img

QQ群聊

img

官方微信

高级检索

黄金科学技术 ›› 2019, Vol. 27 ›› Issue (4): 539-547.doi: 10.11872/j.issn.1005-2518.2019.04.539

• 采选技术与矿山管理 • 上一篇    下一篇

滨海金矿涌水危险评价及涌水量混沌预测研究

李科明(),刘志祥(),兰明   

  1. 中南大学资源与安全工程学院,湖南 长沙 410083
  • 收稿日期:2019-01-11 修回日期:2019-03-26 出版日期:2019-08-31 发布日期:2019-08-19
  • 通讯作者: 刘志祥 E-mail:514924402@qq.com;liulzx@csu.edu.cn
  • 作者简介:李科明(1996-),男,湖南长沙人,硕士研究生,从事采矿与岩石力学研究工作。514924402@qq.com
  • 基金资助:
    国家自然科学基金项目“金属矿海底基岩开采裂隙分形演化与突水混沌孕育机制”(51674288)

Research on Water Inrush Risk Assessment and Water Inflow Chaotic Prediction of Coastal Gold Mine

Keming LI(),Zhixiang LIU(),Ming LAN   

  1. School of Resources and Safety Engineering,Central South University,Changsha 410083,Hunan,China
  • Received:2019-01-11 Revised:2019-03-26 Online:2019-08-31 Published:2019-08-19
  • Contact: Zhixiang LIU E-mail:514924402@qq.com;liulzx@csu.edu.cn

摘要:

针对海底金矿涌水危险评价过程中的不确定性及随机性问题,选择工程地质及水文地质中涉及的10个重要因素构建评价指标体系,建立起涌水危险评价的云模型。基于评价结果,对危险区域的涌水量时间序列进行相空间重构,通过G-P算法及自相关法获得了重构相空间参数;分析了涌水量变化的相点距离演变规律,建立了涌水安全预警机制。结合混沌相空间重构,建立了涌水量预测的RBF神经网络模型。研究表明:涌水危险性的云模型评价结果准确可靠;相空间重构揭示了系统的混沌特性,最邻近相点演化将涌水量的内在细微变化特征放大,为涌水安全预警机制的建立提供了依据;混沌RBF神经网络能够实现涌水量的短期精确预测,为井下安全开采提供了技术保障。

关键词: 滨海矿山, 云模型评价, 混沌, RBF神经网络, 相空间重构, 安全预警, 涌水量预测

Abstract:

Mine water inrush is one of a major problem in the process of mining,and it is also a key technical problem in seabed mining.At present,the study on the potential dangers of water gushing and inrush in the mining of submarine metal deposits in China is still in its infancy,and there is no mature experience to draw lessons from.Therefore,this paper has made relevant research on the risk assessment and safety early warning system of seawater intrusion in seabed metal deposits.In the first,in view of the uncertainty and randomness in the risk assessment process of submerged gold mine water,an evaluation index system is established by selecting 10 important factors in geological and hydrological conditions.The factors are RQD value,joint spacing,rock permeability coefficient,structural plane condition,in-situ stress,water inrush volume,seawater proportion,brine proportion,atmospheric precipitation ratio and absolute value of seepage temperature difference.In the second,based on the evaluation results,the phase space of the time series of water inflow in dangerous areas is reconstructed,and meanwhile,the parameters of reconstructed phase space are obtained by G-P algorithm and autocorrelation method.What’s more,the evolution law of phase distance of water inflow is also analyzed,and then the early warning mechanism of water inflow safety is established.Combining with the reconstruction of chaotic phase space,the RBF neural network model for forecasting water inflow is established.Finally,after 6 000 training iterations,the model meets the training requirements,which is used to compare the simulated value with the actual value.It is found that the predicted result of water inflow within 13 days is basically consistent with the actual value,with the maximum error of 1.48%.After more than 13 days,the prediction effect becomes worse and worse.Therefore,the chaotic neural network model can be used to make a more accurate prediction of water inflow in a short time.The final research shows that the evaluation results of cloud model for water inrush risk are accurate and reliable.On the one hand,the phase space reconstruction reveals the chaotic characteristics of the system,and the evolution of the nearest phase points enlarges the intrinsic subtle variation characteristics of water inrush volume,which provides a basis for the establishment of early warning mechanism for water inrush safety.On the other hand,the chaotic RBF neural network can achieve short-term accurate prediction of water inrush volume and provide technical guarantee for underground safe mining barrier.

