Coal Geology & Exploration
Abstract
It is difficult to accurately identify the sources of water inrush between sandstone-mudstone interbedded overburden in loess hilly and gully region, and these proportions of different water sources cannot be quantified. For this reason. Based on the systematic collection of surface water and groundwater samples from Zhujiamao Coal Mine in northern Shaanxi. This study revealed the hydrochemical evolution laws of water quality and the recharge relationship of various aquifer systems by mathematical statistics, Piper (trilinear) diagram, Gibbs diagram and the conventional hydrochemical characteristic method. Meanwhile, this study analyzed the evolution characteristics of stable isotopic composition in water, clarified the spatial and temporal variations of water quality from various sources under different environmental background. On this basis, this study constructed a calculation model for the mixing ratio of water inrush sources based on the T-spherical fuzzy power aggregation operators coupling with TOPSIS method, rough set theory, D-S evidence theory (DSET) and single-index unascertained measurement functions by using conventional and isotopic characteristic ratios as the indexes. In this study area result shows that,both of surface and groundwater are of the main hydrochemical type of Na-SO4·Cl, but their controlled factors vary greatly. Specifically, the hydrochemical compositions of surface water are jointly controlled by the weathering of silicate rock and evaporative crystallization, while the hydrochemical compositions of groundwater are mainly controlled by evaporative crystallization. In addition, the surface water subjected to strong evaporation recharges the groundwater to some extent. The T-TOPSIS-RST-DSET-SIUMF discrimination model for the mixing ratio of water inrush sources shows that over 50% of water inrush in Zhujiamao Coal Mine in northern Shaanxi comes from the roof sandstone water and the surface Shakonggou water, which accuracy is verified by three dimensional high-density resistivity method.
Keywords
sandstone-mudstone interbedded overburden, hydrochemical characteristics, isotopic signatures, spatialotemporal variations, mixing proportions, three dimensional high-density resistivity method
DOI
10.12363/issn.1001-1986.22.12.0943
Recommended Citation
SHI Longqing, QU Xingyue, HAN Jin,
et al.
(2023)
"Evaluation on spatiaotemporal variations of mine water quality and water source identification based on key dominant factors in loess hilly and gully region,"
Coal Geology & Exploration: Vol. 51:
Iss.
2, Article 15.
DOI: 10.12363/issn.1001-1986.22.12.0943
Available at:
https://cge.researchcommons.org/journal/vol51/iss2/15
Reference
[1] 张许良,张子戌,彭苏萍. 数量化理论在矿井突(涌)水水源判别中的应用[J]. 中国矿业大学学报,2003,32(3):251−254.
ZHANG Xuliang,ZHANG Zixu,PENG Suping. Application of the second theory of quantification in identifying gushing water sources of coal mines[J]. Journal of China University of Mining & Technology,2003,32(3):251−254.
[2] 余克林,杨永生,章臣平. 模糊综合评判法在判别矿井突水水源中的应用[J]. 金属矿山,2007(3):47−50.
YU Kelin,YANG Yongsheng,ZHANG Chenping. Application of fuzzy comprehensive evaluation method in identifying water source of water−rush in underground shaft[J]. Metal Mine,2007(3):47−50.
[3] 李垣志,牛国庆,张轩轩. 矿井突水水源判别的ESN正则化模型[J]. 煤田地质与勘探,2018,46(1):108−114.
LI Yuanzhi,NIU Guoqing,ZHANG Xuanxuan. ESN regularization model for discriminating mine water inrush source[J]. Coal Geology & Exploration,2018,46(1):108−114.
[4] 孙福勋,魏久传,万云鹏,等. 基于Fisher判别分析和质心距评价法的矿井水源判别[J]. 煤田地质与勘探,2017,45(1):80−84.
SUN Fuxun,WEI Jiuchuan,WAN Yunpeng,et al. Recognition method of mine water source based on Fisher’s discriminant analysis and centroid distance evaluation[J]. Coal Geology & Exploration,2017,45(1):80−84.
[5] 黄平华,陈建生. 焦作矿区地下水水化学特征及涌水水源判别的FDA模型[J]. 煤田地质与勘探,2011,39(2):42−46.
HUANG Pinghua,CHEN Jiansheng. The chemical features of ground water and FDA model used to distinguish source of water burst in Jiaozuo Mine Area[J]. Coal Geology & Exploration,2011,39(2):42−46.
[6] 苏玮,姜春露,查君珍,等. 基于客观组合权–改进集对分析模型的矿井突水水源识别[J]. 煤炭科学技术,2022,50(4):156−164.
SU Wei,JIANG Chunlu,ZHA Junzhen,et al. Identification of mine water inrush source based on objective combined weights–improved set pair analysis model[J]. Coal Science and Technology,2022,50(4):156−164.
[7] 黄敏,毛岸,路世昌,等. 矿井突水水源识别的主成分分析–混沌麻雀搜索–RF模型[J/OL]. 安全与环境学报,2022:1–12 [2022-11-23]. DOI:10.13637/j.issn.1009–6094.2022.0623.
