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中文摘要：
对海底金矿床开采过程中不同高度岩层位移进行了监测,对岩层变形时间序列重构相空间,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。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。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。
英文摘要：
The displacements of strata at different heights of an undersea gold mine during the mining process are monitored.The time series of stratum displacement are reconstructed in phase space.The changing laws of distance between two phase points for the displacement of strata at different heights in the phase space are revealed using the chaos theory.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 security early 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.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 security warning system of the undersea mining.
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