海下开采岩层变形混沌时序重构与安全预警系统研究
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摘要
随着人类大规模资源的利用和开发,陆地资源日渐枯竭,人们逐渐把目光投向蕴含丰富矿产资源的海洋。目前,对于海底基岩矿床的开采处于起步阶段,相关的研究较少。海下开采与陆地开采条件的主要区别是上覆海水,一旦海水涌入井下,就会带来灾难性的后果。因此,海下矿床安全开采的关键是对采区上覆岩层变形的控制。
由于海下开采岩层变形是一个复杂系统,其中蕴含着各种随机的、不确定的因素,其演化过程伴随着物质、能量的交换,表现出复杂的非线性行为。因此,本文利用非线性科学中的混沌理论来研究海下开采岩层变形问题。
对海下岩层变形时间序列进行混沌分析的基础是相空间重构技术,本文在详细分析该理论的基础上,应用几何不变量法和复自相关函数法确定了嵌入维数m和延迟时间量τ,对三山岛金矿岩层变形时间序列进行了相空间重构。计算了混沌特征参量—最大Lyapunov(?)旨数λmax。和关联维数D2,两者相结合对重构相空间进行了识别,证明了山岛金矿海下丌采岩层变形的混沌性。用混沌理论揭示了不同高度岩层位移在相空间中的相点距离演变规律,并利用Fourier变换得到不同岩层的时间-功率谱曲线,从而获得了岩层变形能量随时间的变化规律。
应用神经网络建立了岩层变形相空间相点距离演化预测模型,预测了新立矿区海底开采岩层变形,并建立了海底开采岩层变形安全预警系统。采用梯度下降法与混沌优化方法相结合方法训练神经网络,使神经网络预测模型实现快速训练的同时,避免陷入局部极小,同时提高了模型计算精度。并针对不同的预警区域,提出了相应的安全措施。
研究表明,岩层变形表现出混沌特征,对其相空间重构后,岩层变形的细微变化特征被放大,其内在规律能得到充分展示,为建立海下开采安全预警系统提供了基础。
As land resources have been developed and used in large scale, it was depleted day by day. People gradually turn their gaze to the ocean which contains rich mineral resources. Now, mining of bedrock deposit undersea water pressure is still at an early stage and the relative research on undersea mining work is seldom seen. The primary difference between mining under the sea and mining on land is that the seawater above undersea deposit. Once the seawater rush into the stope in undensea, it would result in an escalating probability of disaster. So, the key to safety mining of the bedrock deposit undersea is to control the deformation of the overlying strata.
The stratum displacement during is very complicated, which contains a lot of uncertain and random parameters. During mining process, there are the exchange substance and energy with outside and the displacement shows a complicated nonlinear feature. Therefore, this paper adopt a nonlinear theory, that is chaostheory, to analyze stratum displacement of undersea mining.
The basis of the analysis of displacement of undersea strata with chaos theory is the phase space reconstruction of time series. The paper calculates the time delay and embedding dimension by using both geometrical invariant method and compound autocorrelation function method. The time series of stratum displacement of undersea mining in Sanshandao gold mine are reconstructed in phase space. The chaotic invariants of measured time series for Sanshandao gold mine such as correlation dimension and the Lyapunov exponent, are calculated. The results show that the time series obtained in Sanshandao gold mine are chaotic time series. The changing laws of distance between two phase points for the displacement of strata at different heights in the phase space are revealed by using the chaos theory. The power spectrum curves for stratum displacement has been got by Fourier transform and the laws of stratum displacement energy are obtained.
A prediction model for the evolution laws of phase space distance of stratum displacement is established based on the neural network, by which the stratum displacement of undersea mining in Xinli mining area is predicted. Then the safety warning system of strata displacement for the undersea mining is established. A neural network is trained through the combination of gradient descent method and chaos optimization method. The neural network model can achieve the merit of rapid training. Meanwhile, the defect of local minimum is avoided, and the calculation precision of the model is improved, and some control measures are given for different warning areas.
