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黄金科学技术 ›› 2021, Vol. 29 ›› Issue (1): 99-107.doi: 10.11872/j.issn.1005-2518.2021.01.116

• 采选技术与矿山管理 • 上一篇    下一篇

基于幂律规则的滨海矿山巷道涌水混合比分析

段学良1,2,3(),马凤山1,2(),郭捷1,2,惠鑫4,顾鸿宇5,王善飞6   

  1. 1.中国科学院地质与地球物理研究所,中国科学院页岩气与地质工程重点实验室,北京 100029
    2.中国科学院地球科学研究院,北京 100029
    3.中国科学院大学,北京 100049
    4.北京市基础设施投资有限公司,北京 100101
    5.中国地质调查局成都地质调查中心,四川 成都 610081
    6.山东黄金矿业(莱州)有限公司三山岛金矿,山东 莱州 261442
  • 收稿日期:2020-06-30 修回日期:2020-07-20 出版日期:2021-02-28 发布日期:2021-03-22
  • 通讯作者: 马凤山 E-mail:13051876966@163.com;fsma@mail.iggcas.ac.cn
  • 作者简介:段学良(1994-),男,河北泊头人,博士研究生,从事矿山水文地质与工程地质研究工作。13051876966@163.com
  • 基金资助:
    国家自然科学基金重点项目“海底采矿对地质环境的胁迫影响与致灾机理”(41831293);国家自然科学基金青年项目“基于‘盲源分离’的矿山突水水源识别与混合比研究”(41907174);国家重点研发计划专题“黄渤海不同类型海岸带海水入侵发生机理研究”(2016YFC0402802-01)

Analysis of Mine Water Mixing Ratio in a Coastal Deposit Based on Power Law

Xueliang DUAN1,2,3(),Fengshan MA1,2(),Jie GUO1,2,Xin HUI4,Hongyu GU5,Shanfei WANG6   

  1. 1.Key Laboratory of Shale Gas and Geoengineering,Institute of Geology and Geophysics,Chinese Academy of Sciences,Beijing 100029,China
    2.Innovation Academy for Earth Science,Chinese Academy of Sciences,Beijing 100029,China
    3.University of Chinese Academy of Sciences,Beijing 100049,China
    4.Beijing Infrastructure Investment Co. ,Ltd. ,Beijing 100101,China
    5.Chengdu Center,China Geological Survey,Chengdu 610081,Sichuan,China
    6.Sanshandao Gold Mine,Shandong Gold Mining(Laizhou)Company Limited,Laizhou 261442,Shandong,China
  • Received:2020-06-30 Revised:2020-07-20 Online:2021-02-28 Published:2021-03-22
  • Contact: Fengshan MA E-mail:13051876966@163.com;fsma@mail.iggcas.ac.cn

摘要:

滨海采矿引起的突涌水灾害对矿山的安全生产构成了极大威胁,计算巷道涌水的混合比,进一步分析其演化规律对于突涌水事故的防治具有重要意义。将幂律规则应用于三山岛金矿巷道涌水混合比数据的统计分析中,根据2个已有的混合比研究结果,采用概率密度函数pS)对相邻2个监测周期的海水比例波动事件进行了拟合,拟合的相关系数分别达到0.92和0.93,说明海水比例波动值区间间隔的均值与概率密度的分布符合幂律分布。因此,采用幂律规则对研究区的涌水混合比进行统计分析是可信的。研究结果表明:不同方法得出的混合比,在幂律规则下反映出的规律是相同的;海水比例波动值大于48%的概率小于5%,因此将48%视为预警区间的临界值;F3断层附近的涌水点相对其他监测点海水比值波动较大,这是由于F3为导水断层且连通了海水,受采动的影响,F3断层周围的导水通道错综复杂且不稳定。

关键词: 滨海采矿, 突涌水灾害, 混合比, 演化规律, 统计分析, 幂律规则

Abstract:

