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Gold Science and Technology ›› 2019, Vol. 27 ›› Issue (2): 207-215.doi: 10.11872/j.issn.1005-2518.2019.02.207

• Mining Technology and Mine Management • Previous Articles    

Application of Multivariate Statistical Analysis to Identify Water Source in Coast Mine Area:As Example of Sanshandao Gold Mine

Guowei LIU1,2,3(),Fengshan MA1,2(),Jie GUO1,2,Yunlong DU4,Chenglu HOU4,Wei LI4   

  1. 1. Key Laboratory of Shale Gas and Geoengineering,Institute of Geology and Geophysics,Chinese Academy of Sciences,Beijing 100029,China
    2. Institutions of Earth Science,Chinese Academy of Sciences,Beijing 100029,China
    3. University of Chinese Academy of Sciences,Beijing 100049,China
    4. Sanshandao Gold Mine,Shandong Gold Mining(Laizhou)Co. ,Ltd. ,Laizhou 261442,Shandong,China
  • Received:2018-07-31 Revised:2018-11-01 Online:2019-04-30 Published:2019-04-30
  • Contact: Fengshan MA E-mail:l1014893489@163.com;fsma@mail.iggcas.ac.cn

Abstract:

Xishan gold mine is subordinate to the Sanshandao gold mine and located in the coastal area of Laizhou Bay,Laizhou City,Shandong Province.In terms of geotectonic,it is located in the western part of the second up-warping zone of the Neocathaysian structural system,which is also Sanshandao-Cangshang fracture of the eastern side of Yishu deep facture.Xishan gold mine has been exploited in under the Bohai sea.Submarine mine water inrush has become an urgent problem to be solved in mine mining.The research on subway water can classify the types of mine groundwater and then predict the possibility of water inrush.Taking groundwater system of Sanshandao gold mine for example,hydrochemical data of 31 water samples was chosen to study with multivariate statistical analysis methods.By using factor analysis,it can reduce the spatial dimension of many variables with correlation relationship,and then identify principle factors which represent over ninety percent information of hydrochemical data.Hierarchical clustering analysis(HCA)uses these principle factors as clustering variables.HCA combined with actual groundwater quality divided the studied groundwater into 2 classic groups,then established and validated Fisher identification model.Through FA and HCA,the groundwater of -375 m subway were divided into two types which all have a specific discriminant function could determine which type of water is.The results represent that the water samples were divided into two typical M1 and M2 by factor analysis combined with principle component analysis.Among the 31 water samples,three of them were discriminated wrong,and the correct rate of discriminant reached 90.3%.Stepwise discriminant analysis and factor analysis were combined to process the seven conventional ions data.Bayes linear discriminant function and function values from 1740 exploration line to 2740 exploration line in -375 m sublevel was obtained.Bayes linear function discriminant results are completely consistent with the results of the factor analysis method,and the two selected discriminant water samples also agree.The consistency of the discriminant results shows that the factor analysis method and the stepwise analysis method are mutually verified.A multivariate statistical method was combined to obtain a quantitative Bayes linear discriminant function,which was applied to the recognition of the source type in the mining area.It was only necessary to know the ion concentration of the corresponding variable,and the water sample type could be determined by substituting it.This method has the characters of accurate,fast,and economical.

Key words: Sanshandao gold mine, mine water inrush, factor analysis, systematic cluster analysis, Bayes linear model, discriminate of water sources

CLC Number: 

  • TD745

Fig.1

Regional geological tectonic map"

Fig.2

Geological sketch of study area"

Fig.3

Location map of water samples collection"

Table 1

Hydrochemical parameters of water samples from -375 m middle section"

