Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery

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...

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Autores principales:	Mastoureh Yousefi, Seyed Hassan Tabatabaei, Reyhaneh Rikhtehgaran, Amin Beiranvand Pour, Biswajeet Pradhan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	porphyry copper deposits ASTER machine learning DP SVM SAM Mineralogy QE351-399.2
Acceso en línea:	https://doaj.org/article/8592dc01e06644e6a1437f480d9e7e96
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spelling	oai:doaj.org-article:8592dc01e06644e6a1437f480d9e7e962021-11-25T18:26:28ZApplication of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery10.3390/min111112352075-163Xhttps://doaj.org/article/8592dc01e06644e6a1437f480d9e7e962021-11-01T00:00:00Zhttps://www.mdpi.com/2075-163X/11/11/1235https://doaj.org/toc/2075-163XThe 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.Mastoureh YousefiSeyed Hassan TabatabaeiReyhaneh RikhtehgaranAmin Beiranvand PourBiswajeet PradhanMDPI AGarticleporphyry copper depositsASTERmachine learningDPSVMSAMMineralogyQE351-399.2ENMinerals, Vol 11, Iss 1235, p 1235 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	porphyry copper deposits ASTER machine learning DP SVM SAM Mineralogy QE351-399.2
spellingShingle	porphyry copper deposits ASTER machine learning DP SVM SAM Mineralogy QE351-399.2 Mastoureh Yousefi Seyed Hassan Tabatabaei Reyhaneh Rikhtehgaran Amin Beiranvand Pour Biswajeet Pradhan Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery
description	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.
format	article
author	Mastoureh Yousefi Seyed Hassan Tabatabaei Reyhaneh Rikhtehgaran Amin Beiranvand Pour Biswajeet Pradhan
author_facet	Mastoureh Yousefi Seyed Hassan Tabatabaei Reyhaneh Rikhtehgaran Amin Beiranvand Pour Biswajeet Pradhan
author_sort	Mastoureh Yousefi
title	Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery
title_short	Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery
title_full	Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery
title_fullStr	Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery
title_full_unstemmed	Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery
title_sort	application of dirichlet process and support vector machine techniques for mapping alteration zones associated with porphyry copper deposit using aster remote sensing imagery
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8592dc01e06644e6a1437f480d9e7e96
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Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery

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