CoreSmart

 

Artificial Intelligence for Drill Core data Analysis – Performance Testing of the integrated Neural Network

CoreSmart is an Artificial Intelligence Predictor to enrich drill core geochemical data based on hyperspectral scans. The system is using corescan database Version 2.0 including 1000km scanned core from Australia, Europe, Asia and Africa and one million corresponding geochemical records.
In a performance test the team analyzed the database with standard classification methods, deep learning approaches and the unique for CoreSmart developed neural network. Tested was the prediction accuracy for four classes of Gold, Copper and Iron contents in a 60% learning and 40% testing dataset of 110.000 records. The following table shows the achieved prediction accuracies achieved with the different tested methods for Iron and a rating combined over all commodities tested.

 

Class

Comparison Methods (Accuracy in Percent)

CoreSmart Neural Network

SVM4

RBF

NB

MLG

SVM2

MLP

KNN

Fe0 – below 1ppm

62.3

62.7

80.9

68.0

 

73.0

82.8

76.0

Fe1 – below 1%

31.3

0.0

15.7

9.6

 

1.7

65.4

87.0

Fe2 – below 10%

28.4

77.6

67.4

93.6

60.8

84.4

85.9

84.0

Fe3 – higher concentr.

45.6

48.6

57.5

64.9

72.4

68.9

77.5

82.0

Accuracy overall Iron

42.7

64.9

68.3

72.6

63.6

75.1

80.7

82.3

Average Performance Ranking over all

Commodities

11.00

9.67

9.33

8.00

7.52

6.67

4.67

1.33

Best Fit Rank all commodities

8

7

6

5

4

3

2

1

Abbreviations: SVM4 – Supported Vector Machine (4 classes problem) / RBF – Radial Base Classifier / NB – Naïve Bayes / MLG – Multinomial Logistic Regression / SVM2 – Supported Vector machine (2 classes problem) / MLP – Multi layer Perceptron / KNN – K-Nearest-Neighbor

The results showing a significant better performance of the CoreSmart Neural network (average over all commodities above 80%) against classical prediction methods (average below 70%). Therefore, the CoreSmart Neural Network allows even for a 4-class analysis good prediction of geochemical properties over the complete core and can be used for more targeted geochemical probing, deposit modelling and as base for artificial intelligence areal exploration models.