Skip navigation
Skip navigation

Uncertainty in mineral prospectivity prediction

Kraipeerapun, Pawalai; Fung, Chun Che; Brown, Warwick; Wong, Kok Wai; Gedeon, Tamas (Tom)

Description

This paper presents an approach to the prediction of mineral prospectivity that provides an assessment of uncertainty. Two feedforward backpropagation neural networks are used for the prediction. One network is used to predict degrees of favourability for deposit and another one is used to predict degrees of likelihood for barren, which is opposite to deposit. These two types of values are represented in the form of truth-membership and false-membership, respectively. Uncertainties of type...[Show more]

dc.contributor.authorKraipeerapun, Pawalai
dc.contributor.authorFung, Chun Che
dc.contributor.authorBrown, Warwick
dc.contributor.authorWong, Kok Wai
dc.contributor.authorGedeon, Tamas (Tom)
dc.date.accessioned2015-12-08T22:41:40Z
dc.identifier.isbn9783540464815
dc.identifier.urihttp://hdl.handle.net/1885/36747
dc.description.abstractThis paper presents an approach to the prediction of mineral prospectivity that provides an assessment of uncertainty. Two feedforward backpropagation neural networks are used for the prediction. One network is used to predict degrees of favourability for deposit and another one is used to predict degrees of likelihood for barren, which is opposite to deposit. These two types of values are represented in the form of truth-membership and false-membership, respectively. Uncertainties of type error in the prediction of these two memberships are estimated using multidimensional interpolation. These two memberships arid their uncertainties are combined to predict mineral deposit locations. The degree of uncertainty of type vagueness for each cell location is estimated and represented in the form of indeterminacy-membership value. The three memberships are then constituted into an interval neutrosophic set. Our approach improves classification performance compared to an existing technique applied only to the truth-membership value.
dc.publisherSpringer
dc.relation.ispartofNeural Information Processing: 13th International Conference, ICONIP 2006, Hong Kong October 3-6 2006, Proceedings, Part II
dc.relation.isversionof1st Edition
dc.subjectKeywords: Mineral prospectivity; Truth-membership value; Uncertainties; Classification (of information); Computer science; Error analysis; Interpolation; Neural networks; Uncertain systems; Minerals
dc.titleUncertainty in mineral prospectivity prediction
dc.typeBook chapter
local.description.notesImported from ARIES
dc.date.issued2006
local.identifier.absfor080108 - Neural, Evolutionary and Fuzzy Computation
local.identifier.ariespublicationu4251866xPUB140
local.type.statusPublished Version
local.contributor.affiliationKraipeerapun, Pawalai, Murdoch University
local.contributor.affiliationFung, Chun Che, Murdoch University
local.contributor.affiliationBrown, Warwick, University of Western Australia
local.contributor.affiliationWong, Kok Wai, Murdoch University
local.contributor.affiliationGedeon, Tamas (Tom), College of Engineering and Computer Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage841
local.bibliographicCitation.lastpage849
local.identifier.absseo840108 - Zinc Ore Exploration
dc.date.updated2015-12-08T10:29:45Z
local.bibliographicCitation.placeofpublicationBerlin
local.identifier.scopusID2-s2.0-33750735025
CollectionsANU Research Publications

Download

File Description SizeFormat Image
01_Kraipeerapun_Uncertainty_in_mineral_2006.pdf658.28 kBAdobe PDFThumbnail


Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator