Geoelectrochemical anomaly prospecting for uranium deposits in southeastern China

Liu, Panfeng; X, Luo; Wen, Ming; Zhang, Jiali; Zheng, Chaojie; Wen, Gao; Ouyang, Fei

doi:10.1016/j.apgeochem.2018.07.010

Cited by 9 publications

(6 citation statements)

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“…24 Under environmental conditions, uranium mineral NPs are found to be ubiquitous and have been identified in a number of studies. 7,[25][26][27][28] As highly-hydrolysable cation, U(IV) migrates predominantly in the form of pseudo-colloids and intrinsic colloids rather than in the soluble complexed form. UO 2 NPs formed as a result of bacteria mediated redox reactions have an influence on U migration in the far-field conditions of repositories.…”

Section: Introductionmentioning

confidence: 99%

Insight into the structure–property relationship of UO₂nanoparticles

Gerber

Romanchuk

Weiß

et al. 2021

Inorg. Chem. Front.

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Section: Introductionmentioning

confidence: 99%

Insight into the structure–property relationship of UO₂nanoparticles

Gerber

Romanchuk

Weiß

et al. 2021

Inorg. Chem. Front.

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“…The geo-electrochemical method breaks the dynamic balance of ions in underground rocks under the influence of an artificial electric field, causing anions and cations associated with mineralization to migrate towards the extraction electrode and eventually adsorb onto specific geo-electrochemical devices (foams) (Figure 3). By quantitatively analyzing the element content in the foam, the distribution pattern and geo-electrochemical characteristics can be determined, and the location of buried ore bodies can be inferred based on a comprehensive analysis of geological conditions, thereby achieving the goal of prospecting for mineral deposits [3,4,13].…”

Section: Methods and Technologymentioning

confidence: 99%

“…Since then, it has stimulated significant research work in China [1,2]. Luo [3][4][5][6][7][8] used the geo-electrochemical method to analyze over 30 different types of mineral deposits with overburden layers of varying thicknesses. The team repeatedly adjusted the technical parameters of the method and conducted comparative studies to summarize technical parameters suitable for different buried mineral resources, including independent power supply and fast positioning.…”

Section: Introductionmentioning

confidence: 99%

CoDA-Based Geo-Electrochemical Prospecting Prediction of Uranium Orebodies in Changjiang Area, Guangdong Province, China

Tang,

Sun,

Ouyang

et al. 2023

Minerals

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In order to achieve a breakthrough in the exploration of uranium orebodies in the Changjiang area of Guangdong province in China, the geo-electrochemical exploration method is used for mineral resource prediction. The logarithmically and isometric log-ratio (ilr) transformations are applied to the geo-electrochemical data in this study area to extract geochemical anomalies. The relationship between element associations and mineralization is revealed through descriptive statistical analysis and further biplot analysis. Then, the energy spectrum density–area fractal model (S-A model) is used to identify geochemical backgrounds and anomalies. The results show that: (i) the logarithmically and ilr-transformed data are more uniform, and they more or less obey the rules of normal distribution; (ii) the biplot shows that the ilr-transformed data eliminates the closure effect, and the robust principal component analysis (RPCA) has a better indicative significance for element associations—PC1 reveals the mineralization element association dominated by U and the multiple periods of hydrothermal activity; (iii) the S-A method could extract the local anomalies from different geochemical backgrounds, which indicates mineralization is more reliable. Finally, four favorable prospecting targets are delineated based on the geological and geochemical indicators.

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“…A prospecting method using geoelectrochemical probes has been proven in practice to be an effective way of pinpointing the locations of these hidden deep uranium deposits. The efficacy of the method has been verified through analyses of two carefully selected known uranium deposits: the Xiangshan and Shenyuan uranium deposits in southeastern China [13].…”

Section: Methodsmentioning

confidence: 99%

“…Analyses of geoelectrochemical probe samples presented in transections crossing the ore bodies revealed distinct anomalies of U and Mo distribution [13]. A recent study led by scientists at the Department of Energy's SLAC National Accelerator Laboratory helps describe how the contaminant cycles through the environment at former uranium mining sites and why it can be difficult to remove.…”

Section: Methodsmentioning

confidence: 99%

Association rules and regression linear model of the groundwater population by the evaluation of uranium

Lukman

Natalina

2019

MATEC Web Conf.

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The uranium available more on groundwater samples of certain types on the total alkalinity were relatively the same. But, the content of the uranium was higher in the samples. The multiple linear regression for pH as a dependent variable showed that the pH negatively correlated to the uranium, but the uranium was not significant for the linear regression model. The data of groundwater population from the samples of 127 with 12 variables of measurement of the Energy Department of the United States of America resulted in those association rules and linear regression models. The data has five factors of Producing horizon namely Ogallala Formation (TPO), Dockum Formation (TRD), Quartermaster Group (POQ), Whitehorse and Cloud Chief Group (PGWC), El Reno Group and Blaine Formation (PGEB). The step-wise linear regression for each of the five producing horizon codes was fitted to the data. Then, the regression models for each variable of producing horizon were obtained if pH was the dependent variable. If the Uranium was a dependent variable, then the regression models obtained were four only, with the model for PGEB was not able to be made. When pH as a dependent variable, it was depended upon Boron, Total alkalinity, and Bicarbonate.

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Geoelectrochemical anomaly prospecting for uranium deposits in southeastern China

Cited by 9 publications

References 10 publications

Insight into the structure–property relationship of UO₂nanoparticles

Insight into the structure–property relationship of UO₂nanoparticles

CoDA-Based Geo-Electrochemical Prospecting Prediction of Uranium Orebodies in Changjiang Area, Guangdong Province, China

Association rules and regression linear model of the groundwater population by the evaluation of uranium

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Geoelectrochemical anomaly prospecting for uranium deposits in southeastern China

Cited by 9 publications

References 10 publications

Insight into the structure–property relationship of UO2nanoparticles

Insight into the structure–property relationship of UO2nanoparticles

CoDA-Based Geo-Electrochemical Prospecting Prediction of Uranium Orebodies in Changjiang Area, Guangdong Province, China

Association rules and regression linear model of the groundwater population by the evaluation of uranium

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Insight into the structure–property relationship of UO₂nanoparticles

Insight into the structure–property relationship of UO₂nanoparticles