The results show that the strata at different heights have different chaotic behaviors. After the reconstruction of phase space, subtle features of strata displacement change are enlarged, and the inherent law of strata is adequately demonstrated, which is the basis of the safety warning system of the undersea mining.
由于海下开采岩层变形是一个复杂系统,其中蕴含着各种随机的、不确定的因素,其演化过程伴随着物质、能量的交换,表现出复杂的非线性行为。因此,本文利用非线性科学中的混沌理论来研究海下开采岩层变形问题。
对海下岩层变形时间序列进行混沌分析的基础是相空间重构技术,本文在详细分析该理论的基础上,应用几何不变量法和复自相关函数法确定了嵌入维数m和延迟时间量τ,对三山岛金矿岩层变形时间序列进行了相空间重构。计算了混沌特征参量—最大Lyapunov(?)旨数λmax。和关联维数D2,两者相结合对重构相空间进行了识别,证明了山岛金矿海下丌采岩层变形的混沌性。用混沌理论揭示了不同高度岩层位移在相空间中的相点距离演变规律,并利用Fourier变换得到不同岩层的时间-功率谱曲线,从而获得了岩层变形能量随时间的变化规律。
应用神经网络建立了岩层变形相空间相点距离演化预测模型,预测了新立矿区海底开采岩层变形,并建立了海底开采岩层变形安全预警系统。采用梯度下降法与混沌优化方法相结合方法训练神经网络,使神经网络预测模型实现快速训练的同时,避免陷入局部极小,同时提高了模型计算精度。并针对不同的预警区域,提出了相应的安全措施。
研究表明,岩层变形表现出混沌特征,对其相空间重构后,岩层变形的细微变化特征被放大,其内在规律能得到充分展示,为建立海下开采安全预警系统提供了基础。
As land resources have been developed and used in large scale, it was depleted day by day. People gradually turn their gaze to the ocean which contains rich mineral resources. Now, mining of bedrock deposit undersea water pressure is still at an early stage and the relative research on undersea mining work is seldom seen. The primary difference between mining under the sea and mining on land is that the seawater above undersea deposit. Once the seawater rush into the stope in undensea, it would result in an escalating probability of disaster. So, the key to safety mining of the bedrock deposit undersea is to control the deformation of the overlying strata.
The stratum displacement during is very complicated, which contains a lot of uncertain and random parameters. During mining process, there are the exchange substance and energy with outside and the displacement shows a complicated nonlinear feature. Therefore, this paper adopt a nonlinear theory, that is chaostheory, to analyze stratum displacement of undersea mining.
The basis of the analysis of displacement of undersea strata with chaos theory is the phase space reconstruction of time series. The paper calculates the time delay and embedding dimension by using both geometrical invariant method and compound autocorrelation function method. The time series of stratum displacement of undersea mining in Sanshandao gold mine are reconstructed in phase space. The chaotic invariants of measured time series for Sanshandao gold mine such as correlation dimension and the Lyapunov exponent, are calculated. The results show that the time series obtained in Sanshandao gold mine are chaotic time series. The changing laws of distance between two phase points for the displacement of strata at different heights in the phase space are revealed by using the chaos theory. The power spectrum curves for stratum displacement has been got by Fourier transform and the laws of stratum displacement energy are obtained.
A prediction model for the evolution laws of phase space distance of stratum displacement is established based on the neural network, by which the stratum displacement of undersea mining in Xinli mining area is predicted. Then the safety warning system of strata displacement for the undersea mining is established. A neural network is trained through the combination of gradient descent method and chaos optimization method. The neural network model can achieve the merit of rapid training. Meanwhile, the defect of local minimum is avoided, and the calculation precision of the model is improved, and some control measures are given for different warning areas.
The results show that the strata at different heights have different chaotic behaviors. After the reconstruction of phase space, subtle features of strata displacement change are enlarged, and the inherent law of strata is adequately demonstrated, which is the basis of the safety warning system of the undersea mining.
引文
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