多元统计分析在滨海矿区水源识别中的应用——以三山岛金矿为例

doi:10.11872/j.issn.1005-2518.2019.02.207

Abstract

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

Guowei LIU,Fengshan MA,Jie GUO,Yunlong DU,Chenglu HOU,Wei LI. Application of Multivariate Statistical Analysis to Identify Water Source in Coast Mine Area：As Example of Sanshandao Gold Mine[J].Gold Science and Technology, 2019, 27(2): 207-215.

Figures/Tables 11

Fig.1

Fig.2

Fig.3

Table 1

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

水样位置	K⁺/（mg·L^-1）	Na⁺/（mg·L^-1）	Ca²⁺/（mg·L^-1）	Mg²⁺ /（mg·L^-1）	Cl^-/（mg·L^-1）	SO₄^2-/（mg·L^-1）	HCO $3 -$ /（mg·L^-1）	pH值（标准值）	EC/（μs·cm^-1）	TDS/（mg·L^-1）
375-1-1	248.4	10 400	761.5	1 287.9	19 852	2 305.4	219.6	7.19	44 900	35 074.8
375-1-2	197	9 750	801.6	1 222.3	18453.5	2 334.3	233.7	7.74	39 600	32 995
375-1-3	205	10 031.2	849.7	1 239.3	18 916.1	2 497.6	244.6	7.07	42 300	33 991.5
375-1-4	190.2	9 800	841.7	1 215	19 224.5	624.4	250.7	7.31	42 100	32 147.5
375-1-5	179.7	9 875	721.4	1 166.4	18 402.1	2 372.7	253.2	7.34	40 000	32 974.6
375-2-1	286	10 650	697.4	1 312.2	19 852	2 286.2	207.4	7.03	45 200	35 292.8
375-3-1	299.2	9 445	537.1	1 132.4	17 583.2	2 017.3	219.6	7.36	40 800	31 233.8
375-3-2	241	8 900	681.4	1 040	16 705.8	2 190.2	233.7	7.56	37 000	29 992.1
375-3-3	275.8	9 200	753.5	1 069.2	17 579.7	2 286.2	273.3	7.45	39 800	31 437.9
375-4-1	316.8	9 825	641.3	1 044.9	17 583.2	2 017.3	201.3	7.49	41 100	31 629.8
375-4-2	258	8 900	921.8	945.3	17 014.2	2 295.8	181.2	7.46	37 200	30 516.3
375-4-3	282.5	9 725	1 122.2	1 001.2	18 607.7	2 516.8	170.8	7.11	41 800	33 433.8
375-4-4	285.5	9 900	1 314.6	831.1	18 710.5	2 401.5	168.4	7.32	40 800	33 614.5
375-4-5	285.1	10 000	1 154.3	916.1	18 874.3	2 401.5	170.8	7.03	40 900	33 821.3
375-5-1	260	12 050	1 146.3	1 020.6	21 979	2 459.1	119.6	7.29	50 400	39 052.7
375-5-2	208	10 886.2	1 523	972	20 818	2 545.6	114.1	7.41	42 500	37 081.6
375-5-3	195	10 900	1 595.2	957.4	19 738.6	2 708.9	108	7.05	45 800	36 216.5
375-5-4	226.5	11 250	1 643.3	823.8	21 280.6	2 603.2	85.4	7.32	44 700	37 916.2
375-5-5	252.5	11 500	11 500	517.6	21 471.7	2 353.5	81.8	7.03	44 700	38 156.3
375-6-1	337.5	11 450	2 084.2	777.6	22 156.3	2 497.6	107.4	7.02	49 700	39 411.6
375-6-2	262	11 819	2 276.5	726.6	22 411.5	2 488	108	7.06	44 400	40 117.1
375-6-3	257.8	9 062.5	505	1 142.1	17 560.5	2 315	236.1	7.64	34 500	31 080.1
375-7-1	305	10 700	1 723.4	923.4	21 270	2 363.1	134.2	7.17	48 100	37 424.9
375-7-2	251	9 562.5	521	1 154.3	17 642.4	2 353.5	225.7	7.16	39 300	31 716.5
375-7-3	265.6	9 187.5	481	1 161.5	17 731.7	2 238.2	230	7.66	34 400	31 296.2
375-8-1	290.4	10 350	1 026	1 078.9	18 965.7	1 729.1	158.6	7.3	44 100	33 598.7
375-8-2	294	10 937.5	2 312.6	626.9	21 794.7	2 257.4	102.5	7.66	43 900	38 344.7
375-8-3	198.1	10 438	721.4	1 287.9	19 597.1	2 401.5	233.7	7.22	43 000	34 884
375-8-4	281.3	9 062.5	521	1 154.3	17 389.3	2 353.5	230	7.54	35 000	30 992.4
375-9-1	205	10 375	681.4	1 268.5	18 919	2 449.5	256.2	7.63	42 500	34 154.6
375-9-2	262.5	9 250	521	1 154.3	17 731.7	2 343.9	222.7	7.57	34 900	31 486.7

