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Surficial and Deep Earth Material Prediction from Geochemical Compositions

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dc.contributor.authorTalebi, Hassan
dc.contributor.authorMueller, U A
dc.contributor.authorTolosana-Delgado, Raimon
dc.contributor.authorGrunsky, E C
dc.contributor.authorMcKinley, Jennifer M.
dc.contributor.authorde Caritat, Patrice
dc.date.accessioned2020-02-12T02:01:27Z
dc.date.available2020-02-12T02:01:27Z
dc.date.issued2019-07
dc.date.updated2019-11-25T07:32:32Z
dc.description.abstractPrediction of true classes of surficial and deep earth materials using multivariate spatial data is a common challenge for geoscience modelers. Most geological processes leave a footprint that can be explored by geochemical data analysis. These footprints are normally complex statistical and spatial patterns buried deep in the high-dimensional compositional space. This paper proposes a spatial predictive model for classification of surficial and deep earth materials derived from the geochemical composition of surface regolith. The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive model is trained, and features are ranked based on their contribution to the predictive model. To generate potential and uncertainty maps, compositional data are simulated at unsampled locations via a chain of transformations (isometric log-ratio transformation followed by the flow anamorphosis) and geostatistical simulation. The simulated results are subsequently back-transformed to the original compositional space. The trained predictive model is used to estimate the probability of classes for simulated compositions. The proposed approach is illustrated through two case studies. In the first case study, the major crustal blocks of the Australian continent are predicted from the surface regolith geochemistry of the National Geochemical Survey of Australia project. The aim of the second case study is to discover the superficial deposits (peat) from the regional-scale soil geochemical data of the Tellus Project. The accuracy of the results in these two case studies confirms the usefulness of the proposed method for geological class prediction and geological process discovery.en_AU
dc.description.sponsorshipThe first three authors acknowledge financial support through DAAD-UA grant CodaBlock CoEstimation. The National Geochemical Survey of Australia project was part of the Australian Governments Onshore Energy Security Program 2006–2011, from which funding support is gratefully acknowledged. The Tellus Project was carried out by GSNI and funded by The Department for Enterprise, Trade and Investment (DETINI) and The Rural Development Programme through the Northern Ireland Programme for Building Sustainable Prosperity.en_AU
dc.format.extent23 pagesen_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn1520-7439en_AU
dc.identifier.urihttp://hdl.handle.net/1885/201658
dc.language.isoen_AUen_AU
dc.publisherSpringeren_AU
dc.rights© 2018 The Author(s)en_AU
dc.rights.licenseThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en_AU
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_AU
dc.sourceNatural Resources Researchen_AU
dc.subjectCompositional data, Log-ratio, Flow anamorphosis, Geostatistical simulation, Machine learningen_AU
dc.titleSurficial and Deep Earth Material Prediction from Geochemical Compositionsen_AU
dc.typeJournal articleen_AU
dcterms.accessRightsOpen Accessen_AU
dcterms.dateAccepted2018-10-20
local.bibliographicCitation.issue3en_AU
local.bibliographicCitation.lastpage891en_AU
local.bibliographicCitation.startpage869en_AU
local.contributor.affiliationTalebi, Hassan, Edith Cowan Universityen_AU
local.contributor.affiliationMueller, U A, Edith Cowan Universityen_AU
local.contributor.affiliationTolosana-Delgado, Raimon, Helmholtz Institute Freiberg for Resources Technologyen_AU
local.contributor.affiliationGrunsky, E C, University of Waterlooen_AU
local.contributor.affiliationMcKinley, Jennifer M., Queen’s University Belfasten_AU
local.contributor.affiliationDe Caritat, Patrice, College of Science, The Australian National Universityen_AU
local.contributor.authoremailrepository.admin@anu.edu.auen_AU
local.contributor.authoruidDe Caritat, Patrice, u3702178en_AU
local.description.notesImported from ARIESen_AU
local.identifier.absfor040201 - Exploration Geochemistryen_AU
local.identifier.absseo840199 - Mineral Exploration not elsewhere classifieden_AU
local.identifier.ariespublicationu3102795xPUB1870en_AU
local.identifier.citationvolume28en_AU
local.identifier.doi10.1007/s11053-018-9423-2en_AU
local.identifier.essn1573-8981en_AU
local.identifier.scopusID2-s2.0-85055995561
local.identifier.thomsonID4.67136E+11
local.identifier.uidSubmittedByu3102795en_AU
local.publisher.urlhttp://www.springerlink.com/en_AU
local.type.statusPublished Versionen_AU

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