Inrush Source Research Articles

Hydrochemical analysis and discrimination of mine water source of the Jiaojia gold mine area, China

To study the hydrochemical characteristics of main aquifers in Jiaojia gold mine area and their changing regularities, exploring the main sources of mine water, 244 water samples from the main aquifers and mine water were collected. The principal components were extracted by factor analysis and shows that with the increase of depth, the concentration of Na+ and Cl− increased significantly, the water–rock interaction becomes stronger, the water quality develops towards salinization, the total water quality, hydrodynamic conditions, and the connection between groundwater and surface water becomes worse. We extracted the main influential discriminant indexes using principal component analysis (PCA), that is, pH, MH4+, NO3−, NO2−, total hardness, TDS, Fe3+, SO42−, Cl− and F−. We weighted the extracted discriminant indexes using entropy weight method (EWM), that is in turn, 0.0002, 0.1883, 0.1272, 0.2061, 0.0680, 0.0613, 0.1461, 0.0573, 0.0844, and 0.0613. 55 water samples from each aquifer in the last 5 years were analyzed by hierarchical cluster analysis (HCA), and the relational degree between the main aquifers and mine water was judged by the distance between them; result shows that the main source of mine water is from the hanging wall of fault aquifer, followed by the footwall of fault aquifer. The mine water has little connection with bedrock weathered fractured aquifer, and almost no connection with Quaternary porous aquifer. The PCA–EWM–HCA model established in this paper provides a theoretical basis for identification of mine water inrush source and protection of underground water resources.

Groundwater Hydrogeochemical Processes and the Connectivity of Multilayer Aquifers in a Coal Mine with Karst Collapse Columns

Understanding groundwater hydrogeochemical processes and the connectivity of multilayer aquifers in a coal mine with karst collapse columns (KCCs) is very important for mine safety and groundwater resource management. In this study, 52 groundwater samples from the main aquifers in the Xieqiao coal mine (Anhui Province, China) were analyzed using hydrochemical, multivariate statistical methods, and stable isotope analyses. In the Permian aquifer, the main hydrogeochemical processes are halite and silicate dissolution and sulfate reduction, followed by cation exchange, while in the Carboniferous and Ordovician aquifers, the main hydrogeochemical processes are carbonate, gypsum, and halite dissolution, followed by cation exchange. This causes the hydrochemical characteristics of these two aquifers to be similar. The Permian aquifer contains less SO42−, more HCO3− concentration, and a concentration ratio of SO42− to HCO3− less than 0.25 due to sulfate reduction, which allows the Permian aquifer to be distinguished easily from the other two. Both hierarchical cluster and stable isotope analysis show that water samples from both aquifers appear to have been mixed via the KCCs, which may serve as a potential channel for groundwater inrush into the coal mine. These results may assist in accurately predicting potential water inrush sources in this coal mine as well as in other mines.

A Scientometric Analysis of Trends in Coal Mine Water Inrush Prevention and Control for the Period 2000–2019

Water inrush hazards in coal mines pose a great risk to the safety of miners and coal production. We studied journal articles on coal mine water inrush prevention and control from 2000 to March 2019 using data from the Web of Science Core Collection V.5.32 database. We used CiteSpace V.5.3.R11 software to analyse the literature, including countries, institutions, authors, disciplines, journals, hot topics, and research frontiers. A total of 1612 published papers on coal mine water inrush prevention and control research were identified. China and the USA had the highest numbers of publications in this field. The China University of Mining and Technology was the most influential research institution. The journal with the largest number of relevant publications was the International Journal of Coal Geology. Hidden water disasters, quantitative evaluation of aquicludes, and rapid identification of water inrush sources are still unresolved problems. Improvement of electromagnetic methods, intelligent mine water monitoring, and three-dimensional hydrogeological systems were the leading research trends in this field.

