Identification of Mine Water Sources Based on the Spatial and Chemical Characteristics of Bedrock Brines: A Case Study of the Xinli Gold Mine

Duan, Xueliang; Ma, Feifei; Gu, Hengfei; Guo, Jie; Zhao, Haijun; Liu, Guowei; Liu, Shuaiqi

doi:10.1007/s10230-021-00810-1

Cited by 16 publications

(10 citation statements)

References 39 publications

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“…However, the rate of reduction gradually slows down, and finally, the proportion of seawater is stable at 0.6~0.7. Other experts have analyzed the changes in the proportion of seawater in the early stages of mining in the Xinli mining area and found that the proportion of seawater showed a trend of increasing year by year [23]. Thus, the analysis results of the two papers show that the proportion of seawater first increases and then decreases with the progress of mining.…”

Section: Mixing Ratio Calculation and Deviation Analysismentioning

confidence: 99%

“…Principal component analysis replaces the original water sample information with the extracted principal components, which is a linear combination of the original information. Although it contains complete water sample information, it also results in the main information becoming "neutralized" by the secondary information, which weakens the indication role of the key information and may lead to an inability to accurately identify the water source [23].In this study, principal component analysis, cluster analysis, hydrogeochemical analysis, the maximum likelihood method, and other methods were used to identify the water sources in the Xishan mining area [8,16,24]. However, due to the limitation of timescales and the research area, the common problem with the methods was that only one type of brine was considered in all of the sublevels.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, hierarchical multi-index analysis and principal component analysis methods were used to identify the water inrush patterns and to calculate the mixing ratios. Duan et al demonstrated that hierarchical multi-index analysis is an effective method in complex water inrush pattern recognition [23]. Based on the results of water chemistry and stable isotope analysis, combined with the spatial distribution of brine, three different bedrock brines were identified: shallow brine, central brine, and deep brine; these are the possible end elements.…”

Section: Introductionmentioning

confidence: 99%

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Interpreting Mine Water Sources and Determining Mixing Ratios Based on the Spatial and Chemical Characteristics of Bedrock Brines in a Coastal Mine

Song

Guo

et al. 2023

Water

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Water inrush caused by mining below the seafloor seriously affects the safety and production of mines. Identifying the end element of mine inrush and accurately calculating the mixing ratios of end elements are the basis for a reasonable evaluation of water inrush risk. Based on hydrogeochemical and stable isotope indexes, combined with the spatial distribution characteristics of brine, the classification of brine in the study area was preliminarily determined as follows: shallow brine, middle brine, and deep brine (while previous studies have only classified bedrock brine as one category). Hierarchical multi-index analysis was used to identify the inrush end elements in the different sublevels, and an end-element mixed model was determined according to the analysis results of the four pairs of evaluation indexes (Cl−–δ18 O, Cl−–Ca2+, Cl−–Mg2+, and Cl−–Na+). Through a comparison with the deviation analysis results of previous studies, it was shown that this method is suitable for mine-water-source identification when under complex hydrogeology conditions. According to the calculation results of the mixing ratio, the seawater ratio shows, within the mining process, a trend of first increasing, then decreasing, and finally stabilizing. This trend is controlled by disturbance stress, self-weight stress, and tectonic stress. The vertical zonation of the seawater proportions indicates that seawater mainly recharges mine water through vertical fractures. The difference in the proportion of seawater at the water inrush points of the −600 m sublevels indicates that the F3 fault and the northwest water-conducting fracture zone may be the preferred flow channels for seawater to recharge mine water. The research results are of great significance to promote the safe mining of coastal mines around the world.

