نمایش مختصر رکورد

dc.contributor.authorGhasemi, R.en_US
dc.contributor.authorTokhmechi, B.en_US
dc.contributor.authorBorg, G.en_US
dc.date.accessioned1399-07-09T03:32:16Zfa_IR
dc.date.accessioned2020-09-30T03:32:16Z
dc.date.available1399-07-09T03:32:16Zfa_IR
dc.date.available2020-09-30T03:32:16Z
dc.date.issued2018-01-01en_US
dc.date.issued1396-10-11fa_IR
dc.date.submitted2016-12-27en_US
dc.date.submitted1395-10-07fa_IR
dc.identifier.citationGhasemi, R., Tokhmechi, B., Borg, G.. (2018). Evaluation of effective factors in window optimization of fry analysis to identify mineralization pattern: Case study of Bavanat region, Iran. Journal of Mining and Environment, 9(1), 195-208. doi: 10.22044/jme.2017.909en_US
dc.identifier.issn2251-8592
dc.identifier.issn2251-8606
dc.identifier.urihttps://dx.doi.org/10.22044/jme.2017.909
dc.identifier.urihttp://jme.shahroodut.ac.ir/article_909.html
dc.identifier.urihttps://iranjournals.nlai.ir/handle/123456789/242740
dc.description.abstractThe known ore deposits and mineralization trends are important key exploration criteria in mineral exploration within a specific region. Fry analysis has conventionally been considered as a suitable method to determine the mineralization trends related to linear structures. Based upon literature sources, to date, no investigation has been carried out that includes the Sensitivity Analysis of Feature's Number (SAFN), Sensitivity Analysis of Window Size (SAWS), and Sensitivity Analysis of Spatial Distribution (SASD) of Fry analysis related to mineral locations. In this work, SAFN, SAWS, and SASD are performed by moving several different sub-windows among the main window in order to identify the main trends of mineralization by Fry analysis in the Bavanat region of Iran, which is qualified by its regional and local faults pattern. Based upon our investigation, the effectiveness of the window size and the number of features on Fry analysis are 15-30%. The determined main trends of sub-windows increase, whereas its distribution function of Fry outputs is more similar to the distribution function of Fry outputs of the main window. Moreover, the directions of rose diagrams could be changed due to the edge effects of marginal features around the selected window. However, by selecting an appropriate window, this problem can be solved. Additionally, by an appropriate window selection, the most suitable regional situation is an area that contains the largest number of deposits with a similar metallogenetic origin. Based upon our investigation, the distribution function of the Fry outputs is the main factor that directly controls the identified mineralization pattern of the selected windows.en_US
dc.format.extent3557
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoen_US
dc.publisherShahrood University of Technologyen_US
dc.relation.ispartofJournal of Mining and Environmenten_US
dc.relation.isversionofhttps://dx.doi.org/10.22044/jme.2017.909
dc.subjectMineral Exploration by Fry Analysisen_US
dc.subjectSensitivity Analysisen_US
dc.subjectMineralization Trenden_US
dc.subjectWindow Sizeen_US
dc.subjectFeatures Numberen_US
dc.subjectExploitationen_US
dc.titleEvaluation of effective factors in window optimization of fry analysis to identify mineralization pattern: Case study of Bavanat region, Iranen_US
dc.typeTexten_US
dc.typeCase Studyen_US
dc.contributor.departmentFaculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iranen_US
dc.contributor.departmentFaculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iranen_US
dc.contributor.departmentInstitute for Geosciences and Geography, Martin Luther University, Halle-Wittenberg, Germanyen_US
dc.citation.volume9
dc.citation.issue1
dc.citation.spage195
dc.citation.epage208
nlai.contributor.orcid0000-0003-1516-0624


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