Bayesian model based on Markov chain Monte Carlo for identifying mine water sources in Submarine Gold Mining

Based on the Bayesian theory, the Bayesian water source discrimination model and model prediction method were established by using the adaptive differential evolution Metropolis algorithm of Markov chain Monte Carlo simulation as the parameter posterior distribution sampling calculation method. Taking the mine water samples of Sanshandao Gold Mine as an example, according to the chemical composition analysis of the water samples from different monitoring points, six indexes of Mg2+, Na+ + K+, Ca2+, SO42−, Cl− and HCO3− were selected as the discrimination factors. According to the water characteristics of each aquifer and the actual needs of discrimination, the water inrush sources in the mining area were divided into four categories: sea water is class I, Quaternary pore water is class II, bedrock fissure water is class III, and fresh water is class IV. Taking 40 typical water inrush samples as training samples, using SPSS statistics and Bayes method based on Markov Chain Monte Carlo to build Bayes discriminant analysis model, the posterior distribution of algorithm estimation based on water sample information was obtained, and the analysis method of mine water damage source was obtained. The results showed that the Bayes discriminant model based on Markov Chain Monte Carlo has high accuracy, good applicability and discriminant ability, and has certain guiding significance for the prevention and control of water damage and the related field work.

矿井突水水源的多元统计分析判别

矿井突水水源的多元统计分析判别

CEEMD: A New Method to Identify Mine Water Inrush Based on the Signal Processing and Laser-Induced Fluorescence

The rapid and accurate identification of water source types in mine water inrush has been achieved by combining laser-induced fluorescence technology (LIF) with artificial intelligence algorithms. However, these algorithms solely rely on data and image processing analysis to identify different kinds of water samples. To address this issue, we analyzed the fluorescence spectrum and the types of mine water inrush sources from the signal point of view. Firstly, a LIF water inrush spectral analysis system was built to collect spectral data and exhibit fluorescence spectra. Different methods of spectral signal decomposition and reconstruction were compared. The complementary ensemble empirical mode decomposition (CEEMD) algorithm with a better signal evaluation index was selected to preprocess raw spectral signals. Then, the multi-class support vector machine of the cuckoo search optimization (CS-MSVM) model was implemented to the reconstructed spectral signals in different stages. The classification accuracy of the reconstructed signals in the fifth stage was 100%. Compared with raw spectra, other signal processing methods, and other different classifiers, the proposed method has the highest classification accuracy. Finally, the reliability of the algorithm was validated by using the LIF spectral signals of different edible oils and the classification accuracy was 100%. The experimental results show that the CEEMD signal processing method combined with LIF spectroscopy is effective for the accurate identification of mine water inrush source, and it also provides a theoretical basis for the spectral analysis technology that can be used for the identification of other substances.

Modeling Rock Fracture Propagation and Water Inrush Mechanisms in Underground Coal Mine

Water inrush in underground mines is a major safety threat for mining personnel, and it can also cause major damage to mining equipment and result in severe production losses. Water inrush can be attributed to the coalescence of rock fractures and the formation of water channel in rock mass due to the interaction of fractures, hydraulic flow, and stress field. Hence, predicting the fracturing process is the key for investigating the water inrush mechanisms for safe mining. A new coupling method is designed in FRACOD to investigate the mechanisms of water inrush disaster (known as “Luotuoshan accident”) which occurred in China in 2010 in which 32 people died. In order to investigate the evolution processes and mechanisms of water inrush accident in Luotuoshan coal mine, this study applies the recently developed fracture-hydraulic (F-H) flow coupling function to FRACOD and focuses on the rock fracturing processes in a karst collapse column which is a geologically altered zone linking several rock strata vertically formed by the long-term dissolution of the flowing groundwater. The numerical simulation of water inrush is conducted based on the actual geological conditions of Luotuoshan mining area, and various materials with actual geological characteristics were used to simulate the rocks surrounding the coal seam. The influences of several key factors, such as in situ stresses, fractures on the formation, and development of water inrush channels, are investigated. The results indicate that the water inrush source is the Ordovician limestone aquifer, which is connected by the karst collapse column to No. 16 coal seam; the fracturing zone that led to a water inrush occurs in front of the roadway excavation face where new fractures coalesced with the main fractured zone in the karst collapse column.

