39 | 394058 | 6.92941e+06 | 1401
40 | 393524 | 6.92926e+06 | 1401
41 | 392220 | 6.92904e+06 | 1401
42 | 388860 | 6.92901e+06 | 1401
43 | 394320 | 6.92883e+06 | 1401
44 | 385200 | 6.92877e+06 | 1401
45 | 392640 | 6.92865e+06 | 1401
46 | 386136 | 6.92865e+06 | 1401
47 | 395580 | 6.92847e+06 | 1401
48 | 386895 | 6.92859e+06 | 1401
49 | 391950 | 6.92844e+06 | 1401
50 | 390231 | 6.92853e+06 | 1401
51 | 389561 | 6.92891e+06 | 1401
52 | 391510 | 6.92866e+06 | 1401
53 | 390620 | 6.92844e+06 | 1401
54 | 392396 | 6.92834e+06 | 1401
55 | 393344 | 6.92847e+06 | 1401
56 | 393400 | 6.92801e+06 | 1401
57 | 391009 | 6.92838e+06 | 1401
58 | 394835 | 6.92800e+06 | 1401
59 | 392850 | 6.92793e+06 | 1401
60 | 391860 | 6.92793e+06 | 1401
61 | 391576 | 6.92811e+06 | 1401
62 | 391080 | 6.92793e+06 | 1401
63 | 394207 | 6.92813e+06 | 1401
64 | 390354 | 6.9278e+06 | 1401
65 | 393390 | 6.92775e+06 | 1401
66 | 393630 | 6.92766e+06 | 1401
67 | 391300 | 6.92757e+06 | 1401
68 | 390360 | 6.92751e+06 | 1401
69 | 390480 | 6.92742e+06 | 1401
70 | 395263 | 6.92754e+06 | 1401
71 | 393600 | 6.92724e+06 | 1401
72 | 394213 | 6.92724e+06 | 1401
73 | 390885 | 6.92706e+06 | 1401
74 | 392591 | 6.92742e+06 | 1401
75 | 393577 | 6.92655e+06 | 1401
76 | 395655 | 6.9261e+06 | 1401
77 | 395010 | 6.92496e+06 | 1401
78 | 395910 | 6.92485e+06 | 1401
79 | 396090 | 6.92475e+06 | 1401
80 | 395524 | 6.92516e+06 | 1401
81 | 395460 | 6.92421e+06 | 1401
82 | 382970 | 6.9241e+06 | 1401
83 | 382696 | 6.92347e+06 | 1401
84 | 381991 | 6.9233e+06 | 1401
85 | 381288 | 6.92294e+06 | 1401
86 | 380576 | 6.92271e+06 | 1401

### Drillhole Target Cells List

The following table displays a list of targets within the Gocad pointset object generated by the 'Add to Drillhole Target List' command. These points represent the individual cells selected from the target region. They are ranked by the order they were selected in the Property Viewer Table.

References:

1 Harris, J.R., Sanborn-Barrie, M., 2006, Mineral Potential Mapping: Examples from the Red Lake Greenstone Belt, Northwest Ontario, in Harris, J.R., ed., GIS for the Earth Sciences: Geological Association of Canada, Special Publication 44, p. 1-21.

2 Bonham-Carter, G.F., 1994, Geographic Information Systems for Geoscientists: Modeling with GIS: Pergamon, Oxford, 398 p.

3Thiart, C., Bonham-Carter, G.F., Agterberg, F.P., Cheng, Q., and Panahi, A., 2006, An application of the new omnibus test for conditional independence in weights-of-evidence modelling, in Harris, J.R., ed., GIS for the Earth Sciences: Geological Association of Canada, Special Publication 44, p. 131-142.

4 Agterberg, F.P., Bonham-Carter, G.F., Wright, D.F., 1990, Statistical Pattern Integration for Mineral Exploration: in Gaal, G. and Merriam, D.F., ed., Computer Applications in Resource Estimation: Prediction and Assessment for Metals and Petroleum, Pergamon Press, Toronto, p. 1-21.

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