Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

A three-dimensional feature extraction-based method for coal cleat characterization using X-ray μCT and its application to a Bowen Basin coal specimen

dc.contributor.authorZhang, Yulai
dc.contributor.authorTsang, Matthew
dc.contributor.authorKnackstedt, Mark
dc.contributor.authorTurner, Michael
dc.contributor.authorLatham, Shane
dc.contributor.authorMacaulay, Euan
dc.contributor.authorPitchers, Rhys
dc.date.accessioned2024-09-08T23:27:44Z
dc.date.available2024-09-08T23:27:44Z
dc.date.issued2023
dc.date.updated2024-04-21T08:15:42Z
dc.description.abstractCleats are the dominant micro-fracture network controlling the macro-mechanical behavior of coal. Improved understanding of the spatial characteristics of cleat networks is therefore important to the coal mining industry. Discrete fracture networks (DFNs) are increasingly used in engineering analyses to spatially model fractures at various scales. The reliability of coal DFNs largely depends on the confidence in the input cleat statistics. Estimates of these parameters can be made from image-based three-dimensional (3D) characterization of coal cleats using X-ray micro-computed tomography (μCT). One key step in this process, after cleat extraction, is the separation of individual cleats, without which the cleats are a connected network and statistics for different cleat sets cannot be measured. In this paper, a feature extraction-based image processing method is introduced to identify and separate distinct cleat groups from 3D X-ray μCT images. Kernels (filters) representing explicit cleat features of coal are built and cleat separation is successfully achieved by convolutional operations on 3D coal images. The new method is applied to a coal specimen with 80 mm in diameter and 100 mm in length acquired from an Anglo American Steelmaking Coal mine in the Bowen Basin, Queensland, Australia. It is demonstrated that the new method produces reliable cleat separation capable of defining individual cleats and preserving 3D topology after separation. Bedding-parallel fractures are also identified and separated, which has historically been challenging to delineate and rarely reported. A variety of cleat/fracture statistics is measured which not only can quantitatively characterize the cleat/fracture system but also can be used for DFN modeling. Finally, variability and heterogeneity with respect to the core axis are investigated. Significant heterogeneity is observed and suggests that the representative elementary volume (REV) of the cleat groups for engineering purposes may be a complex problem requiring careful consideration.
dc.description.sponsorshipWe acknowledge the funding support from the Australian Research Council, under the ARC Training Centre for Multiscale 3D Imaging, Modeling and Manufacturing.
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn1674-7755
dc.identifier.urihttps://hdl.handle.net/1885/733716193
dc.language.isoen_AUen_AU
dc.provenanceThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
dc.publisherChinese Academy of Sciences
dc.relationhttp://purl.org/au-research/grants/arc/IC180100008
dc.rights© 2024 The authors
dc.rights.licenseCreative Commons Attribution licence
dc.rights.urihttp://creativecommons.org/licenses/ by-nc-nd/4.0/
dc.sourceJournal of Rock Mechanics and Geotechnical Engineering
dc.subjectCleat separation
dc.subjectCleat statistics
dc.subjectFeature extraction
dc.subjectDiscrete fracture network (DFN) modeling
dc.titleA three-dimensional feature extraction-based method for coal cleat characterization using X-ray μCT and its application to a Bowen Basin coal specimen
dc.typeJournal article
dcterms.accessRightsOpen Access
local.bibliographicCitation.issue1
local.bibliographicCitation.lastpage166
local.bibliographicCitation.startpage153
local.contributor.affiliationZhang, Yulai, College of Science, ANU
local.contributor.affiliationTsang, Matthew, Anglo American Steelmaking Coal
local.contributor.affiliationKnackstedt, Mark, College of Science, ANU
local.contributor.affiliationTurner, Michael, College of Science, ANU
local.contributor.affiliationLatham, Shane, College of Science, ANU
local.contributor.affiliationMacaulay, Euan, Anglo American Steelmaking Coal
local.contributor.affiliationPitchers, Rhys, SCT Operations Pty Ltd
local.contributor.authoruidZhang, Yulai, u1091596
local.contributor.authoruidKnackstedt, Mark, u4031845
local.contributor.authoruidTurner, Michael, u3351931
local.contributor.authoruidLatham, Shane, u3813363
local.description.notesImported from ARIES
local.identifier.absfor401900 - Resources engineering and extractive metallurgy
local.identifier.ariespublicationa383154xPUB41807
local.identifier.citationvolume16
local.identifier.doi10.1016/j.jrmge.2023.04.001
local.identifier.scopusID2-s2.0-85160261134
local.publisher.urlhttps://www.sciencedirect.com/
local.type.statusPublished Version
publicationvolume.volumeNumber16

Downloads

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
1-s2.0-S1674775523001245-main.pdf
Size:
5.78 MB
Format:
Adobe Portable Document Format