The study area,Sanshandao gold mine,is the first coastal mine in China.It belongs to structural fissure water-filled mine,and the hydrogeological conditions are complicated.With the mining of the orebody,multiple water inrush accidents occurred in the shallow and deep parts of the mine,causing the partial roadway to be flooded,and even some sections were accompanied by sand erosion.Water inrush caused by coastal mining is an extreme threat to the safe production of the mine.Therefore,it is important to determine the mine water mixing ratios and analyze its evolutionary law for the prevention of water inrush accidents.The power-law rule is a general law shown in the occurrence of geological disasters in nature.It refers to the relationship between the frequency and the scale of disasters.The frequency of large-scale disasters is low.Conversely,disasters with a high frequency of occurrence are relatively small in scale.To determine the measure of the proportion of seawater,the power-law rule was applied to the statistical analysis of the mixing ratios of the mine water in this study area.Firstly,the results of two existing mixing ratio studies were statistically sorted out,and the probability density statistical results of seawater fluctuation events were obtained.Then,the probability density function pS) was used to fit the fluctuation events of the seawater ratio in two adjacent monitoring periods.Finally,by integrating the fitted curve,the early warning interval of seawater fluctuation value was obtained.The research results show that the correlation coefficients of the fitting reach 0.92 and 0.93,respectively.It indicates that the distribution of the interval mean value and probability density of seawater proportional fluctuation events conformed to the power-law distribution.Thus,it is credible to use the power-law rule to analyze the mixing ratio of the mine water in the study area.For the mixing ratios obtained by different methods,the law reflected under the power law rule is the same.The fluctuation values of the seawater ratio at most monitoring sites are not large,less than 30%.It shows that the power-law rule is not affected by the calculation method of mixing ratio.Because the selected analysis index is the seawater fluctuation value,that is,for the relative value of two monitoring periods,the errors caused by different methods are eliminated.The probability that the fluctuation value of seawater ratio is greater than 48% is less than 5%,so 48% is regarded as the critical value of the warning interval.When the seawater fluctuation value is greater than this value,it should be paid attention to,and combined with the water temperature,flow rate,and other indicators of the water site for further analysis.Water samples near the F3 fault have larger fluctuation values of seawater than that of other water samples because F3 connects the seawater,and due to mining,the water channels around F3 are complicated and unstable.

Key words: coastal mining, water inrush, mixing ratio, evolution law, statistical analysis, power law

中图分类号: 

  • P642

图1

研究区构造及涌水点位置分布图"

表1

涌水样混合比计算结果(Duan et al.,2019)"

