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Gold Science and Technology ›› 2019, Vol. 27 ›› Issue (2): 249-256.doi: 10.11872/j.issn.1005-2518.2019.02.249

• Mining Technology and Mine Management • Previous Articles    

Prediction of Mine Subsidence Area Based on Chaotic Time Series Analysis

Junhui ZENG(),Xibing LI()   

  1. School of Resources and Safety Engineering，Central South University，Changsha 410083，Hunan，China
  • Received:2018-01-23 Revised:2018-03-28 Online:2019-04-30 Published:2019-04-30
  • Contact: Xibing LI E-mail:shvezjh@163.com;xbli@csu.edu.cn

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Abstract:

With the advance of mining to deep in domestic underground metal mines,surface subsidence caused by mining has seriously threatened the industrial production of the mine and the ecological environment of the mining area.Therefore，accurately predicting the extent of mine subsidence area is of great significance to mine safety production and ecological protection of mining areas.Because the mine subsidence area is a complex system，it contains various random factors，and the evolution process of the system is often accompanied by the exchange of energy，showing nonlinear characteristics.This paper demonstrates the mine subsidence area as a nonlinear dissipative dynamic system.Combined with the time series analysis method in chaos theory，the range of mine subsidence area is predicted.The specific method studied in this paper is based on the phase space reconstruction theory，and the phase space reconstruction of the deformation time series of the mine subsidence area is carried out.A small data amount algorithm is used to calculate the key index of the time series-the largest Lyapunov exponent.According to the calculation results，the chaos of the mine subsidence zone system is identified.The evolution law and energy variation law of the phase distance of the collapse zone in the phase space are studied.The prediction model of the boundary of the subsidence zone is established.The model is used to analyze and predict the subsidence zone of the Hongling lead-zinc mine.The results show that the formation and expansion of the subsidence area of the Hongling lead-zinc mine is the result of the comprehensive effects of all kinds of factors such as geological conditions，mining engineering operations and inherent nonlinear characteristics.The maximum Lyapunov exponent and associated dimension of the time series of the subsidence area of the Hongling lead-zinc mine are calculated.It is verified that the variation of the subsidence area of Hongling lead-zinc mine has chaotic characteristics.The time series analysis method can well reflect the inherent law of the range change of the subsidence area.The chaotic characteristics of the different locations in the collapsed zone are different in the phase space due to different geological conditions and mechanical properties.The established mine subsidence area prediction system can better predict the change of the subsidence area.The predicted value is basically consistent with the actual value，and the error size does not exceed 0.1%，which verifies the reliability of the method.It provides a new idea for the prediction of mine subsidence area and guides mine safety production.

Key words: underground mining, mine monitoring, chaotic time series, phase space reconstruction, Lyapunov index, surface deformation law, subsidence area prediction

CLC Number: 

  • TD31

Fig.1

Partial subsidence area of Hongling lead zinc mine"

Fig.2

Pull cracks in the subsidence area of Hongling lead zinc mine"

Fig.3

Phase space distance evolution curve of deformation in subsidence area"

Fig.4

Time power spectrum curve of deformation in subsidence area"

Fig.5

Measured displacement curves"

Fig.7

Comparison between prediction and actual scope in subsidence area"

Table 1

Prediction results of displacement at A2 monitoring point"

时间/d实测值/m预测值/m相对误差/%
39029.4229.610.65
42031.4331.680.80
45035.0635.010.14
48040.8541.090.59
51049.8850.210.66
54057.0256.720.53
57066.1466.570.65
60075.0574.450.80

Table 2

Prediction results of displacement at A9 monitoring point"

时间/d实测值/m预测值/m相对误差/%
39025.0625.250.76
42026.0826.320.92
45028.7128.650.21
48033.4933.730.72
51041.5541.860.75
54048.6248.360.53
57059.8359.310.87
60071.6971.110.81

Fig.6

Comparison curves of measured and predicted displacement"

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