3D Visualization Monitoring and Early Warning System of a Tailings Dam—Gold Copper Mine Tailings Dam in Zijinshan, Fujian, China

Nie, Wen; Luo, Min; Wang, Yunmin; Li, Ruixiang

doi:10.3389/feart.2022.800924

Cited by 13 publications

(4 citation statements)

References 52 publications

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“…Previous studies have demonstrated the great impacts of tailings on environmental quality and the ecological landscape (Nie et al, 2022). For example, affected by Hg mining deposits, the average contents of Hg and Ni in agricultural soil in Gongguan of Shaanxi province exceeded the national secondary standard value by 3.9, and 4.0 times, respectively (Zhu et al, 2018).…”

Section: Introductionmentioning

confidence: 98%

Heavy metal pollution and risk assessment of tailings in one low-grade copper sulfide mine

Zhao

Chen²,

Liu³

et al. 2023

Front. Environ. Sci.

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Analyzing the pollution level and ecological risk of heavy metals in tailings is a necessary step for conducting revegetation after a tailings pond’s closure. Herein, we determined the heavy metal pollution status and ecological risk in one low-grade copper sulfide tailings pond using chemical and mineralogical analysis, chemical extraction, and ecological risk assessment. The results showed that the low-grade copper sulfide tailings displayed a low pollution status and exhibited a very low ecological risk. Among six heavy metals (Cu, Pb, Zn, As, Cr, and Cd), only Cu (53.7 mg/kg) slightly exceeded its standard value limit (50 mg/kg), and was the main pollutant in the tailings. Due to its high toxicity, As had the maximum contribution to the potential ecological risk in the tailings. Pb, Zn, Cr, and Cd in the tailings were practically of no pollution, and at low or none potential ecological risk. In order to conduct revegetation in the tailings pond, more attention should be paid to the acidity change of tailings and its impact on the chemical activity and bioavailability of Cu and As. This research provides a theoretical basis for heavy metals risk control and revegetation in the low-grade copper sulfide tailings pond.

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Section: Introductionmentioning

confidence: 98%

Heavy metal pollution and risk assessment of tailings in one low-grade copper sulfide mine

Zhao

Chen²,

Liu³

et al. 2023

Front. Environ. Sci.

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“…Significant progress has been made in domestic and international research on 3S technology in the field of mine monitoring, yielding important results. Wen Nie et al [6] carry out the design and research of tailing dam monitoring and early warning system based on computer, cloud computing, GIS, and time series prediction technology. 3D geographic information system can manage multisource data effectively and realize two-and three-dimensional visual displays.…”

Section: Introductionmentioning

confidence: 99%

“…3S technology has made notable advancements in addressing issues related to limited data visualization, suboptimal data management, and the feasibility of point-surface combination prediction and early warning systems. Through the application of this technology, highprecision prediction of mine safety risks and real-time disaster early warning can be achieved [6] . This paper summarizes the characteristics and advantages of 3S technology, builds upon previous researchers' approaches, and proposes four suggestions for improvement, with the aim of establishing an efficient, accurate, and secure monitoring system to enhance the overall efficiency of mining area monitoring projects.…”

Section: Introductionmentioning

confidence: 99%

Prospects and applications of 3S technology in the field of risk monitoring in mining areas

Yang,

Liu,

Xue

et al. 2024

Second International Conference on Environmental Remote Sensing and Geographic Information Technology (ERSGIT 2023)

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Risk monitoring in mining areas is an indispensable part of the process of developing and managing natural resources in mining areas, and it involves the assessment of the safety of mining areas. Accurate acquisition and analysis of spatial geographic information of mining areas can better reduce the labour intensity of the mining industry, reduce the damage to the natural environment, and reduce the disasters that may be encountered in the mining process. With the continuous progress of space information technology, the emerging global navigation satellite system GNSS (Global Navigation Satellite System), geographic information processing system GIS (Geographic Information System) and remote sensing system RS (Remote Sensing) common role of 3S technology in the mining industry. 3S technology in the field of mine monitoring shows a strong potential and advantages. The purpose of this paper is to discuss the characteristics, specific operational details, and modes of mutual cooperation and joint collaboration of 3S technologies in mine monitoring, in order to provide suggestions for future mine risk monitoring.

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“…The tailings dam is generally designed as a permeable structure, and the saturation line of dam is called lifeline. Safety evaluation based on monitoring data of rainfall, saturation line, water level and deformation of tailings dam is a common means to solve safety early warning problems (Wang et al, 2018), and the monitoring system of tailings dam is becoming more and more perfect (Nie et al, 2022). The depth of saturation line is the direct external response to the change of seepage field of tailings dam.…”

Section: Introductionmentioning

confidence: 99%

Anomaly identification of monitoring data and safety evaluation method of tailings dam

Dong

Yang²,

Yan³

et al. 2022

Front. Earth Sci.

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The seepage field of tailings dam is closely related to the safety state. Real-time evaluation of seepage field safety based on monitoring data is of great significance to ensure the safe operation of tailings pond. The premise of accurately evaluating the safety status is to ensure reliability of the data, and it is necessary to identify the anomalies of the monitoring data. Because of the complex influence factors of seepage field of tailings dam, the traditional anomaly identification method based on regression model fails due to its low fitting accuracy. Therefore, a novel abnormal identification method of monitoring data based on improved cloud model and radial basis function neural network model, which can accurately identify anomaly data and distinguish the environmental quantity response. Based on the coupling relationship between the seepage field and the slope stability, the surrogate model between the depth of saturation line and the safety factor of slope stability is constructed, and the real-time safety evaluation method of seepage field is put forward. The proposed methods are applied to an engineering example. The misjudgment rates of the abnormal data identification method are less than 5%, and it has better applicability than the traditional regression model. The constructed real-time safety evaluation model accurately reflected the health status of the seepage field, and realized the quantitative assessment of the safety of tailings dam. This provides reliable data support for the operation management and the risk control of tailings pond.

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3D Visualization Monitoring and Early Warning System of a Tailings Dam—Gold Copper Mine Tailings Dam in Zijinshan, Fujian, China

Cited by 13 publications

References 52 publications

Heavy metal pollution and risk assessment of tailings in one low-grade copper sulfide mine

Heavy metal pollution and risk assessment of tailings in one low-grade copper sulfide mine

Prospects and applications of 3S technology in the field of risk monitoring in mining areas

Anomaly identification of monitoring data and safety evaluation method of tailings dam

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