样品编号海水卤水淡水样品编号海水卤水淡水样品编号海水卤水淡水
105-1a0.520.210.26375-7'f0.810.070.12555-5b0.530.390.08
105-1b0.640.080.28375-8a0.620.200.18555-5c0.610.280.11
150-1a0.160.290.54375-8b0.560.310.14555-6a0.470.420.11
150-1b0.300.190.51375-8'e0.800.140.05555-6b0.580.360.05
150-1c0.230.220.55375-8'f0.770.090.14555-7b0.830.050.12
150-1d0.350.290.35375-9e0.800.140.07600-1a0.450.430.12
195-1a0.570.160.28375-9f0.830.070.10600-1b0.540.340.12
195-1b0.620.150.24375-10f0.630.210.16600-1c0.610.300.09
195-1c0.520.190.29375-11f0.480.500.02600-1d0.560.240.20
195-1d0.520.200.29375-12f0.450.490.06600-1e0.450.410.14
240-1a0.620.280.10375-13f0.310.630.06600-2a0.500.390.11
240-2a0.700.180.12420-1a0.620.190.19600-2b0.630.310.06
240-3a0.340.580.08420-1c0.660.220.13600-2c0.600.320.08
240-4a0.720.160.12420-2a0.530.330.14600-2d0.600.200.20
240-5a0.600.100.31465-1a0.540.340.12600-3a0.380.540.08
285-1a0.350.420.24465-1b0.480.350.17600-3b0.500.440.07
285-1b0.580.270.15465-1c0.380.430.20600-3c0.560.370.07
285-1c0.570.290.15465-2a0.360.410.23600-4b0.620.300.07
285-1d0.380.350.28465-2c0.410.370.22600-4c0.560.390.05
285-2a0.610.170.21510-1a0.740.160.10600-5b0.450.460.09
285-2b0.750.110.15510-1b0.820.050.13600-5c0.530.350.12
285-2d0.450.330.22510-1c0.710.180.11600-5d0.350.460.19
285-3a0.000.540.46510-2a0.790.100.11600-6c0.670.220.10
285-3b0.000.500.50510-2b0.870.030.10600-6d0.120.620.26
285-3c0.000.510.49510-2c0.730.100.17600-6e0.790.110.11
285-3d0.000.580.42510-3a0.720.140.14600-7c0.610.310.07
320-7e0.110.870.03510-3b0.740.120.14600-8c0.560.390.05
320-8e0.240.760.00510-4a0.700.160.14600-8d0.570.270.16
320-9e0.390.610.00510-4b0.800.070.13600-9c0.580.370.05
330-1a0.550.310.14510-5a0.710.130.16600-9d0.550.370.08
330-1b0.540.330.13510-6a0.720.100.18600-10d0.710.110.18
330-2a0.590.170.24510-6b0.800.070.13600-11e0.000.810.19
375-1a0.670.210.12510-7a0.400.480.12600-12e0.310.540.15
375-1b0.680.170.15510-8c0.760.070.16600-13e0.610.280.10
375-1c0.640.210.15510-9d0.640.130.23600-14e0.690.170.13
375-1e0.750.140.11510-10d0.610.150.24600-15e0.820.070.11
375-2a0.700.200.11510-11f0.320.630.05600-16e0.780.070.15
375-3a0.780.100.13510-12f0.600.290.11600-17e0.760.070.17
375-3b0.780.080.14510-13f0.640.280.09600-17f0.530.250.22
375-3d0.590.190.22510-15f0.620.280.10600-18e0.810.040.14
375-4a0.750.110.14510-16f0.540.350.11600-18f0.710.110.18
375-4b0.710.120.17510-16kf0.710.210.08600-19e0.820.030.14
375-4c0.690.180.13555-1a0.720.110.18600-20f0.620.230.15
375-4d0.550.240.21555-1c0.710.150.15600-21f0.470.320.21
375-4e0.710.170.12555-1d0.220.470.31600-22f0.770.080.15
375-5a0.470.340.19555-2a0.720.120.16600-23f0.790.060.14
375-5b0.520.300.18555-3a0.670.140.19645-1f0.580.320.10
375-5c0.450.340.21555-3b0.670.170.16645-2f0.380.390.23
375-5d0.350.390.27555-3c0.420.390.19645-3f0.540.340.12
375-5e0.570.280.15555-3d0.400.350.25645-4f0.490.400.11
375-6a0.510.350.14555-4a0.670.180.14690-1f0.550.350.10
375-6b0.470.370.16555-4b0.800.090.11690-10f0.490.270.24
375-6'f0.780.080.13555-4c0.760.110.13690-2c0.590.380.03
375-7a0.490.330.18555-4d0.680.120.20690-2f0.630.260.10
375-7'e0.790.100.11555-5a0.680.180.14690-12f0.670.240.09

表2

研究1中海水波动事件的概率密度统计结果"

统计区间区间均值事件个数累计个数发生频率概率密度
[0,10)545450.6250.063
[10,20)1519640.2640.026
[20,30)255690.0690.007
[30,40)352710.0280.003
[40,50)451720.0140.001

表3

研究2中海水波动事件的概率密度统计结果"

统计区间区间均值事件个数累计个数发生频率概率密度
[0,10)546460.6130.061
[10,20)1522680.2930.029
[20,30)254720.0530.005
[40,50)451730.0130.001
[50,60)551740.0130.001
[60,70)651750.0130.001

图2

海水波动事件的概率密度—间隔均值分布曲线"

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