A new ore grade estimation using combine machine learning algorithms

Umit Emrah Kaplan, Erkan Topal

Research output: Contribution to journalArticle

Abstract

Accurate prediction of mineral grades is a fundamental step in mineral exploration and resource estimation, which plays a significant role in the economic evaluation of mining projects. Currently available methods are based either on geometrical approaches or geostatistical techniques that often considers the grade as a regionalised variable. In this paper, we propose a grade estimation technique that combines multilayer feed-forward neural network (NN) and k-nearest neighbour (kNN) models to estimate the grade distribution within a mineral deposit. The models were created by using the available geological information (lithology and alteration) as well as sample locations (easting, northing, and altitude) obtained from the drill hole data. The proposed approach explicitly maintains pattern recognition over the geological features and the chemical composition (mineral grade) of the data. Prior to the estimation of grades, rock types and alterations were predicted at unsampled locations using the kNN algorithm. The presented case study demonstrates that the proposed approach can predict the grades on a test dataset with a mean absolute error (MAE) of 0.507 and R2 = 0.528, whereas the traditional model, which only uses the coordinates of sample points as an input, yielded an MAE value of 0.862 and R2 = 0.112. The proposed approach is promising and could be an alternative way to estimates grades in a similar modelling tasks.

Original languageEnglish
Article number847
Pages (from-to)1-17
Number of pages17
JournalMinerals
Volume10
Issue number10
DOIs
Publication statusPublished - Oct 2020

Keywords

  • Grade estimation
  • K-nearest neighbours
  • Machine learning
  • Neural network

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Geology

Fingerprint Dive into the research topics of 'A new ore grade estimation using combine machine learning algorithms'. Together they form a unique fingerprint.

  • Cite this