HUANG Min,MAO An,LU Shichang,et al. Identification of mine water inrush source based on PCA−CSSA−RF model[J/OL]. Journal of Safety and Environment,2022:1–12 [2022-11-23]. DOI:10.13637/j.issn.1009–6094.2022.0623.
[8] 侯恩科,姚星,车晓阳,等. 基于KPCA–APSO–ELM的矿井涌水水源识别[J]. 安全与环境学报,2022,22(1):64−71.
HOU Enke,YAO Xing,CHE Xiaoyang,et al. Identification method of mine water inrush sources based on KPCA−APSO−ELM[J]. Journal of Safety and Environment,2022,22(1):64−71.
[9] 秋兴国,刘杰,李娜,等. 改进贝叶斯判别法的矿井水源识别模型[J]. 西安科技大学学报,2022,42(2):237−244.
QIU Xingguo,LIU Jie,LI Na,et al. Identification model of mine water source based on improved Bayesian discrimination[J]. Journal of Xi’an University of Science and Technology,2022,42(2):237−244.
[10] 张好,姚多喜,鲁海峰,等. 主成分分析与Bayes判别法在突水水源判别中的应用[J]. 煤田地质与勘探,2017,45(5):87−93.
ZHANG Hao,YAO Duoxi,LU Haifeng,et al. Application of principal component analysis and Bayes discrimination approach in water source identification[J]. Coal Geology & Exploration,2017,45(5):87−93.
[11] 张春雷,钱家忠,赵卫东,等. Bayes方法在矿井突水水源判别中的应用[J]. 煤田地质与勘探,2010,38(4):34−37.
ZHANG Chunlei,QIAN Jiazhong,ZHAO Weidong,et al. The application of Bayesian approach to discrimination of mine water–inrush source[J]. Coal Geology & Exploration,2010,38(4):34−37.
[12] 朱赛君,姜春露,毕波,等. 基于组合权–改进灰色关联度理论的矿井突水水源识别[J]. 煤炭科学技术,2022,50(4):165−172.
ZHU Saijun,JIANG Chunlu,BI Bo,et al. Identification of mine water inrush source based on combination weight–theory of improved grey relational degree[J]. Coal Science and Technology,2022,50(4):165−172.
[13] 纪卓辰,丁湘,侯恩科,等. 纳林河二号煤矿涌水水源判别的PCA–Logistic方法[J]. 煤田地质与勘探,2020,48(5):97−105.
JI Zhuochen,DING Xiang,HOU Enke,et al. The PCA–Logistic method for identification of water burst in Nalinhe No.2 Coal Mine[J]. Coal Geology & Exploration,2020,48(5):97−105.
[14] 周航,廖昕,陈仕阔,等. 基于组合赋权和未确知测度的深埋隧道岩爆危险性评价:以川藏交通廊道桑珠岭隧道为例[J]. 地球科学,2022,47(6):2130−2148.
ZHOU Hang,LIAO Xin,CHEN Shikuo,et al. Rockburst risk assessment of deep lying tunnels based on combination weight and unascertained measure theory:A case study of Sangzhuling tunnel on Sichuan–Tibet Traffic Corridor[J]. Earth Science,2022,47(6):2130−2148.
[15] WU Qiang,ZHAO Dekang,WANG Yang,et al. Method for assessing coal−floor water−inrush risk based on the variable–weight model and unascertained measure theory[J]. Hydrogeology Journal,2017,25(7):2089−2103.
[16] QU Xingyue,SHI Longqing,HAN Jin. Spatial evaluation of groundwater quality based on toxicological indexes and their effects on ecology and human health[J]. Journal of Cleaner Production,2022,377:134255.
[17] ATUL K S,NITIN K S,ANOOP K S,et al. T–SAW methodology for parametric evaluation of surface integrity aspects in AlMg3 (AA5754) alloy:Comparison with T–TOPSIS methodology[J]. Measurement,2019,132:309−323.
[18] SRIVASTAVA S R,MEENA Y K,SINGH G. Forecasting on Covid–19 infection waves using a rough set filter driven moving average models[J]. Applied Soft Computing,2022,131:109750.
[19] SUN Lin,SI Shanshan,DING Weiping,et al. TFSFB:Two–stage feature selection via fusing fuzzy multi–neighborhood rough set with binary whale optimization for imbalanced data[J]. Information Fusion,2023,95:91−108.
[20] ZHOU Lingge,CUI Huizi,MI Xiangjun,et al. A novel conflict management considering the optimal discounting weights using the BWM method in Dempster–Shafer evidence theory[J]. Information Sciences,2022,612:536−552.
[21] 张生春. 陕西省现代大气降水氢氧同位素组成特征研究[J]. 陕西地质,1989,7(2):57−66.
ZHANG Shengchun. Characteristics of hydrogen−oxygen isotope compositions of contemporarily atmospheric sedimentation in Shaanxi Province[J]. Geology of Shaanxi,1989,7(2):57−66.
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