Title: Prediction of gold-bearing localised occurrences from limited exploration data

Authors: Igor Grigoryev; Adil Bagirov; Michael Tuck

Addresses: School of Science, Engineering and Information Technology, Federation University Australia, University Dr, Mount Helen, VIC 3350, Australia ' School of Science, Engineering and Information Technology, Federation University Australia, University Dr, Mount Helen, VIC 3350, Australia ' School of Science, Engineering and Information Technology, Federation University Australia, University Dr, Mount Helen, VIC 3350, Australia

Abstract: Inaccurate drill-core assay interpretation in the exploration stage presents challenges to long-term profit of gold mining operations. Predicting the gold distribution within a deposit as precisely as possible is one of the most important aspects of the methodologies employed to avoid problems associated with financial expectations. The prediction of the variability of gold using a very limited number of drill-core samples is a very challenging problem. This is often intractable using traditional statistical tools where with less than complete spatial information certain assumptions are made about gold distribution and mineralisation. The decision-support predictive modelling methodology based on the unsupervised machine learning technique, presented in this paper avoids some of the restrictive limitations of traditional methods. It identifies promising exploration targets missed during exploration and recovers hidden spatial and physical characteristics of the explored deposit using information directly from drill hole database.

Keywords: unsupervised learning; mathematical programming; resource definition; gold; diamond drilling; prediction; clusterwise linear regression.

DOI: 10.1504/IJCSE.2020.10028617

International Journal of Computational Science and Engineering, 2020 Vol.21 No.4, pp.503 - 512

Received: 03 Jan 2019
Accepted: 06 Mar 2019

Published online: 24 Apr 2020 *

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