Abstract
The target in this investigation is separation and delineation of geochemical anomalies for the single element Cu in Mesgaran mining area, eastern Iran. Mesgaran mining area is located in south part of Sarbishe county with about 29 Km distance to the county center. This region is part of an Ophiolite sequence and the copper anomalies seem to be related to a volcanic massive sulfide (VMS) deposit whose main part (massive sulfide Lens) has been eroded. In order to delineate Cu anomalies, the boxplot as an Exploratory Data Analysis (EDA) method and concentration-volume (C-V) Fractal modeling are employed. Both of the methods reveal low-deep anomalies which are highly correlated with geological and geophysical studies. As the main result of this study we show that Fractal modeling in spite of the Boxplot, is not recommended for complex geological settings. The proved shallow anomalies recorded by geophysical studies and defined by the used methods are in accordance to the stringer zone of a volcanic massive sulfide (VMS) deposit in Mesgaran mining area which means this region is the bottom of a VMS deposit and geochemical anomalies are related to the remained parts of the deposit.
Highlights
As the main result of this study we show that Fractal modeling in spite of the Boxplot, is not recommended for complex geological settings
The proved shallow anomalies recorded by geophysical studies and defined by the used methods are in accordance to the stringer zone of a volcanic massive sulfide (VMS) deposit in Mesgaran mining area which means this region is the bottom of a VMS deposit and geochemical anomalies are related to the remained parts of the deposit
Shallow anomalies as proved by this study are in consistence to the stringer zone of a volcanic massive sulfide (VMS) deposit in Mesgaran mining area
Summary
As early as 1962, several procedures had been recommended for selecting Threshold levels in order to identify outliers [1]. An alternative approach for understanding single-element distribution and defining outlier data is the use of Exploratory Data Analysis (EDA) [2]. The EDA methods have been firstly expressed by Tukey [3], used and developed by other researchers for geochemical anomalies modeling [4]-[9]. The Boxplot is one of the EDA methods widely used before as a useful instrument. This method divides dataset into four quartiles, which identifies the outliers and gives a schematic concept of data distribution as well. The Boxplot function is most informative if the true number of outliers is below 10% [10]
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