水样位置K+/(mg·L-1Na+/(mg·L-1Ca2+/(mg·L-1Mg2+ /(mg·L-1Cl-/(mg·L-1SO42-/(mg·L-1HCO3-/(mg·L-1pH值(标准值)EC/(μs·cm-1TDS/(mg·L-1
375-1-1248.410 400761.51 287.919 8522 305.4219.67.1944 90035 074.8
375-1-21979 750801.61 222.318453.52 334.3233.77.7439 60032 995
375-1-320510 031.2849.71 239.318 916.12 497.6244.67.0742 30033 991.5
375-1-4190.29 800841.71 21519 224.5624.4250.77.3142 10032 147.5
375-1-5179.79 875721.41 166.418 402.12 372.7253.27.3440 00032 974.6
375-2-128610 650697.41 312.219 8522 286.2207.47.0345 20035 292.8
375-3-1299.29 445537.11 132.417 583.22 017.3219.67.3640 80031 233.8
375-3-22418 900681.41 04016 705.82 190.2233.77.5637 00029 992.1
375-3-3275.89 200753.51 069.217 579.72 286.2273.37.4539 80031 437.9
375-4-1316.89 825641.31 044.917 583.22 017.3201.37.4941 10031 629.8
375-4-22588 900921.8945.317 014.22 295.8181.27.4637 20030 516.3
375-4-3282.59 7251 122.21 001.218 607.72 516.8170.87.1141 80033 433.8
375-4-4285.59 9001 314.6831.118 710.52 401.5168.47.3240 80033 614.5
375-4-5285.110 0001 154.3916.118 874.32 401.5170.87.0340 90033 821.3
375-5-126012 0501 146.31 020.621 9792 459.1119.67.2950 40039 052.7
375-5-220810 886.21 52397220 8182 545.6114.17.4142 50037 081.6
375-5-319510 9001 595.2957.419 738.62 708.91087.0545 80036 216.5
375-5-4226.511 2501 643.3823.821 280.62 603.285.47.3244 70037 916.2
375-5-5252.511 50011 500517.621 471.72 353.581.87.0344 70038 156.3
375-6-1337.511 4502 084.2777.622 156.32 497.6107.47.0249 70039 411.6
375-6-226211 8192 276.5726.622 411.52 4881087.0644 40040 117.1
375-6-3257.89 062.55051 142.117 560.52 315236.17.6434 50031 080.1
375-7-130510 7001 723.4923.421 2702 363.1134.27.1748 10037 424.9
375-7-22519 562.55211 154.317 642.42 353.5225.77.1639 30031 716.5
375-7-3265.69 187.54811 161.517 731.72 238.22307.6634 40031 296.2
375-8-1290.410 3501 0261 078.918 965.71 729.1158.67.344 10033 598.7
375-8-229410 937.52 312.6626.921 794.72 257.4102.57.6643 90038 344.7
375-8-3198.110 438721.41 287.919 597.12 401.5233.77.2243 00034 884
375-8-4281.39 062.55211 154.317 389.32 353.52307.5435 00030 992.4
375-9-120510 375681.41 268.518 9192 449.5256.27.6342 50034 154.6
375-9-2262.59 2505211 154.317 731.72 343.9222.77.5734 90031 486.7

Table 2

Correlation matrix of variables"

K+Na+Ca2+Mg2+Cl-SO42-CHO-pHECTDS
K+1.0000.0100.194-0.3660.0650.037-0.270-0.1050.1120.065
Na+0.0101.0000.763-0.4960.9560.260-0.766-0.5220.8780.969
Ca2+0.1940.7631.000-0.8600.8420.254-0.880-0.3980.6400.854
Mg2+-0.366-0.496-0.8601.000-0.555-0.2370.8290.235-0.334-0.576
Cl-0.0650.9560.842-0.5551.0000.209-0.777-0.4700.8350.987
SO42-0.0370.2600.254-0.2370.2091.000-0.328-0.1520.1170.345
CHO--0.270-0.766-0.8800.829-0.777-0.3281.0000.404-0.621-0.805
pH-0.105-0.522-0.3980.235-0.470-0.1520.4041.000-0.624-0.495
EC0.1120.8780.640-0.3340.8350.117-0.621-0.6241.0000.839
TDS0.0650.9690.854-0.5760.9870.345-0.805-0.4950.8391.000

Table 3

Explanation of total variance"

主成分特征值特征值
方差方差/%累计方差/%方差方差/%累计方差/%
16.04860.47660.4766.04860.47660.476
21.33113.31173.7871.33160.47673.787
30.9729.71583.5020.9729.71583.502
40.8298.29091.7920.8298.29091.792
50.5005.00396.794
60.1371.37098.164
70.1101.09999.263
80.0590.59199.854
90.0150.146100.000
100.0000020.000018100.000

Table 4

Factor component matrix"

