RUSSIAN JOURNAL OF EARTH SCIENCES, VOL. 20, doi:10.2205/2020ES000708

Identification of Univariate Geochemical Anomalies Using Hot Spot Analysis

Tien Nguyen Thanh et al.


Identification of geochemical anomalies plays an important role in mineral exploration. Commonly used methods for geochemical anomalous separation from background fails to take into account for spatial dependence among observations in such geochemical data. This study proposed a new method for identifying univariate geochemical anomalies by means of hot spot analysis. The Cu concentration distribution is first studied. Getis-Ord statistic is then used to measure the degree of spatial autocorrelation among Cu samples at different local scales. The areas of geochemical anomalies were finally extracted based on Getis z-score > 2.58, 1.96 < Getis z-score < 2.58, and 1.65 < Getis z-score < 1.96 corresponding to hot spots with 99%, 95%, and 90% confidence levels, respectively. The proposed method was illustrated by using 1842 stream sediment Cu samples associated with Cu-Au-Mo and Sn mineralizations in the Jiurui ore district (Jiangxi province, southeastern China). It was found that (i) geochemical anomalies (significant hot spots) were strongly correlated with 13 known Cu-Au-Mo and Cu-Mo deposits, especially at spatial scales of 2, 4 and 6km where high degrees of positive spatial autocorrelation of Cu samples were detected; (ii) weak Cu anomalies associated with a magmatic-hydrothermal related Sn and polymetallic mineralization were successfully identified in the Zengiialong and Jianfengpo Sn deposits at the three first spatial scales, which were not detected using local Moran’s I. These study results demonstrate hot spot analysis is a potential method for geochemical anomaly identification.

KEYWORDS: Univariate geochemical anomalies, hot spot analysis, spatial dependence, Jiurui copper district (China)..

Submitted 24.02.2020, accepted 13.03.2020.


Citation: Thanh Tien Nguyen et al. (2020), Identification of Univariate Geochemical Anomalies Using Hot Spot Analysis, Russ. J. Earth Sci., 20, doi:10.2205/2020ES000708.