IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Spatial Variability Analysis of Cu Content: A Case Study in Jiurui Copper Mining Area

Spatial Variability Analysis of Cu Content: A Case Study in Jiurui Copper Mining Area
View Sample PDF
Author(s): Huy A. Hoang (Hanoi University of Natural Resources and Environment, Hanoi, Vietnam), Tuyen D. Vu (Hanoi University of Natural Resources and Environment, Hanoi, Vietnam)and Thanh T. Nguyen (Hanoi University of Natural Resources and Environment, Hanoi, Vietnam)
Copyright: 2017
Volume: 8
Issue: 1
Pages: 13
Source title: International Journal of Applied Geospatial Research (IJAGR)
Editor(s)-in-Chief: Donald Patrick Albert (Sam Houston State University, USA)and Samuel Adu-Prah (Sam Houston State University, USA)
DOI: 10.4018/IJAGR.2017010105

Purchase

View Spatial Variability Analysis of Cu Content: A Case Study in Jiurui Copper Mining Area on the publisher's website for pricing and purchasing information.

Abstract

Conventional variogram has been widely applied to study spatial variability of geochemical data. In case of data is not normally distributed, the conventional estimator is biased. In this study, Cressie variogram and Moran correlogram were used to identify the degree of spatial variabilty of Cu content using 1341 stream sediment samples in Jiurui copper mining area. Cressie variogram was applied to reduce the influences of high values in identifying spatial variability in different directions. Moran correlogram was employed to study spatial correlation at different distances and the influences of data distribution on the results in quantitative ways. It was found that Cressie variogram yields stable robust estimates of the variogram with the maximum spatial variability of 12km for all directions; Moran correlogram provided more information, directly viewed and stable than variogram. Moran correlogram identified a strong positive spatial correlation at distances below 6km for the raw data and a strong positive spatial correlation at distances below 11km for Box-Cox transformed data.

Related Content

Mehrnaz Khademian, Rick Bunch. © 2024. 23 pages.
Dhanjit Deka, Jyoti Prasad Das, Madine Hazarika, Debashree Borah. © 2024. 25 pages.
Daniel D. Shults, John W. Nowlin, Joseph H. Massey, Michele L. Reba. © 2024. 22 pages.
Donald P. Albert. © 2023. 3 pages.
Henry N. N. Bulley, Oludunsin T. Arodudu, Esther A. Obonyo, Aniko Polo-Akpisso, Esther Shupel Ibrahim, Yazidhi Bamutaze. © 2023. 23 pages.
Karen Keller Kesler, Rick Bunch. © 2023. 22 pages.
Elaf A. Alyasiri, James L. Wilson, Ryan D. James. © 2023. 22 pages.
Body Bottom