Table 1

Table 2

Table 3

Table 4

Table 5

Table 6

Fig.4

Table 7

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	K⁺	Na⁺	Ca²⁺	Mg²⁺	Cl^-	SO₄^2-	CHO^-	pH	EC	TDS
K⁺	1.000	0.010	0.194	-0.366	0.065	0.037	-0.270	-0.105	0.112	0.065
Na⁺	0.010	1.000	0.763	-0.496	0.956	0.260	-0.766	-0.522	0.878	0.969
Ca²⁺	0.194	0.763	1.000	-0.860	0.842	0.254	-0.880	-0.398	0.640	0.854
Mg²⁺	-0.366	-0.496	-0.860	1.000	-0.555	-0.237	0.829	0.235	-0.334	-0.576
Cl^-	0.065	0.956	0.842	-0.555	1.000	0.209	-0.777	-0.470	0.835	0.987
SO₄^2-	0.037	0.260	0.254	-0.237	0.209	1.000	-0.328	-0.152	0.117	0.345
CHO^-	-0.270	-0.766	-0.880	0.829	-0.777	-0.328	1.000	0.404	-0.621	-0.805
pH	-0.105	-0.522	-0.398	0.235	-0.470	-0.152	0.404	1.000	-0.624	-0.495
EC	0.112	0.878	0.640	-0.334	0.835	0.117	-0.621	-0.624	1.000	0.839
TDS	0.065	0.969	0.854	-0.576	0.987	0.345	-0.805	-0.495	0.839	1.000

主成分	特征值			特征值
主成分	方差	方差/%	累计方差/%	方差	方差/%	累计方差/%
1	6.048	60.476	60.476	6.048	60.476	60.476
2	1.331	13.311	73.787	1.331	60.476	73.787
3	0.972	9.715	83.502	0.972	9.715	83.502
4	0.829	8.290	91.792	0.829	8.290	91.792
5	0.500	5.003	96.794
6	0.137	1.370	98.164
7	0.110	1.099	99.263
8	0.059	0.591	99.854
9	0.015	0.146	100.000
10	0.000002	0.000018	100.000

变量	因子载荷				旋转因子载荷				方差H_I²
变量	F1	F2	F3	F4	F11	F21	F31	F41	方差H_I²
K⁺	0.195	-0.712	-0.451	0.393	0.136	0.055	-0.938	-0.011	0.90
Na⁺	0.933	0.272	0.014	-0.049	0.695	0.652	0.178	0.083	0.95
Ca²⁺	0.918	-0.212	0.030	-0.213	0.915	0.282	-0.100	0.086	0.93
Mg²⁺	-0.722	0.575	-0.032	0.213	-0.869	0.033	0.359	-0.117	0.90
Cl^-	0.945	0.185	-0.024	-0.136	0.770	0.578	0.135	0.022	0.95
SO₄^2-	0.330	-0.166	0.794	0.473	0.170	0.076	0.006	0.977	0.99
CHO^- pH	-0.900	0.273	-0.059	0.096	-0.865	-0.289	0.185	-0.179	0.90
CHO^- pH	-0.575	-0.297	0.264	-0.557	-0.071	-0.861	0.195	-0.120	0.80
EC	0.837	0.353	-0.235	0.130	0.478	0.815	0.044	-0.052	0.90
TDS	0.966	0.161	0.084	-0.053	0.765	0.583	0.127	0.161	0.97

变量	主成分得分系数
变量	F1	F2	F3	F4
K⁺	0.195	-0.712	-0.451	0.393
Na⁺	0.933	0.272	0.014	-0.049
Ca²⁺	0.918	-0.212	0.030	-0.213
Mg²⁺	-0.722	0.575	-0.032	0.213
Cl^-	0.945	0.185	-0.024	-0.136
SO₄^2-	0.330	-0.166	0.794	0.473
CHO^-	-0.900	0.273	-0.059	0.096
pH	-0.575	-0.297	0.264	-0.557
EC	0.837	0.353	-0.235	0.130
TDS	0.966	0.161	0.084	-0.053

水样位置	因子得分
水样位置	F1	F2	F3	F4
375-1-1	0.145	1.681	-0.299	0.707
375-1-2	-4.660	0.781	1.220	-1.139
375-1-3	-1.536	1.969	0.566	0.858
375-1-4	-4.660	2.575	-3.173	-2.340
375-1-5	-3.639	1.595	0.991	-0.270
375-2-1	1.180	1.388	-0.940	1.441
375-3-1	-4.223	-0.812	-1.128	0.426
375-3-2	-6.907	-1.024	0.311	-0.483
375-3-3	-5.205	-0.758	-0.156	0.309
375-4-1	-3.236	-1.471	-1.133	0.118
375-4-2	-4.830	-1.697	0.311	-0.176
375-4-3	0.083	-0.688	-0.063	1.154
375-4-4	0.297	-1.496	-0.008	0.251
375-4-5	0.903	-0.763	-0.363	1.059
375-5-1	8.111	1.430	-0.023	0.119
375-5-2	3.954	0.656	1.285	-0.716
375-5-3	4.935	1.322	1.178	0.440
375-5-4	6.717	0.252	1.057	-0.463
375-5-5	13.600	-1.555	0.094	-1.265
375-6-1	10.150	-0.631	-0.999	1.186
375-6-2	9.644	0.187	0.185	0.061
375-6-3	-6.903	-1.162	0.638	-0.402
375-7-1	6.120	-0.197	-0.785	0.665
375-7-2	-3.798	0.163	-0.005	0.873
375-7-3	-6.697	-1.197	0.421	-0.492
375-8-1	0.153	-0.098	-1.786	-0.198
375-8-2	6.550	-1.847	0.027	-1.430
375-8-3	-0.864	2.346	0.589	0.270
375-8-4	-6.468	-1.429	0.325	0.153
375-9-1	-2.916	1.540	1.098	-0.542
375-9-2	-5.999	-1.060	0.569	-0.175

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

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