Hydrogeochemical evolution of multilayer aquifers in a massive coalfield

Groundwater is an important drinking water resource worldwide. However, mining could influence the natural groundwater geochemistry, reducing the correct discrimination of inrush sources and affecting mine safety. To accurately identify potential inrush sources in the future, hydrogeochemical processes and their evolution should be investigated using multiple analytical methods. In the study, based on hydrochemical analysis and multivariate statistical methods, the major ion chemistry of groundwater in multi-aquifer groundwater systems was analyzed in the Huainan coalfield (Anhui Province, China). The results showed that the hydrogeochemical processes in this area consisted of the three subprocesses: dissolution, desulfidation, and cation exchange. For the Cenozoic bottom aquifer and the Ordovician aquifer, the major hydrogeochemical processes are the dissolution of salt rock, silicate, carbonate, and sulfate, followed by cation exchange; for the Permian and Carboniferous aquifers, the main hydrogeochemical processes are desulfidation and cation exchange, followed by dissolution. From the Permian aquifer to the Carboniferous aquifer and then to the Ordovician aquifer, the concentration of SO42− increased, while the concentration of HCO3− decreased due to enhanced sulfate dissolution and weakened desulfurization. However, long-term mining activities changed the groundwater seepage field and the hydrogeochemical field, causing hydrogeochemical evolutionary processes with obvious zoning. The eastern discharge zone showed the strongest dissolution and the weakest desulfidation and cation exchange; from the eastern discharge zone to the central runoff zone, dissolution weakened, while desulfidation and cation exchange were enhanced; and in the western recharge zone, desulfidation and cation exchange were the strongest, while dissolution was the weakest.

Hydrogeochemical Characteristics and Groundwater Inrush Source Identification for a Multi‐aquifer System in a Coal Mine

Correct identification of water inrush sources is particularly important to prevent and control mine water disasters. Hydrochemical analysis, Fisher discriminant analysis, and geothermal verification analysis were used to identify and verify the water sources of the multi‐aquifer groundwater system in Gubei coal mine, Anhui Province, North China. Results show that hydrochemical water types of the Cenozoic top aquifer included HCO3–Na+K–Ca, HCO3–Na+K–Mg and HCO3–Na+K, and this aquifer was easily distinguishable from other aquifers because of its low concentration of Na++K+ and Cl–. The Cenozoic middle and bottom aquifers, the Permian fissure aquifer, and the Taiyuan and Ordovician limestone aquifers were mainly characterized by the Cl–Na+K and SO4–Cl‐Na+K or HCO3–Cl–Na+K water types, and their hydrogeochemistries were similar. Therefore, water sources could not be identified via hydrochemical analysis. Fisher model was established based on the hydrogeochemical characteristics, and its discrimination rate was 89.19%. Fisher discrimination results were improved by combining them with the geothermal analysis results, and this combination increased the identification rate to 97.3 % and reasonably explained the reasons behind two water samples misjudgments. The methods described herein are also applicable to other mines with similar geological and hydrogeological conditions in North China.

Combining the Fisher Feature Extraction and Support Vector Machine Methods to Identify the Water Inrush Source: A Case Study of the Wuhai Mining Area

Discriminating the source of water inrush accurately and efficiently is necessary for water control in the coal mining industry. We combined the Fisher feature extraction and support vector machine (SVM) methods and applied this new model to the Wuhai mining area. The method extracts features from the raw data and integrated SVM, and synthetically considers the influence of geographical factors. Cross-analysis was tested 100 times, which arbitrarily selected 12 samples for the prediction and discrimination process. The results indicate that this new combined model of linear dimension reduction and non-linear dimension elevation was more accurate and efficient in discriminating water inrush sources than the traditional SVM model. Moreover, by reducing the penalty term of SVM model, we analyzed the correlation among the aquifers. We concluded that aquifers II and IV correlated strongly with each other, and that aquifer III was poorly connected with the other aquifers.

The multiple logistic regression recognition model for mine water inrush source based on cluster analysis

Mine water inrush is one of the major geological hazards that threaten safe production in coal mines. The accurate identification of mine water inrush sources plays a vital role in mine water disaster control, and it is the key to preventing mine water inrush incidents. Ninety-three water samples were extracted from the three types of aquifers in the Qinan coal mine. The cluster analysis method was then used to analyze 82 of the original water samples, and the other 11 water samples that did not meet the requirements were removed. Then, the remaining 82 water samples were regarded as training samples, and the principal component analysis was completed. Taking the scores of the principal components as the independent variable and the types of water inrush sources as the dependent variable, the multiple logistic regression recognition model was established. Meanwhile, this recognition model was used to recognize the types of mine water inrush sources and verify the recognition accuracy for the 82 training samples. The comprehensive recognition accuracy reached 86.6%, which is much higher than the traditional recognition methods of water inrush sources. Based on cluster analysis, the multiple logistic regression recognition model fully considers the ion content measurement errors and the complex relationships between the internal ions, and this recognition model is more reasonable and improves the accuracy of water inrush source recognition. This paper provides a new method for recognizing the problem of water inrush sources, which also provides an effective basis for mine water inrush prevention and control.

