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

Zhao, Di; Zeng, Yifan; Wu, Qiang; Du, Xin; Gao, Shi; Mei, Aoshuang; Zhao, Haonan; Zhang, Zhihao; Zhang, Xiaohui

doi:10.3390/su14116551

Cited by 6 publications

(6 citation statements)

References 43 publications

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“…According to the conventional ion concentration, the hydrochemical types of groundwater can be classified. With the introduction of Piper diagram [7,8] and Durov diagram [9], the hydrochemical types and ion distribution of groundwater can be more intuitively seen. Then, the source of groundwater and the hydraulic connection between different aquifers can be preliminarily judged.…”

Section: Introductionmentioning

confidence: 99%

Study on the Hydrochemical Characteristics and Evolution Law of Taiyuan Formation Limestone Water under the Influence of Grouting with Fly Ash Cement: A Case Study in Gubei Coal Mine of Huainan, China

Xiao,

2024

Water

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The hydrogeological conditions of Huainan Coalfield are complex. The Taiyuan formation limestone water (Taihui water) in this area is a direct threat to the water source of the 1# coal mining floor. In order to prevent and control water disasters, Gubei Coal Mine adopted ground high-pressure grouting with fly ash cement to block the hydraulic connection between the Taiyuan formation limestone aquifer and the Ordovician limestone aquifer. However, the injected slurry will destroy the original hydrochemical balance of Taihui water and change its hydrochemical characteristics. Taking the influence area of the 2# karst collapse column in the Beiyi 1# coal mining area of Gubei Coal Mine as an example, a total of 25 Taihui water samples were collected. The hydrochemical characteristics and evolution law of Taihui water before and after grouting are studied via the multivariate statistical method. The research methods include constant index statistics, Piper diagram, correlation analysis, ion combination ratio, and saturation index analysis. The results show that after grouting, the concentrations of Na+ + K+, Ca2+, Mg2+, and Cl− in Taihui water decrease, while the concentrations of SO42− and HCO3− increase. The average values of PH and TDS become larger. The hydrochemical types of Taihui water are more concentrated, mainly HCO3-Na and Cl-Na. The correlations between conventional indicators decrease. According to the analysis of ion combination ratio, dissolution, cation exchange, and pyrite oxidation mainly occur in Taihui water, and these effects are enhanced after grouting. The saturation index results show that after grouting, the saturation index of dolomite, calcite, and gypsum is significantly reduced, and the saturation index of rock salt is slightly increased. The conclusion of this study is that the hydrochemical characteristics of Taihui water are greatly affected by fly ash cement. Moreover, because fly ash cement contains a lower calcium oxide content than ordinary Portland cement, the effect of fly ash cement on the ion concentration of Taihui water and the resulting hydrogeochemical effect are significantly different. Therefore, in the treatment of mine water disasters, the hydrogeochemical evolution law affected by fly ash cement grouting should be identified.

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Section: Introductionmentioning

confidence: 99%

Study on the Hydrochemical Characteristics and Evolution Law of Taiyuan Formation Limestone Water under the Influence of Grouting with Fly Ash Cement: A Case Study in Gubei Coal Mine of Huainan, China

Xiao,

2024

Water

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“…By combining data from water chemical characteristics and physical characteristics, such as temperature and water level, researchers have been able to determine the water sources in mine water inrush disasters [21][22][23][24][25][26][27]. In recent years, more inroads have been made for increasing the speed and accuracy when identifying these sources by using a variety of mathematical model calculation methods based on the characteristics of water chemical physics, as well as other factors [28][29][30][31][32]. Based on hydrochemical data and self-organizing feature maps (SOM), Zhao et al [28] analyzed the water inrush source of Ningtiao mine.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, more inroads have been made for increasing the speed and accuracy when identifying these sources by using a variety of mathematical model calculation methods based on the characteristics of water chemical physics, as well as other factors [28][29][30][31][32]. Based on hydrochemical data and self-organizing feature maps (SOM), Zhao et al [28] analyzed the water inrush source of Ningtiao mine. Fisher discriminant analysis, water temperature, and traditional hydrogeochemical discrimination methods were employed as auxiliary indicators to verify and analyze the results of the SOM, and the source of all water samples was confirmed to be surface water.…”

Section: Introductionmentioning

confidence: 99%

Identification of Mine Mixed Water Inrush Source Based on Genetic Algorithm and XGBoost Algorithm: A Case Study of Huangyuchuan Mine