Key words: coastal mine, cloud model assessment, chaos, RBF neural network, phase space reconstruction, security early warning, water inflow prediction

中图分类号: 

  • TD742

表1

涌水危险性评价指标分级标准"

危险等级 工程地质因素 水文地质因素
X 1/% X 2/cm X 3/(×10-7 m·s-1 X 4 X 5 X 6/(m3·h-1 X 7/% X 8/% X 9/% X 10/℃
Ⅰ级 <70 <20 >2.50 <10 <5 >1 200 >40 <25 <10 >15
Ⅱ级 70~80 20~30 1.75~2.50 10~15 5~10 800~1 200 25~40 25~35 10~25 10~15
Ⅲ级 80~90 30~40 1.00~1.75 15~20 10~15 400~800 10~25 55~85 25~40 5~10
Ⅳ级 >90 >40 <1.00 >20 >15 <400 <10 >85 >40 <10

图1

RQD值涌水危险等级的云模型"

表2

各中段评价指标实测值"

中段水平/m X 1 X 2 X 3 X 4 X 5 X 6 X 7 X 8 X 9 X 10
-105 83.4 39.0 2.18 15 16.0 580.0 41.0 50.7 8.3 0.62
-135 90.1 26.9 1.80 10 15.0 69.4 16.0 79.4 4.5 1.93
-165 88.6 34.8 0.88 18 14.5 947.1 15.2 68.0 16.8 4.25
-200 79.8 32.4 2.44 12 14.5 1962.3 13.3 74.4 12.2 7.47
-240 74.5 31.8 1.88 20 14.0 78.0 15.8 50.6 33.6 8.29
-280 91.6 30.6 2.21 20 11.0 155.3 2.7 93.8 3.5 7.70
-320 89.8 28.1 2.00 22 11.0 64.6 14.8 54.4 30.8 6.33
-360 82.0 32.6 2.07 20 11.0 166.8 46.4 18.8 34.8 10.03
-400 89.5 34.1 1.83 17 11.0 320.6 5.1 79.2 15.7 13.83

表3

评价指标权重值"

评价方法 评价指标权重值
X 1 X 2 X 3 X 4 X 5 X 6 X 7 X 8 X 9 X 10
综合权重值 0.044 0.113 0.112 0.058 0.058 0.274 0.071 0.127 0.080 0.063
层次分析法 0.026 0.124 0.124 0.044 0.013 0.351 0.061 0.148 0.055 0.055
熵权法 0.092 0.085 0.083 0.095 0.173 0.078 0.095 0.073 0.144 0.081

表4

涌水危险性评价结果"

中段水平/m U 1 U 2 U 3 U 4 本文方法 未确知法 实际等级
-105 0.0952 0.2163 0.4749 0.1105 Ⅲ级 Ⅲ级 Ⅲ级
-135 0.1074 0.2104 0.2847 0.2975 Ⅳ级 Ⅲ级 Ⅳ级
-165 0.0136 0.3528 0.5200 0.1444 Ⅲ级 Ⅲ级 Ⅲ级
-200 0.0325 0.3944 0.2345 0.0604 Ⅱ级 Ⅰ级 Ⅱ级
-240 0.0047 0.1734 0.4415 0.2809 Ⅲ级 Ⅲ级 Ⅲ级
-280 0.0792 0.1825 0.2556 0.5152 Ⅳ级 Ⅳ级 Ⅳ级
-320 0.0010 0.2167 0.3792 0.2919 Ⅲ级 Ⅳ级 Ⅳ级
-360 0.1922 0.2039 0.3317 0.3433 Ⅳ级 Ⅳ级 Ⅳ级
-400 0.0235 0.2342 0.4601 0.3894 Ⅲ级 Ⅳ级 Ⅲ级

