Application of dirichlet process and support vector machine techniques for mapping alteration zones associated with porphyry copper deposit using aster remote sensing imagery

Yousefi, M; Tabatabaei, SH; Rikhtehgaran, R; Pour, AB; Pradhan, B

Application of dirichlet process and support vector machine techniques for mapping alteration zones associated with porphyry copper deposit using aster remote sensing imagery

Yousefi, M Tabatabaei, SH Rikhtehgaran, R Pour, AB Pradhan, B

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Minerals, 2021, 11, (11), pp. 1-25
Issue Date:: 2021-11-01

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Field	Value	Language
dc.contributor.author	Yousefi, M
dc.contributor.author	Tabatabaei, SH
dc.contributor.author	Rikhtehgaran, R
dc.contributor.author	Pour, AB
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.date.accessioned	2022-02-17T05:09:02Z
dc.date.available	2022-02-17T05:09:02Z
dc.date.issued	2021-11-01
dc.identifier.citation	Minerals, 2021, 11, (11), pp. 1-25
dc.identifier.issn	2075-163X
dc.identifier.issn	2075-163X
dc.identifier.uri	http://hdl.handle.net/10453/154645
dc.description.abstract	The application of machine learning (ML) algorithms for processing remote sensing data is momentous, particularly for mapping hydrothermal alteration zones associated with porphyry copper deposits. The unsupervised Dirichlet Process (DP) and the supervised Support Vector Machine (SVM) techniques can be executed for mapping hydrothermal alteration zones associated with porphyry copper deposits. The main objective of this investigation is to practice an algorithm that can accurately model the best training data as input for supervised methods such as SVM. For this purpose, the Zefreh porphyry copper deposit located in the Urumieh-Dokhtar Magmatic Arc (UDMA) of central Iran was selected and used as training data. Initially, using ASTER data, different alteration zones of the Zefreh porphyry copper deposit were detected by Band Ratio, Relative Band Depth (RBD), Linear Spectral Unmixing (LSU), Spectral Feature Fitting (SFF), and Orthogonal Subspace Projection (OSP) techniques. Then, using the DP method, the exact extent of each alteration was determined. Finally, the detected alterations were used as training data to identify similar alteration zones in full scene of ASTER using SVM and Spectral Angle Mapper (SAM) methods. Several high potential zones were identified in the study area. Field surveys and laboratory analysis were used to validate the image processing results. This investigation demonstrates that the application of the SVM algorithm for mapping hydrothermal alteration zones associated with porphyry copper deposits is broadly applicable to ASTER data and can be used for prospectivity mapping in many metallogenic provinces around the world.
dc.language	English
dc.publisher	MDPI AG
dc.relation.ispartof	Minerals
dc.relation.isbasedon	10.3390/min11111235
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0403 Geology, 0502 Environmental Science and Management, 0914 Resources Engineering and Extractive Metallurgy
dc.title	Application of dirichlet process and support vector machine techniques for mapping alteration zones associated with porphyry copper deposit using aster remote sensing imagery
dc.type	Journal Article
utslib.citation.volume	11
utslib.for	0403 Geology
utslib.for	0502 Environmental Science and Management
utslib.for	0914 Resources Engineering and Extractive Metallurgy
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-02-17T05:08:56Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	11

Abstract:

The application of machine learning (ML) algorithms for processing remote sensing data is momentous, particularly for mapping hydrothermal alteration zones associated with porphyry copper deposits. The unsupervised Dirichlet Process (DP) and the supervised Support Vector Machine (SVM) techniques can be executed for mapping hydrothermal alteration zones associated with porphyry copper deposits. The main objective of this investigation is to practice an algorithm that can accurately model the best training data as input for supervised methods such as SVM. For this purpose, the Zefreh porphyry copper deposit located in the Urumieh-Dokhtar Magmatic Arc (UDMA) of central Iran was selected and used as training data. Initially, using ASTER data, different alteration zones of the Zefreh porphyry copper deposit were detected by Band Ratio, Relative Band Depth (RBD), Linear Spectral Unmixing (LSU), Spectral Feature Fitting (SFF), and Orthogonal Subspace Projection (OSP) techniques. Then, using the DP method, the exact extent of each alteration was determined. Finally, the detected alterations were used as training data to identify similar alteration zones in full scene of ASTER using SVM and Spectral Angle Mapper (SAM) methods. Several high potential zones were identified in the study area. Field surveys and laboratory analysis were used to validate the image processing results. This investigation demonstrates that the application of the SVM algorithm for mapping hydrothermal alteration zones associated with porphyry copper deposits is broadly applicable to ASTER data and can be used for prospectivity mapping in many metallogenic provinces around the world.

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http://hdl.handle.net/10453/154645