scholarly journals Application of Clustering and Stepwise Discriminant Analysis Based on Hydrochemical Characteristics in Determining the Source of Mine Water Inrush

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Weitao Liu ◽  
Jie Yu ◽  
Jianjun Shen ◽  
Qiushuang Zheng ◽  
Mengke Han ◽  
...  
Keyword(s):  
Water Samples ◽  
Mine Water ◽  
Water Inrush ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Water Source ◽  
Stepwise Discriminant Analysis ◽  
Limestone Aquifer ◽  
Ordovician Limestone ◽  
Taiyuan Formation

In order to explore the law of groundwater evolution, the water source connection between faults and aquifers and the main sources of mine water inrush in the deep mining area of Yangcheng Coal Mine in Jining City, 40 groups of hydrochemical samples were collected and analyzed by Piper Diagram and Durov Diagram. The results showed that the fluidity of groundwater developing to the deep became weaker, the value of total dissolved solids (TDS) became larger. So, the roof and floor of coal seam were more similar in water quality types due to the conduction of faults. Using principal component analysis (PCA) to the raw data, two principal components were extracted, and the principal component scores were used as clustering variables for hierarchical cluster analysis (HCA), 5 groups of abnormal water samples were eliminated and 3 clustering groups M1, M2 and M3 were obtained from the other water samples on the tree diagram. The results showed that the combination of HCA and hydrochemical analysis was more effective in screening water samples, and the 3 clustering groups could be qualified samples to represent 3 major aquifers (Taiyuan Formation limestone aquifer, Shanxi Formation sandstone aquifer and Ordovician limestone aquifer). Finally, taking M1, M2 and M3 as grouping variables, the discriminant functions f 1 , f 2 and f 3 of the 3 aquifers were obtained, the results of stepwise discrimination analysis (SDA) showed that the discrimination model established by using 25 groups of standard water samples could discriminate the known water samples with the correct rate of 96%, 10 groups of unknown water samples collected at the fault are identified as Taiyuan Formation limestone water samples, which was consistent with the classification results of HCA, proving that the water inrush of fault DF53 was from Taiyuan Formation limestone aquifer, while the fault had little influence on Ordovician limestone aquifer.

scholarly journals Piper-PCA-Fisher Recognition Model of Water Inrush Source: A Case Study of the Jiaozuo Mining Area

Geofluids ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Pinghua Huang ◽  
Xinyi Wang
Keyword(s):  
Water Sample ◽  
Water Samples ◽  
Mine Water ◽  
Water Inrush ◽  
Water Source ◽  
Mining Area ◽  
Information Quantity ◽  
Recognition Model ◽  
Piper Trilinear Diagram ◽  
Discrimination Rate

Source discrimination of mine water plays an important role in guiding mine water prevention in mine water management. To accurately determine water inrush source from a mine in the Jiaozuo mining area, a Piper trilinear diagram based on hydrochemical experimental data of stratified underground water in the area was utilized to determine typical water samples. Additionally, principal component analysis (PCA) was used for dimensionality reduction of conventional hydrochemical variables, after which mutually independent variables were extracted. The Piper-PCA-Fisher water inrush source recognition model was established by combining the Piper trilinear diagram and Fisher discrimination theory. Screened typical samples were used to conduct back-discriminate verification of the model. Results showed that 28 typical water samples in different aquifers were determined through the Piper trilinear diagram as a water sample set for training. Before PCA was carried out, the first five factors covered 98.92% of the information quantity of the original data and could effectively represent the data information of the original samples. During the one-by-one rediscrimination process of 28 groups of training samples using the Piper-PCA-Fisher water inrush source model, 100% correct discrimination rate was achieved. During the prediction and discrimination process of 13 samples, one water sample was misdiscriminated; hence, the correct prediscrimination rate was 92.3%. Compared with the traditional Fisher water source recognition model, the Piper-PCA-Fisher water source recognition model established in this study had higher accuracy in both rediscrimination and prediscrimination processes. Thus it had a strong ability to discriminate water inrush sources.

scholarly journals Source Identification and Quantification of Seepage Water in a Coastal Mine, in China

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1862
Author(s):  
Xueliang Duan ◽  
Fengshan Ma ◽  
Jie Guo ◽  
Haijun Zhao ◽  
Hongyu Gu ◽  
...  
Keyword(s):  
Mine Water ◽  
Water Inrush ◽  
Principal Component ◽  
Seepage Water ◽  
Mixing Ratios ◽  
Flow Channels ◽  
Geological Conditions ◽  
Secondary Fractures ◽  
End Members ◽  
Supply Pattern