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Section: Mixing Ratio Calculation and Deviation Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interpreting Mine Water Sources and Determining Mixing Ratios Based on the Spatial and Chemical Characteristics of Bedrock Brines in a Coastal Mine

Song

Guo

et al. 2023

Water

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“…The heights of the sublayer, cutting layer, and filling body are 3, 4.5, and 3 m, respectively. Horizontal cut and fill mining is adopted for some stopes [ 34 ]. At present, the mining level of the study area is nearly 1000 m. The in situ stress field is composed of gravitational and tectonic stress fields.…”

Section: Geological Environment and Engineering Propertiesmentioning

confidence: 99%

Analysis of Acoustic Emission Characteristics and Failure Mode of Deep Surrounding Rock of Sanshandao Gold Mine

et al. 2022

IJERPH

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In mining engineering, crack distribution has a considerable influence on the mechanical behavior and stability of the surrounding rock mass. Using the granite of the Sanshandao gold mine as experimental samples, the deformation and failure of fractured rock were analyzed based on a rock uniaxial compression test with acoustic emission monitoring. We analyzed the characteristics of different stages of rock sample deformation, and evaluated the failure mode of seven types of rock samples. The results show that the cracks had a considerable impact on rock sample strength and mechanical behavior, and the strength of intact rock was the highest, while that of the sample with parallel double cracks was the lowest. The acoustic emission parameters, AF, RA, and lg(AF/RA), have different change trends in different stages of rock deformation and failure. Based on these change trends, the failure modes of rock samples with different crack distributions were identified. Additionally, for the rock samples with seven types of crack distribution, a sudden or progressive failure caused by the b-value curves was observed. The research findings provide a database for deep surrounding rock stability in the study area and provide suggestions for failure prediction.

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“…The hydrochemical characteristics are ignored, so a large amount of hydrogeochemical characteristic information may be lost, resulting in unclear classification. Multivariate statistical methods mainly include discriminant analysis, cluster analysis, and principal component analysis [20,21]. The discriminant analysis methods include the sequential discriminant method, the secondary discriminant method, the stepwise discriminant method [22], the distance discriminant method [3], FDA [15,22], Bayesian discriminant analysis [3], and the random forest method [23].…”

Section: Introductionmentioning

confidence: 99%

Source Discrimination of Mine Gushing Water Using Self-Organizing Feature Maps: A Case Study in Ningtiaota Coal Mine, Shaanxi, China

Zhao

Zeng

et al. 2022

Sustainability

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Currently, there is a contradiction between coal mining and protection of water resources, meaning that there is a need for an effective method for discriminating the source of mine gushing water. Ningtiaota Coal Mine is a typical and representative main coal mine in the Shennan mining area. Taking this coal mine as an example, the self-organizing feature map (SOM) approach was applied to source discrimination of mine gushing water. Fisher discriminant analysis, water temperature, and traditional hydrogeochemical discrimination methods, such as Piper and Gibbs diagrams, were also employed as auxiliary indicators to verify and analyze the results of the SOM approach. The results from the three methods showed that the source of all the gushing water samples was surface water. This study represents the innovative use of an SOM in source discrimination for the first time. This approach has the advantages of high precision, high efficiency, good visualization, and less human interference. It can quantify sources while also comprehensively considering their hydrogeochemical characteristics, and it is especially suitable for case studies with large sample sizes. This research provides a more satisfactory solution for water inrush traceability, water disaster prevention and control, ecological protection, coal mine safety, and policy intervention.

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Identification of Mine Water Sources Based on the Spatial and Chemical Characteristics of Bedrock Brines: A Case Study of the Xinli Gold Mine

Cited by 16 publications

References 39 publications

Interpreting Mine Water Sources and Determining Mixing Ratios Based on the Spatial and Chemical Characteristics of Bedrock Brines in a Coastal Mine

Interpreting Mine Water Sources and Determining Mixing Ratios Based on the Spatial and Chemical Characteristics of Bedrock Brines in a Coastal Mine

Analysis of Acoustic Emission Characteristics and Failure Mode of Deep Surrounding Rock of Sanshandao Gold Mine

Source Discrimination of Mine Gushing Water Using Self-Organizing Feature Maps: A Case Study in Ningtiaota Coal Mine, Shaanxi, China

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