Research on Piper-PCA-Bayes-LOOCV discrimination model of water inrush source in mines

The occurrence of mine water inrush constantly threatens the safety production of coal mines and causes serious financial losses in China. Existing water inrush source identification methods do not consider the mixing effect of aquifers and the complexity of discrimination indexes. To identify water inrush rapidly and accurately, an identification model that combines water chemistry and multivariate statistical methods is proposed. The Piper trilinear diagram was used to screen 48 water samples taken from a water inrush aquifer in a mining area. Forty-two typical water samples that represent the water inrush aquifers were obtained. They were selected as training samples with discrimination indexes (Ca2+, Mg2+, Na++K+, HCO3−, SO42−, Cl−). Principal component analysis (PCA) was used to extract three principal indexes. Then, with these indexes taken as factors of Bayesian discrimination, we established a model for determining the source of water bursting in Pingdingshan Mine. Finally, the prejudgment classification stability of the constructed model was evaluated by leave-one-out cross-validation (LOOCV), which reports 95.2% overall classification accuracy. The constructed model was used to obtain a prognosis of 10 samples collected from Pingdingshan Mine, and it reported one misjudgment on one sample. In addition to comparing the prediction results with fuzzy comprehensive evaluation and gray relational degree model, results indicate that the constructed Piper-PCA-Bayes-LOOCV discrimination model of the water inrush source in mines can increase the recognition accuracy effectively, thus guaranteeing the safety production of mines.

Analysis of water conductivity and water gushing source of collapse column in changcun mine

Based on the water conductivity of Lu’an coal mine collapse column and the analysis of the causes of water inrush of Lu’an coal mine collapse column, the water inrush source of collapse column in Changcun Mine was comprehensively analyzed by stable isotope tracing method. It was found that the groundwater runoff conditions and geological structure condition of collapse column seriously affected the water inrush of collapse column. It is determined that the water inrush source of N2-7 collapse column in Changcun coal mine is the same as the Upper Sandstone water source, that is, the infiltration supply of atmospheric precipitation, but has no direct connection with the aquifer of Ordovician limestone., which provides a relatively reliable method for the water inrush source identification of collapse column.

Selection of characteristic wavelengths using SPA for laser induced fluorescence spectroscopy of mine water inrush.

In the process of prevention and control of water inrush disaster, it is of great significance to identify the type of water inrush source for coal mine safety production accurately and quickly. The application of laser induced fluorescence (LIF) technology to identify the water inrush in coal mine broke the shortage of the traditional hydrochemical method, which could realize the accurate and rapid identification of water inrush types. Firstly, in order to avoid the influence of random variations of spectral data, four kinds of common pretreatment methods were analyzed and compared, and the moving average smoothing method was chosen to preprocess the original fluorescence spectral data. Then, for the purpose of selecting the appropriate sample division method to improve the predictive performance of the model, four common sample division methods were compared, and the sample set partitioning based on joint x-y distance (SPXY) method was used to divide the samples into training set and test set. Further, the 10 characteristic wavelengths were selected by successive projections algorithm (SPA) to reduce the amount of data. Finally, the selected data was taken as input, the sigmoid function was selected as the activation function of extreme learning machine (ELM), and the number of hidden layer neurons was set to 34, which realized the construction of water source identification model. The prediction accuracy of ELM model for the training set and test set were 99.0% and 94.0%, respectively. In addition, the water samples collected at different time were mixed in the same way to form the independent verification set, and the prediction accuracy of the ELM water source identification model for independent verification set was 91.5%. The results shown that it was feasible to select the characteristic wavelengths of fluorescence spectra by using the SPA. The data of 10 characteristic wavelengths could fully represent the effective information of whole band spectrum. And it also provided a theoretical basis for the development of a special online identification instrument for mine water inrush.