The Sanshandao gold mine, which is the largest coastal mine in China, is under threat from seawater intrusion and water inrush. The objective of this study is to determine the water end-members (seawater, freshwater, and brine) of the seepage water in the mine and quantify the proportion of end-members. Non-conservative ions and ion exchange were identified by using hydrogeochemical analysis. Then, the principal component analysis (PCA) was used to identify the end-members of mine water. Three end-members were identified, so a ternary mixture model was applied to compute the mixing ratios. The potential water flow channels and the prevailing supply patterns were inferred by combining the results of mixing ratios with the tectonic and engineering geological conditions. The results indicate that the proportion of seawater in mine water is about 57%, the freshwater is about 16% and the brine is about 27% for the entire mine area, the prevailing supply pattern of seawater was lateral recharge, the water samples which were located in −510 m sublevel or in the northeast of prospecting line 2260 had high proportions of seawater, the freshwater supplied the groundwater mainly through the secondary fractures developed area in a vertical recharge and the influence depth was about −500 m, and F3 was the largest tensile-shear fault in the study area and it was both a watercourse for seawater and fresh water.

scholarly journals Application of Partial Least Squares-Discriminate Analysis Model Based on Water Chemical Compositions in Identifying Water Inrush Sources from Multiple Aquifers in Mines

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Yaoshan Bi ◽  
Jiwen Wu ◽  
Xiaorong Zhai ◽  
Shuhao Shen ◽  
Libin Tang ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Water Samples ◽  
Mine Water ◽  
Water Inrush ◽  
Chemical Compositions ◽  
Discriminate Analysis ◽  
Discriminant Model ◽  
Water Chemical Composition ◽  
Multiple Aquifers ◽  
Different Water Sources

Mine water inrush seriously threatens the safety of coal mine production. Quick and accurate identification of mine water inrush sources is of great significance to preventing mine water hazards. This paper combined partial least squares-discriminate analysis (PLS-DA) with inrush water chemical composition to identify the source of water inrush from multiple aquifers in mines. The Renlou Coal Mine in the Linhuan mining area was selected for this study, and seven conventional water chemical compositions from 54 water samples in three aquifers were collected and tested, of which 45 water samples were used to establish the PLS-DA discriminant model, and nine were used to test the prediction effect. To improve model accuracy and predictive ability, hierarchical clustering analysis method was used to eliminate seven unqualified water samples to reduce the errors caused by improper data. PCA and PLS-DA methods were used to analyze and process the remaining water sample data, and on the basis of PCA analysis, the remaining 38 water samples were used to establish the PLS-DA discriminant model. The model was validated using permutation and external prediction tests. The research shows the following results: (1) Both PCA and PLS-DA methods can distinguish water samples from three different water sources, but the classification effect of PLS-DA was better than PCA because it can strengthen the difference of water chemical composition between different water sources. (2) The correct discrimination rate of the PLS-DA discriminant model was as high as 100%, and permutation tests showed that the model was not overfit. External validation found that the model had good stability and discrimination. (3) HCO3- and total dissolved solids (TDS) were the most important differential marker compositions that affected the discrimination results based on Variable Importance for the Projection (VIP) scores. The discriminant model established in this study combined the advantages of principal component analysis and multiple regression analysis, providing a new method for accurately identifying the sources of water inrush in mines.

scholarly journals Application of Multivariate Statistical Analysis to Identify Water Sources in A Coastal Gold Mine, Shandong, China

2019 ◽  
Vol 11 (12) ◽  
pp. 3345 ◽  
Author(s):  
Guowei Liu ◽  
Fengshan Ma ◽  
Gang Liu ◽  
Haijun Zhao ◽  
Jie Guo ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Water Samples ◽  
Multivariate Statistical Analysis ◽  
Water Inrush ◽  
Principal Component ◽  
Component Analysis ◽  
Gold Mine ◽  
Mining Operations ◽  
Multivariate Statistical

Submarine mine water inrush has become a problem that must be urgently solved in coastal gold mining operations in Shandong, China. Research on water in subway systems introduced classifications for the types of mine groundwater and then established the functions used to identify each type of water sample. We analyzed 31 water samples from −375 m underground using multivariate statistical analysis methods. Cluster analysis combined with principle component analysis and factor analysis divided water samples into two types, with one type being near the F3 fault. Principal component analysis identified four principle components accounting for 91.79% of the total variation. These four principle components represented almost all the information about the water samples, which were then used as clustering variables. A Bayes model created by discriminant analysis demonstrated that water samples could also be divided into two types, which was consistent with the cluster analysis result. The type of water samples could be determined by placing Na+ and CHO3− concentrations of water samples into Bayes functions. The results demonstrated that F3, which is a regional fault and runs across the whole Xishan gold mine, may be the potential channel for water inrush, providing valuable information for predicting the possibility of water inrush and thus reducing the costs of the mining operation.

