Statistical Analyses of La, Ce, Nd, Y, Nb, Ti, P, and Zr in Bedrocks and Their Significance in Geochemical Exploration at the Um Garayat Gold Mine Area, Eastern Desert, Egypt

El-Makky, Ahmed M.

doi:10.1007/s11053-011-9144-2

Cited by 36 publications

(9 citation statements)

References 22 publications

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“…A primary purpose of geochemical data analysis is to identify geochemical/geological processes based on statistical and spatial variations in a data set (e.g., Aitchison, 1986;Bonham-Carter et al, 1988, 1989Cheng et a., 2000;Singer and Kouda, 2001;Carranza, 2010;El-Makky, 2011;El-Makky and Sediek, 2012;Grunsky et al, 2014;Luz et al, 2014).…”

Section: -2 Dealing With Censored Geochemical Datamentioning

confidence: 99%

Analysis and mapping of soil geochemical anomalies: Implications for bedrock mapping and gold exploration in Giyani area, South Africa

Sadeghi

Billay

Carranza

2015

Journal of Geochemical Exploration

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Section: -2 Dealing With Censored Geochemical Datamentioning

confidence: 99%

Analysis and mapping of soil geochemical anomalies: Implications for bedrock mapping and gold exploration in Giyani area, South Africa

Sadeghi

Billay

Carranza

2015

Journal of Geochemical Exploration

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“…The elemental correlation coefficients and paragenesis were found by applying bivariate and multivariate statistical analysis [23] [24] to the geochemical data using SPSS 18 statistical software package. Bivariate matrix of correlation coefficients was calculated by the Pearson correlation method [25].…”

Section: Methodsmentioning

confidence: 99%

Copper, Zinc, and Lead Mineral Prospectivity Mapping in the North of Tafresh, Markazi Province, Central Iran, Using the AHP-OWA Method

Niyeh¹,

Jafarirad²,

Karami³

et al. 2017

OJG

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The reason of this research is to identify the favorable areas for copper, zinc, and lead mineralization in the western part of the 1:100,000 Tafresh geological Sheet in the Urmia-Dokhtar structural zone of Iran. Effective data layers for mineralization, such as geology, geochemistry, structures, and satellite images, were analyzed and then integrated using the AHP-OWA method to identify favorable areas. Geochemical stream samples were analyzed by univariate, multivariate, and classical statistical methods and revealed the first, second, and third class anomalies for copper, zinc, and lead in the study region. Detection of hydrothermal alteration zones by Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery in various algorithms, such as Relative absorption Band Depth (RBD), Minimum Noise Fraction (MNF), and Least Square Fit (LS-Fit), shows that argillic, phyllic, propylitic, and iron oxide alterations develop around the faults in the area under study. The favorable areas for copper, zinc, and lead mineralization have been identified by a combination of evidence maps of lithology, faults, dikes, geochemistry, and alteration data layers. Field observations in the area under study have confirmed the results.

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“…Recent development of analytical methods and computational resources facilitates the implementation of geochemical mapping and its use in natural resources management (Antunes and Albuquerque 2013). In particular multivariate data analysis has been widely applied to characterize the statistical patterns of geochemical data (Carranza 2010; El-Makky 2011; Sadeghi et al 2013, 2014; Viladevall et al 1999; Zuo 2011). These methods commonly aim to reduce the dimensionality of the problem through the creation of a few relevant factors that explain a large proportion of variance in a multivariate data set (Davis 2002; Reimann et al 2008).…”

Section: Introductionmentioning

confidence: 99%

A Multivariate Geostatistical Methodology to Delineate Areas of Potential Interest for Future Sedimentary Gold Exploration

2016

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This paper describes a multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration, with an application to an abandoned sedimentary gold mining region in Portugal. The main challenge was the existence of only a dozen gold measurements confined to the grounds of the old gold mines, which precluded the application of traditional interpolation techniques, such as cokriging. The analysis could, however, capitalize on 376 stream sediment samples that were analyzed for twenty two elements. Gold (Au) was first predicted at all 376 locations using linear regression (R2=0.798) and four metals (Fe, As, Sn and W), which are known to be mostly associated with the local gold’s paragenesis. One hundred realizations of the spatial distribution of gold content were generated using sequential indicator simulation and a soft indicator coding of regression estimates, to supplement the hard indicator coding of gold measurements. Each simulated map then underwent a local cluster analysis to identify significant aggregates of low or high values. The one hundred classified maps were processed to derive the most likely classification of each simulated node and the associated probability of occurrence. Examining the distribution of the hot-spots and cold-spots reveals a clear enrichment in Au along the Erges River downstream from the old sedimentary mineralization.

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Statistical Analyses of La, Ce, Nd, Y, Nb, Ti, P, and Zr in Bedrocks and Their Significance in Geochemical Exploration at the Um Garayat Gold Mine Area, Eastern Desert, Egypt

Cited by 36 publications

References 22 publications

Analysis and mapping of soil geochemical anomalies: Implications for bedrock mapping and gold exploration in Giyani area, South Africa

Analysis and mapping of soil geochemical anomalies: Implications for bedrock mapping and gold exploration in Giyani area, South Africa

Copper, Zinc, and Lead Mineral Prospectivity Mapping in the North of Tafresh, Markazi Province, Central Iran, Using the AHP-OWA Method

A Multivariate Geostatistical Methodology to Delineate Areas of Potential Interest for Future Sedimentary Gold Exploration

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