Mechanism of mine water inrush from overlying porous aquifer in Quaternary: a case study in Xinhe Coal Mine of Shandong Province, China

Mine water inrush from coal seam roof is one of the serious disasters that threaten safe production of coal mines. Identification of water inrush source and water inrush pathway is a key task for preventing and controlling such mine water hazards. A water inrush accident at approximately 1316 m3/h occurred at no. 3301 working face of Xinhe Coal Mine in Shandong Province, China. Multiple lines of evidence including characteristics of mine water inflow rate, dynamic monitoring data of water levels in different aquifers, geochemical fingerprinting, and drill hole core examination suggest that the water inrush source originated from the overlying Lower Quaternary porous aquifer with calcite cementation. Structural analysis and numerical simulation with FLAC3D indicate that a low-angle fault, DF49, and mining-induced fractures provided the pathway for the water inrush. Mining activities made the fault hydraulically conductive and connected to the Quaternary aquifer. The numerical simulation demonstrated that the water-conducting fracture zone in the coal seam roof extended to the fault. Groundwater gushed into the mining area from the Quaternary aquifer via the combined pathway of the activated fault and mining-induced fractures. Presence of the fault in the overlying formations played a critical role in occurrence of the water inrush. Results from this case study can be of reference to all coal mines with faults or other geological discontinuities present in the overlying formation. These discontinuities may significantly extend the height of the mining-induced water-conducting fracture zone in the roof and thus increase water inrush risks.

基于水化学特征判别谢桥煤矿突水水源

基于水化学特征判别谢桥煤矿突水水源

Water inrush sources monitoring and identification based on mine IoT

SummaryInternet of things (IoT) is applied to water inrush source monitoring network for overcoming complexity and uncertainty of coal mines in monitoring. This study starts from the idea of sensory mines and the architecture of IoT, and describes key techniques in IoT‐based mine water inrush source sensing. In the sensing layer, networked and intelligent sensors are employed for constructing the distributed monitoring system; in the network service layer, data mining, 3D geoscience simulation system and cloud computing are combined for establishing the cloud service platform so as to comprehensively analyze water inrush sources; in the application layer, a novel maximum/minimum‐margin‐based hierarchical‐SVM model for the identification of water‐inrush sources is proposed. Test results demonstrate that, owing to the application of mine IoT, both information collection efficiency and processing capability of water‐inrush source monitoring can be effectively enhanced, and the established water source identification system exhibits low false alarm rate and mis‐detection ratio, thereby significantly improving the prediction reliability.

Application of the comprehensive identification model in analyzing the source of water inrush

Disasters caused by water inrush during production affect many coal mines in China. To rapidly and accurately prevent further water inrush, a comprehensive identification model combining hydrochemistry analysis, water source detection, and water channel exploration is proposed. The Schukalev classification method (SCM) is used to distinguish the concentration distribution of the main ion between the water inrush source and other aquifers, and hierarchical cluster analysis (HCA) is adopted to classify the water samples in the hydrochemistry analysis stage. Water channel exploration and water source detection are combined to test and verify the conclusion in the third stage. The comprehensive discrimination model is applied to the water inrush of the Buliangou coal mine. Hydrochemistry analysis shows that the percentages of the main ions (Na+, Ca2+, Mg2+, HCO3−, and Cl−) are greater than 30% (excluding SO42−), which is in agreement with the case of a sandstone aquifer. In the second stage, water source detection based on the transient electromagnetic method (TEM) and drilling technique indicates that not much water is present in the anomaly area around the water inrush location. The limited water recharge contradicts the phenomenon of water inrush. Water channel exploration is carried out, and an area with massive fissures that have water storage capacity is revealed. The conclusion is that the water conserved in the fissures flows out over a short time without the occurrence of stable recharge. The comprehensive discrimination model has complementary advantages and improves the water inrush prediction efficiency.

Risk Evaluation of Bed-Separation Water Inrush: A Case Study in the Yangliu Coal Mine, China

Water that accumulates in the void space where overburden strata separate can be an inrush source. We established an engineering geology model of bed-separation water inrush, and chose five factors (hard rock thickness, coal seam thickness, aquifuge thickness, aquifer thickness and hydrostatic head) as risk factors. Additionally, based on an analysis of hydrogeological and engineering geological conditions at two areas in the Yangliu coal mine, the comprehensive weight values of these factors were found to be 0.32, 0.24, 0.18, 0.15, and 0.11, respectively, using fuzzy analytic hierarchy process and entropy method. Finally, the data were normalized, and a zoning map for risk evaluation was created using the space superposition function of GIS. This method was further used to evaluate the risk at the Haizi coal mine, and the results were in accordance with an inrush disaster that occurred at the 745 working face at that mine. This validated the model’s practical applications. This provides mines with strategies to prevent and control bed-separation water inrush.

Application of hydrochemistry for inrush water source identification in coal mine: approach based on statistical analysis

Application of hydrochemistry for inrush water source identification in coal mine: approach based on statistical analysis