图2

涌水量数据关联维数"

图3

涌水量自相关函数曲线"

图4

涌水量相点距离演化曲线"

图5

涌水量预测值与实际值对比"

1 Bouw P C , Morton K L .Calculation of mine water inflow using interactively a groundwater model and an inflow model[J].International Journal of Mine Water,1987,6(4):31-50.
2 Holmøy K H , Nilsen B .Significance of geological parameters for predicting water inflow in hard rock tunnels[J].Rock Mechanics and Rock Engineering,2014,47(3):853-868.
3 Guo H , Adhikary D P , Craig M S .Simulation of mine water inflow and gas emission during longwall mining[J].Rock Mechanics and Rock Engineering,2009,42(1):25-51.
4 乔伟,李文平,赵成喜 .煤矿底板突水评价突水系数—单位涌水量法[J].岩石力学与工程学报,2009,28(12):2466-2474.
Qiao Wei , Li Wenping , Zhao Chengxi .Water inrush coefficient-unit inflow method for water inrush evaluation of coal mine floor[J].Chinese Journal of Rock Mechanics and Engineering,2009,28(12):2466-2474.
5 周宗青,李术才,李利平,等 .岩溶隧道突涌水危险性评价的属性识别模型及其工程应用[J].岩土力学,2013,34(3):818-826.
Zhou Zongqing , Li Shucai , Li Liping ,et al .Attribute recognition model of fatalness assessment of water inrush in karst tunnels and its application[J].Rock and Soil Mechanics,2013,34(3):818-826.
6 高松,张军进,孙珊珊,等 .三山岛北部海域金矿区水文地质特征分析[J].黄金科学技术,2016,24(1):11-16.
Gao Song , Zhang Junjin , Sun Shanshan ,et al .Hydrogeological characteristics of gold deposit in north sea area of Sanshandao[J].Gold Science and Technology,2016,24(1):11-16.
7 郑长成,刘春平 .三山岛金矿地下水水量模型及其应用[J].矿业研究与开发,1993(增2):89-94.
Zheng Changcheng , Liu Chunping .A ground water flow model in Sanshandao gold mine and its application[J].Research and Development of Mining,1993(Supp.2):89-94.
8 何顺斌,王宏伟,郭遥,等 .水化学损伤作用下三山岛金矿渗流场特征分析[J].金属矿山,2016,45(6):167-172.
He Shunbin , Wang Hongwei , Guo Yao ,et al .Analysis of characteristics of seepage field in Sanshandao Island gold mine under the action of water chemical damage[J].Metal Mine,2016,45(6):167-172.
9 杜敏铭,邓英尔,许模 .矿井涌水量预测方法综述[J].四川地质学报,2009,29(1):70-73.
Du Minming , Deng Ying’er , Xu Mo .Review of methodology for prediction of water yield of mine[J].Acta Geologica Sichuan,2009,29(1):70-73.
10 黄存捍,冯涛,王卫军,等 .基于分形和支持向量机矿井涌水量的预测[J].煤炭学报,2010,35(5):806-810.
Huang Cunhan , Feng Tao , Wang Weijun ,et al .Mine water inrush prediction based on fractal and support vector machines[J].Journal of China Coal Society,2010,35(5):806-810.
11 谢道文,施式亮 .基于云理论与加权马尔可夫模型的矿井涌水量预测[J].中南大学学报(自然科学版),2012,43(6):2308-2315.
Xie Daowen , Shi Shiliang .Mine water inrush prediction based on cloud model theory and Markov model[J].Journal of Central South University(Science and Technology),2012,43(6):2308-2315.
