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《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

卷:

第42卷

期数:

2020年第01期

页码:

132-142

栏目:

水资源与环境

出版日期:

2020-01-15

文章信息/Info

Title:

End-members Identification of Mine Water Inrush Based on Hydrochemical Data: Principal Component Analysis and Residual Analysis

文章编号:

1672-6561(2020)01-0132-11

作者:

顾鸿宇¹; 马凤山²; 王东辉¹; 李胜伟^1*; 刘港³; 向元英⁴; 郭子奇¹

(1. 中国地质调查局成都地质调查中心,四川 成都 610081; 2. 中国科学院地质与地球物理研究所 页岩气与地质工程重点实验室,北京 100029; 3. 中国地质调查局西安地质调查中心, 陕西 西安 710054; 4. 德阳市环境监测中心站,四川 德阳 618000)

Author(s):

GU Hong-yu¹;  MA Feng-shan²;  WANG Dong-hui¹;  LI Sheng-wei^1*;  LIU Gang³;  XIANG Yuan-ying⁴;  GUO Zi-qi¹

(1. Chengdu Center, China Geological Survey, Chengdu 610081, Sichuan, China; 2. Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China; 3. Xi'an Center of Geological Survey, China Geological Survey, Xi'an 710054, Shaanxi, China; 4. Deyang Environmental Monitoring Center Station, Deyang 618000, Sichuan, China)

关键词:

矿山涌水;  水化学;  主成分分析;  残差分析;  水源数量;  水源类型;  离子浓度

Keywords:

mine water inrush;  hydrochemistry;  principal component analysis;  residual analysis;  number of water source;  type of water source;  ion concentration

分类号:

P641.4; TD741

DOI:

10.19814/j.jese.2019.07014

文献标志码:

A

摘要:

涌水是矿山安全生产的重要威胁之一,掌握涌水的水源是预防这类灾害的重要依据。基于多期次的水化学监测数据,提出了利用主成分分析(PCA)和残差分析来识别涌水的水源数量和水源类型。主成分分析能够充分提取高维数据的结构信息,消除变量冗余。残差分析利用主成分分析结果,提取不同的主成分个数重构离子浓度,得出重构离子浓度和原始离子浓度之间的残差,利用残差的结构性信息来判断可能的涌水水源数量和水源类型。结果表明:随保留主成分个数的增加,重构离子浓度的精度越高,离子残差的结构性得到消除且表现出随机性。在实例应用中,通过利用5个主成分重构离子浓度消除了所有主要离子残差结构性,保证了信息的最大程度提取,因此,确定研究矿山涌水水源数量为5个; 同时,利用每个主成分对残差结构性的影响程度及离子相关性分析,确定了这5个水源类型为海水、第四系孔隙水、富钙基岩水、富镁基岩水和淡水。

Abstract:

Water inrush is a threat to mine safety, and the knowledge of water sources(end-members)identification is the foundation of prediction for potential disasters. Based on the multiple-stage hydrochemical data, a method using principal component analysis(PCA)and residual analysis was developed to identify the numbers and types of water sources. PCA can eliminate the redundancy and extract the structural information of original variables. Residual analysis is based on the results of PCA by extracting different numbers of principal components to reconstruct the ion concentrations, calculating the residual between reconstructed and measured ion concentrations, then, using the structural degree to identify the numbers and types of water sources. The results show that the accuracy is improved, and the structure of residual is eliminated with the increasing number of principal components. Using the 5 principal components can eliminate most of the structural information of main ions, and extract all the useful information in the case; meanwhile, 5 types of water sources, including seawater, Quaternary pore water, Ca-rich water, Mg-rich water and freshwater, are identified based on the different effects of each principal component to the residual and the relationships among ion concentrations.

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备注/Memo

备注/Memo:

收稿日期:2019-07-11; 修回日期:2019-08-23; 网络首发日期:2019-11-08投稿网址:http:∥jese.chd.edu.cn/
基金项目:国家自然科学基金项目(41831293,41907174); 中国地质调查局地质调查项目(DD20189210)
作者简介:顾鸿宇(1991-),男,四川成都人,工程师,工学博士,E-mail:xy0909040129@126.com。
*通讯作者:李胜伟(1975-),女,四川成都人,高级工程师,E-mail:401245116@qq.com。

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更新日期/Last Update: 2020-01-07