eCite Digital Repository

Automated acid rock drainage indexing from drill core imagery

Citation

Cracknell, MJ and Parbhakar-Fox, A and Jackson, L and Savinova, E, Automated acid rock drainage indexing from drill core imagery, Minerals, 8, (12) Article 571. ISSN 2075-163X (2018) [Refereed Article]


Preview
PDF
702Kb
  

Copyright Statement

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

DOI: doi:10.3390/min8120571

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 Details

Item Type:Refereed Article
Keywords:drill core, hyperspectral, prediction, supervised classification, acid mine drainage, waste management, sulphide, mining, mine planning, machine learning
Research Division:Earth Sciences
Research Group:Geology
Research Field:Geology not elsewhere classified
Objective Division:Energy
Objective Group:Mining and Extraction of Energy Resources
Objective Field:Mining and Extraction of Energy Resources not elsewhere classified
UTAS Author:Cracknell, MJ (Dr Matthew Cracknell)
UTAS Author:Parbhakar-Fox, A (Dr Anita Parbhakar-Fox)
UTAS Author:Jackson, L (Miss Laura Jackson)
ID Code:129635
Year Published:2018
Web of Science® Times Cited:1
Deposited By:CODES ARC
Deposited On:2018-12-11
Last Modified:2019-03-08
Downloads:15 View Download Statistics

Repository Staff Only: item control page