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Gold Science and Technology ›› 2019, Vol. 27 ›› Issue (4): 539-547.doi: 10.11872/j.issn.1005-2518.2019.04.539

• Mining Technology and Mine Management • Previous Articles     Next Articles

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

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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

CLC Number: 

  • TD742

Table 1

Classification standard of water inrush risk evaluation indexes"

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

Fig.1

Cloud model for the water inrush risk of RQD"

Table 2

Measured values of evaluation indexes for each middle segment"

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

Table 3

Weight values of evaluation indexes"

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

Table 4

Evaluation results of water inrush risk"

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

Fig.2

Correlation dimension of water inflow data"

Fig.3

Autocorrelation function curve of water inflow"

Fig.4

Distance evolution curve in phase space of water inflow"

Fig.5

Comparison of predicted and actual values of water inflow"

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