动态变形监测数据混沌特性分析及预测模型研究
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摘要
变形数据分析及变形预测是一个复杂的系统工程,随着GPS等变形监测新技术的发展,如何引进先进的数学理论和信号分析方法来深入地了解变形的非线性、复杂性,探讨变形量信息提取、预测预报及变形体稳定性判定的新方法是本文研究的重点。本文研究的主要成果和具体内容如下:
(1)集中介绍了几类变形体变形混沌性态判定方法,阐述了进行变形监测时序非线性判定的意义,并结合实例进行了实际工程的非线性判定研究,指出Lyapunov指数可以作为变形体内部不同部分动态变形性态的判定指标;
(2)小波分析方法在混沌变形监测数据噪声去除方面具有较强的能力,而经验模态分解(EMD)技术在变形量的提取方面具有较强功能。本论文结合仿真数据深入研究了两者的优缺点,提出了小波-EMD降噪及变形量信息提取耦合模型。并以内蒙古某煤矿采动区地表动态GPS数据为例进行了应用研究,实例证实该模型具有较强的去噪能力和变形量信息提取功能,适用于连续动态GPS变形数据处理;
(3)Kalman滤波在消除变形监测数据白噪声方面具有独到优势,论文基于Kalman滤波的噪声滤除能力,重点研究了基于Kalman滤波的EMD信息提取方法,并以工程监测数据为例进行应用研究,结果证实该模型具有较好的应用效果;
(4)基于对变形数据的混沌非线性分析,引入加权一阶局域法多步预测模型,对变形体混沌时间序列进行多步预测,取得了较理想的预报结果;以混沌动力学参数m作为判定神经网络输入层节点个数,研究了基于相空间重构的多步预测神经网络建立方法,构建了基于混沌相空间重构的时间延迟BP神经网络预报模型,并结合工程实例进行了预报试验;
(5)EMD方法能够有效的把非线性数据序列自适应的分解成一系列从高频到低频的分量,信号平稳性能够得到有效提高。基于此本文提出顾及尺度特征的EMD多尺度预报模型,经工程实例验证,该模型具有较高的预测精度;文中还进一步研究和建立了适用于具有混沌非线性特征的监测时序的EMD多尺度分解SVM变形预报模型,并进行了实例分析研究;
(6)把混沌理论及EMD分析技术引入老采空区动态空间信息处理及变形预报领域,重构了老采空区上方地表沉降系统的动力学过程,对兖州某矿某老采空区复垦场地的监测数据分析表明,该老采空区上方地表变形具有混沌特性;本文首次采用EMD分解技术对上述数据进行了分尺度分析研究,实例试验结果证实,EMD分析技术可以作为研究和判定老采空区上方地表稳定性的有效理论方法。
Deformation data analysis and prediction is a complex systematic project. With the development of the new technique of deformation monitoring such as GPS, the new methods of extracting deformation information, predicting and evaluating the stability of deformation body based on introducing advanced mathematical theory and signal analysis methods to deeply understand the non-linear and complexity of deformation.
The main results of this study and the specific contents are as follows:
(1) Introducing the judging method of chaotic behavior of several types of deformation body. Determining the non-linear judgment of deformation monitoring combined with examples of actual projects. Pointing out that the Lyapunov-index can serve as a deformation evaluation criteria in judging different part of dynamic deformation body.
(2) Wavelet transform has a strong ability to remove the noise of the chaotic data, however empirical mode decomposition(EMD)does well in extracting of the information. In this paper, comparing the two methods and pointing out their advantages and disadvantages with the help of simulation. And proposing Wavelet-EMD coupled model to extract the deformation information and remove the noise. The results in real project show that the model is very useful in continuously dynamic GPS deformation monitoring data.
(3)Kalman filter has an original advantage in eliminating the white noise and predicting future systematic state of deformation data. The key research has analyzed the noise reduction capabilities of Kalman filter and EMD. The dynamic GPS data of a surface mining area in Mongolia was processed and analyzed to prove the effectiveness in practical application of the model.
(4) Chaotic time series of deformation body was multi-step predicted and obtained ideal prediction results through weighted local-region model can be gotten, which based on chaotic non-linear analysis method. Multi-steps neural network method was studied by phase space reconstruction and time delay BP neural network prediction model was reconstructed based on chaotic phase space reconstruction method, when chaotic dynamics parameter m is used as the number of input layer modes, and prediction experiment was made with an engineer example.
(5)EMD method can decompose the non-linear data to a series components from the high-frequency to low-frequency, and the signal stationary be significantly improved. The EMD multi-scale prediction model was put forward based on the multi-scale characteristics of non-linear time series, and had high prediction accuracy. The integration deformation prediction model of EMD multi-scale decomposition and SVM was researched and established, which was suitable to process non-linear deformation time series.
(6)The chaotic theory and EMD analysis technology were introduced to process deformation data, make deformation prediction and reconstruct dynamic process of ground surface settlement of old goaf. Studying and analyzing on the reclamation field of old goaf in Yanzhou coal mine show that ground surface deformation of the old goaf has chaotic characteristics. In this paper, EMD technique was first introduced to analyze deformation data. Experimental results show that EMD analysis technology is an effective method to research and judge the stability of the ground surface of old goaf.