12 王迎超,靖洪文,张强,等 .基于正态云模型的深埋地下工程岩爆烈度分级预测研究[J].岩土力学,2015,36(4):1189-1194.
Wang Yingchao , Jing Hongwen , Zhang Qiang ,et al .A normal cloud model-based study of grading prediction of rockburst intensity in deep underground engineering[J].Rock and Soil Mechanics,2015,36(4):1189-1194.
13 周启刚,张晓媛,王兆林 .基于正态云模型的三峡库区土地利用生态风险评价[J].农业工程学报,2014,30(23):289-297.
Zhou Qigang , Zhang Xiaoyuan , Wang Zhaolin .Land use ecological risk evaluation in Three Gorges Reservoir area based on normal cloud model[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(23):289-297.
14 王新民,秦健春,张钦礼,等 .基于AHP-TOPSIS评判模型的姑山驻留矿采矿方法优选[J].中南大学学报(自然学科版),2013,44(3):1131-1137.
Wang Xinmin , Qin Jianchun , Zhang Qinli ,et al .Mining method optimization of Gu Mountain stay ore based on AHP-TOPSIS evaluation model[J].Journal of Central South University(Science and Technology),2013,44(3):1131-1137.
15 程启月 .评测指标权重确定的结构熵权法[J].系统工程理论与实践,2010,30(7):1225-1228.
Cheng Qiyue .Structure entropy weight method to confirm the weight of evaluating index[J].Systems Engineering—Theory and Practice,2010,30(7):1225-1228.
16 杨清泉,李威,陶晓杰 .三山岛海底金矿地质特征及矿床成因探讨[J].黄金科学技术,2010,18(3):5-8.
Yang Qingquan , Li Wei , Tao Xiaojie .Discussion on the geological characteristics and genesis of Sanshandao submarine gold mine[J].Gold Science and Technology,2010,18(3):5-8.
17 刘志祥,刘超,刘强,等 .海底开采岩层变形混沌时序重构与安全预警系统研究[J].岩土工程学报,2010,32(10):1530-1534.
Liu Zhixiang , Liu Chao , Liu Qiang ,et al .Chaotic time series reconstruction and security alarm system of rock mass deformation in undersea mining[J].Chinese Journal of Geotechnical Engineering,2010,32(10):1530-1534.
18 毕洪涛,马凤山,李克蓬,等 .山东三山岛金矿深部低温热水对采矿的影响[J].中国地质灾害与防治学报,2014,25(3):89-93.
Bi Hongtao , Ma Fengshan , Li Kepeng ,et al .Impact of low temperature geothermal water on mining of Sanshandao gold mine[J].The Chinese Journal of Geological Hazard and Control,2014,25(3):89-93.
19 简相超,郑君里 .混沌和神经网络相结合预测短波通信频率参数[J].清华大学学报(自然科学版),2001,41(1):16-19.
Jian Xiangchao , Zheng Junli .Prediction of frequency parameters in short wave radio communications based on chaos and neural networks[J].Journal of Tsinghua University(Science and Technology),2001,41(1):16-19.
20 王新迎,韩敏 .基于极端学习机的多变量混沌时间序列预测[J].物理学报,2012,61(8):97-105.
Wang Xinying , Han Min .Multivariate chaotic time series prediction based on extreme learning machine[J].Acta Physica Sinica,2012,61(8):97-105.
21 Albano A M , Muench J , Schwartz C ,et al .Singular-value decomposition and the Grassberger-Procaccia algorithm[J].Physical Review A,1988,38(6):3017-3026.
