Geostatistical and Remote Sensing Studies to Identify High Metallogenic Potential Regions in the Kivi Area of Iran; Minerals; Vol. 10, iss. 10

Geostatistical and Remote Sensing Studies to Identify High Metallogenic Potential Regions in the Kivi Area of Iran; Minerals; Vol. 10, iss. 10

Bibliografische gegevens
Parent link:Minerals
Vol. 10, iss. 10.— 2020.— [869, 15 p.]
Coauteur: Национальный исследовательский Томский политехнический университет Инженерная школа природных ресурсов Отделение геологии
Andere auteurs: Shirazy A. Adel, Ziaii M. M. Mansur Madzhid, Hezarkhani A. Ardeshir, Timkin T. V. Timothy Vasilyevich
Samenvatting:Title screen
The Kivi area in the East Azerbaijan Province of Iran is one of the country’s highest-potential regions for metal element exploration. The primary goal herein was to process the data obtained from geochemical, geostatistical, and remote sensing tools (in the form of stream sediment samples and satellite images) to identify metallic mineralization anomalies in the region. After correcting the raw stream sediment geochemical data, single-variable statistical processing was performed, and Ti and Zn were identified as the elements with the highest degree of contrast. The relationship among these elements was further investigated using correlation and hierarchical clustering analyses. Principal component analysis was then applied to determine the principal components related to these elements, which were subsequently plotted on a regional geological map. Elements related to Ti and Zn were identified using threshold limits of anomalous samples determined via linear discriminant analysis. Lithological units and alteration patterns were detected through remote sensing investigations on Landsat-8 images. Stream sediment geochemical and remote sensing survey results identified anomalous areas of Ti and Zn in the eastern part of the study region. Our results indicate that Ti and Zn are good pathfinder elements for further exploratory investigation in this area.
Taal:Engels
Gepubliceerd in: 2020
Onderwerpen:
электронный ресурс
труды учёных ТПУ
geochemical exploration
geostatistics
stream sediment samples
exploratory remote sensing
principal component analysis
linear discriminant analysis
геохимические исследования
геостатистика
Online toegang:http://earchive.tpu.ru/handle/11683/64797
https://doi.org/10.3390/min10100869
Formaat: Elektronisch Hoofdstuk
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=662878

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