A New Ore Grade Estimation Using Combine Machine Learning Algorithms

Umit Emrah Kaplan,Erkan Topal

doi:10.3390/min10100847

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.

Highlights

One of the critical tasks in mining value chain is to accurately estimate the grades of interest within a mineral deposit
The literature review highlights the potential benefits of machine learning methods in accurate grade estimation on variety of case studies, they mention various drawbacks as well
Proper structure can only be achieved by trial and error, which is a computationally intensive procedure. Another critical issue is that once an artificial neural network (ANN) provides a solution, it does not give any explanation regarding the possible relationship with input; there is still room for further investigation of data-driven learning models in order to obtain accurate grade estimation

Summary

Introduction

One of the critical tasks in mining value chain is to accurately estimate the grades of interest within a mineral deposit. The grades can be in the form of observation or estimation and are used in several stages of mining that ranges from exploration to exploitation. Due to the complex relationships between the grade distribution and spatial pattern variability, geostatistical methods may not give the best estimation results [19]. These limitations and complexities inspired researchers to investigate alternative approaches that can be utilised to overcome such obstacles. Over the past few decades, several researchers focused on various

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