Detection of alteration zones using the Dirichlet process Stick-Breaking model-based clustering algorithm to hyperion data: the case study of Kuh-Panj porphyry copper deposits, Southern Iran

Detection of hydrothermal alteration zones (HAZs) associated with porphyry copper systems using remote sensing imagery is a crucial stage for discovering high potential zone of ore mineralization. Statistical model-based clustering methods have great potential for automatic and accurate detection of...

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Published in:	Geocarto international Vol. 37; no. 25; pp. 9788 - 9816
Main Authors:	Yousefi, Mastoureh, Tabatabaei, Seyed Hassan, Rikhtehgaran, Reyhaneh, Pour, Amin Beiranvand, Pradhan, Biswajeet
Format:	Journal Article
Language:	English
Published:	Taylor & Francis 13.12.2022
Subjects:	algorithms Alteration zones case studies DPSB hyperion Iran mineralization porphyry copper systems remote sensing SAM Iran
ISSN:	1010-6049, 1752-0762, 1752-0762
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Abstract	Detection of hydrothermal alteration zones (HAZs) associated with porphyry copper systems using remote sensing imagery is a crucial stage for discovering high potential zone of ore mineralization. Statistical model-based clustering methods have great potential for automatic and accurate detection of hydrothermal alteration minerals using hyperspectral remote sensing imagery. In this research, the Dirichlet Process based on Stick-Breaking (DPSB) model-based clustering algorithm was implemented to hyperion remote sensing imagery to discriminate HAZs associated with the Kuh-Panj porphyry copper deposit, south, Iran. The DPSB clustering algorithm was implemented and subsequently compared with the k-means algorithm, CLARA clustering, hierarchical clustering, Gaussian finite mixture model (GFMM), Gaussian model for high-dimensional (GMHD) and spectral clustering as well as spectral angle mapping (SAM). Results derived from the DPSB model-based clustering algorithm show 88.6% accuracy in distinguishing propylitic, argillic, advanced argillic, propylitic-argillic and phyllic alteration zones. The DPSB algorithm can be broadly implemented to hyperspectral remote sensing imagery for detecting alteration zones associated with porphyry systems.
AbstractList	Detection of hydrothermal alteration zones (HAZs) associated with porphyry copper systems using remote sensing imagery is a crucial stage for discovering high potential zone of ore mineralization. Statistical model-based clustering methods have great potential for automatic and accurate detection of hydrothermal alteration minerals using hyperspectral remote sensing imagery. In this research, the Dirichlet Process based on Stick-Breaking (DPSB) model-based clustering algorithm was implemented to hyperion remote sensing imagery to discriminate HAZs associated with the Kuh-Panj porphyry copper deposit, south, Iran. The DPSB clustering algorithm was implemented and subsequently compared with the k-means algorithm, CLARA clustering, hierarchical clustering, Gaussian finite mixture model (GFMM), Gaussian model for high-dimensional (GMHD) and spectral clustering as well as spectral angle mapping (SAM). Results derived from the DPSB model-based clustering algorithm show 88.6% accuracy in distinguishing propylitic, argillic, advanced argillic, propylitic-argillic and phyllic alteration zones. The DPSB algorithm can be broadly implemented to hyperspectral remote sensing imagery for detecting alteration zones associated with porphyry systems.
Author	Yousefi, Mastoureh Pradhan, Biswajeet Tabatabaei, Seyed Hassan Rikhtehgaran, Reyhaneh Pour, Amin Beiranvand
Author_xml	– sequence: 1 givenname: Mastoureh orcidid: 0000-0002-8412-0792 surname: Yousefi fullname: Yousefi, Mastoureh organization: Department of Mining Engineering, Isfahan University of Technology – sequence: 2 givenname: Seyed Hassan orcidid: 0000-0003-2869-748X surname: Tabatabaei fullname: Tabatabaei, Seyed Hassan organization: Department of Mining Engineering, Isfahan University of Technology – sequence: 3 givenname: Reyhaneh orcidid: 0000-0001-5815-2769 surname: Rikhtehgaran fullname: Rikhtehgaran, Reyhaneh organization: Department of Mathematical Sciences, Isfahan University of Technology – sequence: 4 givenname: Amin Beiranvand orcidid: 0000-0001-8783-5120 surname: Pour fullname: Pour, Amin Beiranvand organization: Institute of Oceanography and Environment (INOS), Universiti Malaysia Terengganu (UMT) – sequence: 5 givenname: Biswajeet orcidid: 0000-0001-9863-2054 surname: Pradhan fullname: Pradhan, Biswajeet organization: Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia
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SubjectTerms	algorithms Alteration zones case studies DPSB hyperion Iran mineralization porphyry copper systems remote sensing SAM
Title	Detection of alteration zones using the Dirichlet process Stick-Breaking model-based clustering algorithm to hyperion data: the case study of Kuh-Panj porphyry copper deposits, Southern Iran
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