Dong

Liu

et al. 2022

Water

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Mine water inrush disaster seriously threatens the production of coal mine. Rapid and accurate identification of mine water inrush sources is a key premise for mine water disaster prevention. The conventional research on the identification of water inrush source has focused on a single source, and the identification of mixed water samples from multi-source aquifers in deep coal mining environment is not yet fully explored. In this study, absorption spectrum technology was introduced into the identification of water inrush sources. The absorption spectra of the water samples with different mixing ratios were prepared using the ultraviolet and visible spectrophotometry (UV–Vis) spectrophotometer. In addition, spectral data preprocessing such as scattering correction, baseline correction, smoothing and denoising, and data enhancement were conducted to reduce the influence of experimental error, environment, radiation, molecular interaction, and other factors on the spectral data. Furthermore, a genetic algorithm (GA) was used to improve the seven parameters of the extreme gradient boosting (XGBoost) algorithm, such as learning rate, base model selection, tree parameters, regularization parameters, and iteration times. The deep-learning classifier of mine mixed water sources based on GA-XGBoost was established and used to identify 66 groups of mixed water sources in the Huangyuchuan Mine. The simulation results show that spectral preprocessing and normalization enhancement effectively improved the accuracy of the discriminant model. After 100 cross-validations, the average recognition accuracy of the GA-XGBoost model was 94%, and the results were accurate and reliable. This study provides a new direction and method for the identification of water inrush sources, particularly for mixed water inrush sources. It may also serve as a technical reference for decision-makers to formulate effective coal mine water inrush prevention and control programs and for mine water disaster prevention in similar coalfields in North China.

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“…Managing mine flooding usually starts with the source of the sudden water. Therefore, rapid and accurate identification of mine water sources is significant for mine water damage prevention and control 2 . The hydrochemical analysis method is based on the variation of geochemical ion concentrations in aquifers and incorporates mathematical methods to create discriminant functions for different aquifers 3 .…”

mentioning

confidence: 99%

“…However, when the OPEN 1 China Pingmei Shenma Holding Group Co., LTD, Pingdingshan 467000, Henan, China. 2 China Pingmei Shenma Group, State Key Laboratory of Coking Coal Resources Green Exploitation, Pingdingshan 467000, Henan, China. 3 Institute of Resources & Environment, Henan Polytechnic University, Jiaozuo 454000, Henan, China.…”

mentioning

confidence: 99%

Identification of mine water source based on TPE-LightGBM

Wang,

Zhang,

et al. 2024

Sci Rep

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Mine water inrush is a serious threat to mine safety production. It is very important to identify water inrush source types quickly to prevent and control water damage. In this study, the aqueous chemical components Na+ + K+, Ca2+, Mg2+, Cl−, SO42− and HCO3− of different aquifers in Pingdingshan coalfield were selected as the characteristic values, and the Surface water, Quaternary pore water, Carboniferous limestone karst water, Permian sandstone water, and Cambrian limestone karst water were used as the labels. An intelligent water source discrimination model is proposed by combining data mining, classification models, and reinforcement learning. As outlier data in the samples may interfere with the model recognition ability, the data distribution range was analyzed using box plots, and 20 groups of abnormal samples were excluded. The processed water chemistry data were divided into 80% learning samples and 20% test samples, and the learning samples were fed into a light gradient boosting machine (LightGBM) for training. The tree-structured parson estimator (TPE) obtains the optimal values of the main parameters of LightGBM in a very short time. Substituting the hyperparameters back into the model yields a 13.9% improvement in the accuracy of the model, proving the effectiveness of the TPE algorithm. To further validate the performance of the model, TPE-LightGBM is compared and analyzed with a Random Search-Multi Layer Perceptron Machine (RS-MLP) and Genetic Algorithm-Extreme Gradient Boosting Tree (GA-SVM). The accuracy of TPE-LightGBM, RS-MLP, and GA-SVM is 0.931, 0.759, 0.724 in that order, and the generalization error RMSE is 0.415, 1.05, and 1.313 in that order. The results show that TPE-LightGBM is more advantageous in water source identification and is more resistant to overfitting. By calculating and comparing the information gain of each variable, the contribution of Ca2+ is the highest, so it is necessary to pay attention to the change in Ca2+ concentration. TPE-LightGBM’s high accuracy and generalization ability have a good prospect for the identification of sudden water source types.

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

Cited by 6 publications

References 43 publications

Study on the Hydrochemical Characteristics and Evolution Law of Taiyuan Formation Limestone Water under the Influence of Grouting with Fly Ash Cement: A Case Study in Gubei Coal Mine of Huainan, China

Study on the Hydrochemical Characteristics and Evolution Law of Taiyuan Formation Limestone Water under the Influence of Grouting with Fly Ash Cement: A Case Study in Gubei Coal Mine of Huainan, China

Identification of Mine Mixed Water Inrush Source Based on Genetic Algorithm and XGBoost Algorithm: A Case Study of Huangyuchuan Mine

Identification of mine water source based on TPE-LightGBM

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