22 董春娇,邵春福,张辉,等 .基于G-P算法的快速路交通流参数相空间重构[J].吉林大学学报(工学版),2012,42(3):594-599.
Dong Chunjiao , Shao Chunfu , Zhang Hui ,et al .Phase space reconstruction of traffic flow parameters on expressway based on G-P algorithm[J].Journal of Jilin University(Engineering and Technology Edition),2012,42(3):594-599.
23 刘文博 .混沌时间序列分析与计算方法及应用研究[D].大连:大连理工大学,2008.
Liu Wenbo .Research on Computational Method of Chaotic Time Series and Its Application[D].Dalian:Dalian University of Technology,2008.
24 李鹤,杨周,张义民,等 .基于径向基神经网络预测的混沌时间序列嵌入维数估计方法[J].物理学报,2011,60(7):137-142.
Li He , Yang Zhou , Zhang Yimin ,et al .Methodology of estimating the embedding dimension in chaos time series based on the prediction performance of radial basis function neural networks[J].Acta Physica Sinica,2011,60(7):137-142.
25 张建龙,解建仓,韩宇平,等 .基于混沌径向基神经网络模型的洪水预测研究[J].西北农林科技大学学报(自然科学版),2009,37(8):221-227.
Zhang Jianlong , Xie Jiancang , Han Yuping ,et al .Flood forecasting research based on the chaotic RBF neural network model[J].Journal of Northwest A & F University(Natural Science Edition),2009,37(8):221-227.
26 徐东辉,李岳林,杨巍,等 .基于混沌RBF神经网络的汽油机进气流量预测研究[J].计算机工程与应用,2014,50(1):222-226.
Xu Donghui , Li Yuelin , Yang Wei ,et al .Gasoline engine intake flow forcast study of chaotic RBF neural network[J].Computer Engineering and Applications,2014,50(1):222-226.
[1] 曾俊晖,李夕兵. 基于混沌时间序列分析方法的矿山塌陷区范围预测[J]. 黄金科学技术, 2019, 27(2): 249-256.
[2] 刘港,马凤山,赵海军,郭捷,侯成录,李威. 三山岛金矿西山矿区主要透水中段裂隙分布规律与三维建模[J]. 黄金科学技术, 2019, 27(2): 199-206.
[3] 高松,张军进,孙珊珊,王有智. 三山岛北部海域金矿区水文地质特征分析[J]. 黄金科学技术, 2016, 24(1): 11-16.
[4] 李夕兵,朱玮,刘伟军,张德明. 基于主成分分析法与RBF神经网络的岩体可爆性研究[J]. 黄金科学技术, 2015, 23(6): 58-63.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 杨明荣, 牟长贤. 原子荧光法测定化探样品中砷和锑的不确定度评定[J]. J4, 2010, 18(3): 68 -71 .
[2] 李斌, 邹海洋, 杨牧, 杜高峰, 韦继康, 王天国. 马来西亚吉兰丹州Ulu Sokor金矿地质特征及找矿方向[J]. J4, 2010, 18(4): 17 -21 .
[3] 闫杰, 覃泽礼, 谢文兵, 蔡邦永. 青海南戈滩—乌龙滩地区多金属地质特征与找矿潜力[J]. J4, 2010, 18(4): 22 -26 .
[4] 任广智, 赵玉锁, 肖振, 卿敏, 魏峰, 缪振平. 河北峪耳崖金矿床矿体赋存规律及找矿预测[J]. J4, 2010, 18(4): 27 -32 .
[5] 姜琪, 王荣超. 甘肃枣子沟金矿床形成环境及矿床成因[J]. J4, 2010, 18(4): 37 -40 .
[6] 李洪杰, 戚静洁, 马树江. 胶西北地区金矿床构造控矿规律[J]. J4, 2010, 18(4): 41 -46 .
[7] 原冬成, 徐小凤. 山东曹家埠金矿床的成矿机理与找矿前景[J]. J4, 2010, 18(4): 47 -49 .
[8] 迟继松, 牌洪坤, 亓传铎, 王虎, 秦香伟, 李文玉. 伴有充填体矿石选矿方法的研究与应用[J]. J4, 2010, 18(4): 68 -70 .
[9] 刘远华, 杨贵才, 张轮, 齐金忠, 李文良. 西秦岭阳山超大型金矿床花岗岩岩石地球化学特征[J]. J4, 2010, 18(6): 1 -7 .
[10] 刘东海, 刘新会. 西秦岭寨上特大型金矿床黄铁矿特征及其含金性研究[J]. J4, 2010, 18(6): 8 -12 .