Mineralization zoning in Yindongzi-Daxigou barite-siderite, silver-polymetallic deposits in the Qinling orogen, China

Fang, Wangxi; Hu, Ruizhong; Huang, Zhuanying

doi:10.1007/bf03166848

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…The area with the content no more than 30 mg/kg accounted for 84.1% of the total area. In addition, the content of Cu is the highest; Pb is the second highest in the study area, which is consistent with the geochemical investigation mapping data [21]. Meanwhile, by comparing the two elements estimated in this study with the maximum and arithmetic mean values of the reference values of soil elements in Shaanxi Province, it is found that the content of the two elements in some parts of the study area exceed the average reference values of soil elements in Shaanxi Province, which indicates that the soil in some areas has been polluted by these two heavy metals since 1990.…”

Section: Resultssupporting

confidence: 89%

“…It is the largest siderite operation in Shaanxi, accounting for 47.6% of the total iron ore reserves. In 1982, the Northwest Metallurgical Geological Exploration Company found that the Daxigou-Yindongzi deposit is rich in copper, lead, zinc, silver, etc., in and around the mining area [21]. Mining in the area officially began in 1988, and open-pit mining has mainly been used since 2007.…”

Section: The Study Areamentioning

confidence: 99%

“…By comparing the detected values of each element with their reference values published by State Environmental Protection Administration of China [23], according to the degree to which the detected values exceeded the reference and combined with the enrichment degree of heavy metals in Daxigou-Yindongzi polymetallic ore deposit [21] and the total cost of soil samples to be analyzed in the laboratory, Cu and Pb were determined to be the elements of interest in this study.…”

Section: Soil Sample Analysis and Preprocessingmentioning

confidence: 99%

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Retrieval of Soil Heavy Metal Content for Environment Monitoring in Mining Area via Transfer Learning

Yang,

Cui,

Cheng

et al. 2023

Sustainability

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Monitoring environmental pollution sources is an ongoing issue that must be addressed to reduce risks to public health, food safety, and the environment. However, retrieving topsoil heavy metal content at a low cost for environmental monitoring in mining areas is challenging. Therefore, this study proposes a network model based on transfer learning theory and a back propagation (BP) network optimized by a genetic algorithm (GA), taking the Daxigou mining area in Shaanxi Province, China, as a case study. Firstly, visible and near-infrared spectrum data from Landsat8 satellite images, digital elevation models, and geochemical data from field-collected soil samples were used to extract environmental factor candidates indicating the content and spatial distribution of certain heavy metals, including copper (Cu) and lead (Pb). Secondly, each element was correlated with environmental factors and a multicollinearity test was performed to determine the optimal factor set. Then, the BP network optimized by GA was pre-trained with sample data collected in 2017 and retrained with minimal sample data from 2019 using the parameter transfer learning method, allowing spatial distribution mapping of the Cu and Pb content in topsoil of the Daxigou mining area in 2019. From the validation results using field-collected data, the root mean square error (RMSE) and mean relative error (MRE) values using the proposed model, respectively, reduced by 4.688 mg/kg and 1.533 mg/kg for Cu and reduced by 1.586 mg/kg and 1.232 mg/kg for Pb compared to the traditional GA-BP model. Thus, conclusions can be drawn that our proposed Tr-GA-BP network performs well, requiring 16 training samples collected in 2019. In addition, the content of Cu is the highest; Pb is the second highest in the study area. Both of them were spatially distributed mainly in the exploitation, slag stacking, roadside, etc., consistent with field investigation results.

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Section: Resultssupporting

confidence: 89%

Section: The Study Areamentioning

confidence: 99%

Section: Soil Sample Analysis and Preprocessingmentioning

confidence: 99%

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Retrieval of Soil Heavy Metal Content for Environment Monitoring in Mining Area via Transfer Learning

Yang,

Cui,

Cheng

et al. 2023

Sustainability

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“…The mine is one of the largest siderite mines in China, accounting for 47.6% of the total iron ore reserves in Shaanxi Province. In 1982, it was discovered that the Daxigou-Yindongzi deposit was rich in Cu, Pb, Zn, Ag, and other minerals [21]. The mining activity officially began through open-pit mining in 1988.…”

Section: Overview Of the Study Areamentioning

confidence: 99%

Time-Domain Transfer Learning for Accurate Heavy Metal Concentration Retrieval Using Remote Sensing and TrAdaBoost Algorithm: A Case Study of Daxigou, China

Yang,

Tian,

Bai

et al. 2024

Water

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Traditionally, the assessment of heavy metal concentrations using remote sensing technology is sample-intensive, with expensive model development. Using a mining area case study of Daxigou, China, we propose a cross-time-domain transfer learning model to monitor heavy metal pollution using samples collected from different time domains. Specifically, spectral indices derived from Landsat 8 multispectral images, terrain, and other auxiliary data correlative to soil heavy metals were prepared. A cross time-domain sample transfer learning model proposed in the paper based on the TrAdaBoost algorithm was used for the Cu content mapping in the topsoil by selective use of soil samples acquired in 2017 and 2019. We found that the proposed model accurately estimated the concentration of Cu in the topsoil of the mining area in 2019 and performed better than the traditional TrAdaBoost algorithms. The goodness of fit (R2) of the test set increased from 0.55 to 0.66; the relative prediction deviation (RPD) increased from 1.37 to 1.76; and finally, the root-mean-square deviation (RMSE), decreased from 8.33 to 7.24 mg·kg−1.The proposed model is potentially applicable to more accurate and inexpensive monitoring of heavy metals, facilitating remediation-related efforts.

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Genesis of the Tuokesai Zn-Pb deposit in the Sayram Massif, Xinjiang, NW China: Constraints from geology, jasperite geochemistry and Si-S-Pb isotopes

Man

Xue

Zhao

et al. 2020

Ore Geology Reviews

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Mineralization zoning in Yindongzi-Daxigou barite-siderite, silver-polymetallic deposits in the Qinling orogen, China

Cited by 4 publications

References 8 publications

Retrieval of Soil Heavy Metal Content for Environment Monitoring in Mining Area via Transfer Learning

Retrieval of Soil Heavy Metal Content for Environment Monitoring in Mining Area via Transfer Learning

Time-Domain Transfer Learning for Accurate Heavy Metal Concentration Retrieval Using Remote Sensing and TrAdaBoost Algorithm: A Case Study of Daxigou, China

Genesis of the Tuokesai Zn-Pb deposit in the Sayram Massif, Xinjiang, NW China: Constraints from geology, jasperite geochemistry and Si-S-Pb isotopes

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