(1)集中介绍了几类变形体变形混沌性态判定方法,阐述了进行变形监测时序非线性判定的意义,并结合实例进行了实际工程的非线性判定研究,指出Lyapunov指数可以作为变形体内部不同部分动态变形性态的判定指标;
(2)小波分析方法在混沌变形监测数据噪声去除方面具有较强的能力,而经验模态分解(EMD)技术在变形量的提取方面具有较强功能。本论文结合仿真数据深入研究了两者的优缺点,提出了小波-EMD降噪及变形量信息提取耦合模型。并以内蒙古某煤矿采动区地表动态GPS数据为例进行了应用研究,实例证实该模型具有较强的去噪能力和变形量信息提取功能,适用于连续动态GPS变形数据处理;
(3)Kalman滤波在消除变形监测数据白噪声方面具有独到优势,论文基于Kalman滤波的噪声滤除能力,重点研究了基于Kalman滤波的EMD信息提取方法,并以工程监测数据为例进行应用研究,结果证实该模型具有较好的应用效果;
(4)基于对变形数据的混沌非线性分析,引入加权一阶局域法多步预测模型,对变形体混沌时间序列进行多步预测,取得了较理想的预报结果;以混沌动力学参数m作为判定神经网络输入层节点个数,研究了基于相空间重构的多步预测神经网络建立方法,构建了基于混沌相空间重构的时间延迟BP神经网络预报模型,并结合工程实例进行了预报试验;
(5)EMD方法能够有效的把非线性数据序列自适应的分解成一系列从高频到低频的分量,信号平稳性能够得到有效提高。基于此本文提出顾及尺度特征的EMD多尺度预报模型,经工程实例验证,该模型具有较高的预测精度;文中还进一步研究和建立了适用于具有混沌非线性特征的监测时序的EMD多尺度分解SVM变形预报模型,并进行了实例分析研究;
(6)把混沌理论及EMD分析技术引入老采空区动态空间信息处理及变形预报领域,重构了老采空区上方地表沉降系统的动力学过程,对兖州某矿某老采空区复垦场地的监测数据分析表明,该老采空区上方地表变形具有混沌特性;本文首次采用EMD分解技术对上述数据进行了分尺度分析研究,实例试验结果证实,EMD分析技术可以作为研究和判定老采空区上方地表稳定性的有效理论方法。
Deformation data analysis and prediction is a complex systematic project. With the development of the new technique of deformation monitoring such as GPS, the new methods of extracting deformation information, predicting and evaluating the stability of deformation body based on introducing advanced mathematical theory and signal analysis methods to deeply understand the non-linear and complexity of deformation.
The main results of this study and the specific contents are as follows:
(1) Introducing the judging method of chaotic behavior of several types of deformation body. Determining the non-linear judgment of deformation monitoring combined with examples of actual projects. Pointing out that the Lyapunov-index can serve as a deformation evaluation criteria in judging different part of dynamic deformation body.
(2) Wavelet transform has a strong ability to remove the noise of the chaotic data, however empirical mode decomposition(EMD)does well in extracting of the information. In this paper, comparing the two methods and pointing out their advantages and disadvantages with the help of simulation. And proposing Wavelet-EMD coupled model to extract the deformation information and remove the noise. The results in real project show that the model is very useful in continuously dynamic GPS deformation monitoring data.
(3)Kalman filter has an original advantage in eliminating the white noise and predicting future systematic state of deformation data. The key research has analyzed the noise reduction capabilities of Kalman filter and EMD. The dynamic GPS data of a surface mining area in Mongolia was processed and analyzed to prove the effectiveness in practical application of the model.
(4) Chaotic time series of deformation body was multi-step predicted and obtained ideal prediction results through weighted local-region model can be gotten, which based on chaotic non-linear analysis method. Multi-steps neural network method was studied by phase space reconstruction and time delay BP neural network prediction model was reconstructed based on chaotic phase space reconstruction method, when chaotic dynamics parameter m is used as the number of input layer modes, and prediction experiment was made with an engineer example.
(5)EMD method can decompose the non-linear data to a series components from the high-frequency to low-frequency, and the signal stationary be significantly improved. The EMD multi-scale prediction model was put forward based on the multi-scale characteristics of non-linear time series, and had high prediction accuracy. The integration deformation prediction model of EMD multi-scale decomposition and SVM was researched and established, which was suitable to process non-linear deformation time series.
(6)The chaotic theory and EMD analysis technology were introduced to process deformation data, make deformation prediction and reconstruct dynamic process of ground surface settlement of old goaf. Studying and analyzing on the reclamation field of old goaf in Yanzhou coal mine show that ground surface deformation of the old goaf has chaotic characteristics. In this paper, EMD technique was first introduced to analyze deformation data. Experimental results show that EMD analysis technology is an effective method to research and judge the stability of the ground surface of old goaf.
引文
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