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