Vulnerability assessment of water bursting from Ordovician limestone into coal mines of China

Zeng, Yifan; Wu, Qiang; Liu, Shouqiang; Zhai, Yanliang; Zhang, Wei; Liu, Yuanzhang

doi:10.1007/s12665-016-6239-4

Cited by 35 publications

(12 citation statements)

References 11 publications

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“…Afterward, we assigned the whitenization weights f 1i (k), f 2i (k), and f 3i (k) to each X i (k) series according to the formula (equations (11)- (13) or Figures 1-3, respectively) given below:…”

Section: Whitenization Weight Functionmentioning

confidence: 99%

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Risk Assessments of Water Inrush from Coal Seam Floor during Deep Mining Using a Data Fusion Approach Based on Grey System Theory

et al. 2020

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With the increase in depth of coal mining, the hydrogeological complexity largely increases and water inrush accidents happen more frequently. For the safety of coal mining, horizontal directional drilling and grouting techniques have been implemented to detect and plug the fractures and conduits that deliver high-pressure groundwater to coal mine. Taking the grouting engineering performed at Xingdong coal mine at 980 m below sea level as an example, we collected the data of grouting quantity, the loss of drilling fluid, gamma value, water temperature, average water absorption, distance between grouting loss points, water pressure on coal seam floor, and aquifuge thickness at 90 boreholes in the mine to conduct grey relational analysis, first. The analysis showed that the grouting quantity was highly correlated with all other factors. Subsequently, grey system evaluation was used to evaluate the risk of water inrush from the coal seam floor. The results of risk analysis illustrated that three water inrushes from Ordovician limestone occurred in mining face 2127, 2125, and 2222 in the study area were all located in the area with a risk score higher than 65. Through grouting, the identified cracks were effectively blocked and waterproof layers beneath the coal seam floors were constructed to reduce the threat of water inrush. By comparing the risk assessment results with three water inrush cases before grouting operation, we found that water inrush areas were consistent with the area of higher risk.

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Section: Whitenization Weight Functionmentioning

confidence: 99%

“…e facts indicated that the method is effective for prevention and control of water inrush in the mine. Similarily, a GISbased analytic hierarchy process was proposed by Zeng et al [13]. Eight control factors were determined to evaluate the water inrush of No.…”

Section: Introductionmentioning

confidence: 99%

Risk Assessments of Water Inrush from Coal Seam Floor during Deep Mining Using a Data Fusion Approach Based on Grey System Theory

et al. 2020

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“…The GAN model is a semi-supervised learning process. Suppose there are m input train samples X = x (1) , x (2) , .,…”

Section: Discrimination Model Of Mine Water Inrush Source Based On Ganmentioning

confidence: 99%

“…, where m is the number of input samples, and x (i) is defined in n + 1 dimensional Euclidean space. The corresponding labels are Y = y (1) , y (2) , . .…”

Section: Discrimination Model Of Mine Water Inrush Source Based On Ganmentioning

confidence: 99%

“…Many research works have verified that different aquifers and different water sources have distinctive hydro chemical and physical characteristics. 1,[2][3][4] Based on these theories, the available methods to discriminate water source including trace elements, 5 representative ions, 6 water temperature level, 7 and water flow conditions, in mines could be acknowledged in detail, that could be used to accurately forecast mine water inrush disasters.…”

Section: Introductionmentioning

confidence: 99%

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Generative adversarial nets in laser-induced fluorescence spectrum image recognition of mine water inrush

Yang

et al. 2019

International Journal of Distributed Sensor Networks

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Water inrush occurred in mines, threatens the safety of working miners which triggers severe accidents in China. To make full use of existing distinctive hydro chemical and physical characteristics of different aquifers and different water sources, this article proposes a new water source discrimination method using laser-induced fluorescence technology and generative adversarial nets. The fluorescence spectrum from the water sample is stimulated by 405-nm lasers and improved by recursive mean filtering method to alleviate interference and auto-correlation to enhance the feature difference. Based on generative adversarial nets framework and improved spectra features, the article proposes a novel water source discrimination-generative adversarial nets model in mines to solve the problem of data limitation and improve the discrimination ability. The results show that the proposed method is an effective method to distinguish water inrush types. It provides a new idea to discriminate the sources of water inrush in mines timely and accurately.

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Using random forest for the risk assessment of coal-floor water inrush in Panjiayao Coal Mine, northern China

Zhao

Cui

et al. 2018

Hydrogeol J

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Vulnerability assessment of water bursting from Ordovician limestone into coal mines of China

Cited by 35 publications

References 11 publications

Risk Assessments of Water Inrush from Coal Seam Floor during Deep Mining Using a Data Fusion Approach Based on Grey System Theory

Risk Assessments of Water Inrush from Coal Seam Floor during Deep Mining Using a Data Fusion Approach Based on Grey System Theory

Generative adversarial nets in laser-induced fluorescence spectrum image recognition of mine water inrush

Using random forest for the risk assessment of coal-floor water inrush in Panjiayao Coal Mine, northern China

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