CoreSmart – 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 uses 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, and 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 overall 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 concentration |
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 Overall 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
@CoreSmart