变量因子载荷旋转因子载荷方差HI2
F1F2F3F4F11F21F31F41
K+0.195-0.712-0.4510.3930.1360.055-0.938-0.0110.90
Na+0.9330.2720.014-0.0490.6950.6520.1780.0830.95
Ca2+0.918-0.2120.030-0.2130.9150.282-0.1000.0860.93
Mg2+-0.7220.575-0.0320.213-0.8690.0330.359-0.1170.90
Cl-0.9450.185-0.024-0.1360.7700.5780.1350.0220.95
SO42-0.330-0.1660.7940.4730.1700.0760.0060.9770.99

CHO-

pH

-0.9000.273-0.0590.096-0.865-0.2890.185-0.1790.90
-0.575-0.2970.264-0.557-0.071-0.8610.195-0.1200.80
EC0.8370.353-0.2350.1300.4780.8150.044-0.0520.90
TDS0.9660.1610.084-0.0530.7650.5830.1270.1610.97

Table 5

Factor score coefficient matrix"

变量主成分得分系数
F1F2F3F4
K+0.195-0.712-0.4510.393
Na+0.9330.2720.014-0.049
Ca2+0.918-0.2120.030-0.213
Mg2+-0.7220.575-0.0320.213
Cl-0.9450.185-0.024-0.136
SO42-0.330-0.1660.7940.473
CHO--0.9000.273-0.0590.096
pH-0.575-0.2970.264-0.557
EC0.8370.353-0.2350.130
TDS0.9660.1610.084-0.053

Table 6

Factor scores matrix of water samples"

水样位置因子得分
F1F2F3F4
375-1-10.1451.681-0.2990.707
375-1-2-4.6600.7811.220-1.139
375-1-3-1.5361.9690.5660.858
375-1-4-4.6602.575-3.173-2.340
375-1-5-3.6391.5950.991-0.270
375-2-11.1801.388-0.9401.441
375-3-1-4.223-0.812-1.1280.426
375-3-2-6.907-1.0240.311-0.483
375-3-3-5.205-0.758-0.1560.309
375-4-1-3.236-1.471-1.1330.118
375-4-2-4.830-1.6970.311-0.176
375-4-30.083-0.688-0.0631.154
375-4-40.297-1.496-0.0080.251
375-4-50.903-0.763-0.3631.059
375-5-18.1111.430-0.0230.119
375-5-23.9540.6561.285-0.716
375-5-34.9351.3221.1780.440
375-5-46.7170.2521.057-0.463
375-5-513.600-1.5550.094-1.265
375-6-110.150-0.631-0.9991.186
375-6-29.6440.1870.1850.061
375-6-3-6.903-1.1620.638-0.402
375-7-16.120-0.197-0.7850.665
375-7-2-3.7980.163-0.0050.873
375-7-3-6.697-1.1970.421-0.492
375-8-10.153-0.098-1.786-0.198
375-8-26.550-1.8470.027-1.430
375-8-3-0.8642.3460.5890.270
375-8-4-6.468-1.4290.3250.153
375-9-1-2.9161.5401.098-0.542
375-9-2-5.999-1.0600.569-0.175

Fig.4

Dendrogram of systematic cluster"

Table 7

Identification of water samples"

点号Bayes函数值
M1M2类型

375-1-1

375-1-3

375-1-4

375-1-5

375-2-1

375-3-1

375-3-2

375-3-3

375-4-1

375-4-2

375-4-3

375-4-5

375-5-1

242.5498

237.3542

230.8616

234.4816

247.6762

207.2148

191.8156

216.3004

215.0894

174.0706

201.0804

211.2554

269.7998

230.4202

219.9806

211.5024

215.1574

238.4558

190.3102

170.2684

190.8676

202.5736

159.6634

192.2126

203.7626

279.5202

M1

M1

M1

M1

M1

M1

M1

M1

M1

M1

M1

M1

M2

375-5-2

375-5-3

375-5-4

375-5-5

375-6-1

375-6-2

375-7-2

375-7-3

375-8-1

375-8-3

375-8-4

375-9-1

375-9-2

224.8802

223.3290

228.6402

236.6734

243.4762

257.3320

213.6241

201.2025

220.0818

248.7216

196.5775

253.9956

201.0476

229.5296

228.8770

239.0118

248.7846

251.8558

267.4750

196.4774

181.5960

215.9982

234.8644

176.3460

236.7634

182.7464

M2

M2

M2

M2

M2

M2

M1

M1

M1

M1

M1

M1

M1

检测水点Bayes函数值
M1M2类型
375-1-2223.2656205.9684M1
375-4-4206.7442199.0778M1
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