Microbes in Waste Water Samples from Mining Environments

2009 ◽  
Vol 71-73 ◽  
pp. 43-46
Author(s):  
Xue Hui Xie ◽  
Sheng Mu Xiao ◽  
Jian She Liu
Keyword(s):  
Microbial Communities ◽  
Water Samples ◽  
Mine Water ◽  
Principal Component ◽  
Rflp Analysis ◽  
Key Factors ◽  
Group Iii ◽  
Two Samples ◽  
Culture Independent ◽  
Biogeochemical Parameters

The composition of microbial communities in five acid mine water samples were studied, using culture-independent 16S rDNA based cloning and restriction fragment length polymorphism (RFLP) analysis. Phylogenetic analysis revealed that the bacteria in these five samples fell into 4 major groups: Proteobacteria, Nitrospira, Firmicutes and Bacteroidetes. Proteobacteria organisms such as A. ferrooxidans appeared in samples SX3, K1 and K2, but was scarce in samples SX1 and SX2; Nitrospira organisms Leptospirillum ferrooxidans, Leptospirillum ferriphilum and Leptospirillum group III, were prevalent in samples SX1, K1 and K2, but with fewer in samples SX2 and SX3. Archaea were only detected in samples K1 and K2 from the Tong shankou copper mine. Thermoplasma and Ferroplasma lineages were detected abundantly in these two samples. Meanwhile, the results of Principal Component Analysis (PCA) based on the percentages of OTUs and data of biogeochemical parameters, revealed that biogeochemical properties affected the diversity of microbial communities in mine water. The pH, temperature and different concentrations of elements such as S, Ni, Co and Cu seemed to be key factors resulting in the diverse distribution of microbes.

Groundwater flow simulation and its application in GaoSong ore field, China

2018 ◽  
Vol 10 (2) ◽  
pp. 276-284 ◽  
Author(s):  
Gang Chen ◽  
Shiguang Xu ◽  
Chunxue Liu ◽  
Lei Lu ◽  
Liang Guo
Keyword(s):  
Numerical Model ◽  
Flow Field ◽  
Groundwater Flow ◽  
Mine Water ◽  
Permeability Coefficient ◽  
Water Inrush ◽  
Seepage Field ◽  
Groundwater Seepage ◽  
Calculation Results ◽  
Groundwater Flow Field

Abstract Mine water inrush is one of the important factors threatening safe production in mines. The accurate understanding of the mine groundwater flow field can effectively reduce the hazards of mine water inrush. Numerical simulation is an important method to study the groundwater flow field. This paper numerically simulates the groundwater seepage field in the GaoSong ore field. In order to ensure the accuracy of the numerical model, the research team completed 3,724 field fissure measurements in the study area. The fracture measurement results were analyzed using the GEOFRAC method and the whole-area fracture network data were generated. On this basis, the rock mass permeability coefficient tensor of the aquifer in the study area was calculated. The tensor calculation results are used in the numerical model of groundwater flow. After calculation, the obtained numerical model can better represent the groundwater seepage field in the study area. In addition, we designed three different numerical models for calculation, mainly to explore the influence of the tensor assignment of permeability coefficient on the calculation results of water yield of the mine. The results showed that irrational fathom tensor assignment would cause a significant deviation in calculation results.

Quality evaluation of tugaksheeree samples by ATR-FTIR spectroscopy using multicomponent analysis

2020 ◽  
Vol 10 ◽  
Author(s):  
Nikunj D. Patel ◽  
Niranjan S. Kanaki
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Quality Evaluation ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Attenuated Total Reflectance ◽  
Chemometric Methods ◽  
Total Reflectance ◽  
Fingerprint Region ◽  
Ftir Technique

Background: Numerous Ayurvedic formulations contains tugaksheeree as key ingredient. Tugaksheereeis the starch gained from the rhizomes of two plants, Curcuma angustifoliaRoxb. (Zingiberaceae) and Marantaarundinacea (MA) Linn. (Marantaceae). Objective: The primary concerns in quality assessment of Tugaksheeree occur due to adulteration or substitution. Method: In current study, Fourier transform infrared (FTIR) technique with attenuated total reflectance (ATR) facility was used to evaluate tugaksheeree samples. Total 10 different samples were studied and transmittance mode was kept to record the spectra devoid of pellets of KBR. Further treatment was given with multi component tools by considering fingerprint region of the spectra. Multivariate analysis was performed by various chemometric methods. Result: Multi component methods like Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA)were used to discriminate the tugaksheeree samples using Minitab software. Conclusion: This method can be used as a tool to differentiate samples of tugaksheeree from its adulterants and substitutes.

Sign in / Sign up

Export Citation Format

Share Document