Predicting ore content throughout a machine learning procedure: an Sn-W enrichment case study

article

Iglesias, Carla
Antunes, I.M.H.R.
Albuquerque, M.T.D.
Martínez, Javier

Elsevier

IGLESIAS, C. [et al.] (2020] - Predicting ore content throughout a machine learning procedure: an Sn-W enrichment case study. Journal of Geochemical Exploration. ISSN 0375-6742. Vol. 208, p. 1-12. Doi 10.1016/ j.gexplo.2019.106405

0375-6742

10.1016/j.gexplo.2019.106405

Predicting ore content throughout a machine learning procedure: an Sn-W enrichment case study

Ore potential
Machine learning
Classification model
Sn-W prediction
Stream sediments
Portugal

2020-02-10T11:51:54Z
2020-02-10T11:51:54Z
2020-01-01

The distribution patterns of trace elements are very useful for predicting mineral deposits occurrence. Machine learning techniques were used for the computation of adequate models in trace elements’ prediction. The main subject of this research is the definition of an adequate model to predict the amounts of Sn and W in the abandoned mine area of Lardosa (Central Portugal). Stream sediment samples (333) were collected within the study area and their geochemical composition - As, B, Be, Cd, Co, Cr, Cu, Fe, Ni, P, Sn, U, V, W, Y, and Zn - used as input attributes. Different machine learning techniques were tested: Decision Trees (CART), Multilayer Perceptron (MLP) and Support Vector Machines (SVM). For regression and clustering, CART, MLP approaches were tested and for the classification, problem SVM was used. These algorithms used six different inputs – N1 to N6 – aiming to pick out the best-performing model.The results show that CART is the optimized predictor for Sn and W. Concerning the regression approach, correlation coefficients of 0.67 for Sn (with Input N1) and 0.70 for W (with Input N3) were obtained. Regarding the classification problem, an error rate of 0.10 was reached for both Sn (Input N1) and W (Input N2). The classification process is the best methodology to predict Sn and W, using as input the trace element concentrations in the collected stream sediment samples, Lardosa area, Portugal.
info:eu-repo/semantics/publishedVersion

openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/

eng