Open Access Repository

Automated acid rock drainage indexing from drill core imagery

Downloads

Downloads per month over past year

Cracknell, MJ ORCID: 0000-0001-9843-8251, Parbhakar-Fox, A ORCID: 0000-0002-3570-1195, Jackson, L and Savinova, E 2018 , 'Automated acid rock drainage indexing from drill core imagery' , Minerals, vol. 8, no. 12 , pp. 1-11 , doi: 10.3390/min8120571.

[img]
Preview
PDF
129634 - Automa...pdf | Download (718kB)

| Preview

Abstract

The automated classification of acid rock drainage (ARD) potential developed in this study is based on a manual ARD Index (ARDI) logging code. Several components of the ARDI require accurate identification of sulfide minerals that hyperspectral drill core scanning technologies cannot yet report. To overcome this, a new methodology was developed that uses red–green–blue (RGB) true color images generated by Corescan® to determine the presence or absence of sulfides using supervised classification. The output images were then recombined with Corescan® visible to near infrared-shortwave infrared (VNIR-SWIR) mineral classifications to obtain information that allowed an automated ARDI (A-ARDI) assessment to be performed. To test this, A-ARDI estimations and the resulting acid-forming potential classifications for 22 drill core samples obtained from a porphyry Cu–Au deposit were compared to ARDI classifications made from manual observations and geochemical and mineralogical analyses. Results indicated overall agreement between automated and manual ARD potential classifications and those from geochemical and mineralogical analyses. Major differences between manual and automated ARDI results were a function of differences in estimates of sulfide and neutralizer mineral concentrations, likely due to the subjective nature of manual estimates of mineral content and automated classification image resolution limitations. The automated approach presented here for the classification of ARD potential offers rapid and repeatable outcomes that complement manual and analyses derived classifications. Methods for automated ARD classification from digital drill core data represent a step-change for geoenvironmental management practices in the mining industry.

Item Type: Article
Authors/Creators:Cracknell, MJ and Parbhakar-Fox, A and Jackson, L and Savinova, E
Keywords: drill core, hyperspectral, prediction, supervised classification, acid mine drainage, waste management, sulphide, mining, mine planning, machine learning
Journal or Publication Title: Minerals
Publisher: MDPIAG
ISSN: 2075-163X
DOI / ID Number: 10.3390/min8120571
Copyright Information:

Copyright 2018 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

Related URLs:
Item Statistics: View statistics for this item

Actions (login required)

Item Control Page Item Control Page
TOP