| 基于混沌时间序列的黄土滑坡变形预测方法及应用 |
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| 引用本文： | 王 利1,2,3,岳 聪4,舒 宝1,2,3,张耀辉1,2,3,许 豪1,2,3,义 琛1,2,3. 基于混沌时间序列的黄土滑坡变形预测方法及应用[J]. 延边大学学报(自然科学版), 2021, 0(5): 917-925. DOI: 10.19814/j.jese.2021.03037 |
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| 作者姓名： | 王 利1 2 3 岳 聪4 舒 宝1 2 3 张耀辉1 2 3 许 豪1 2 3 义 琛1 2 3 |
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| 作者单位： | (1. 长安大学 地质工程与测绘学院,陕西 西安 710054; 2. 地理信息工程国家重点实验室,陕西 西安 710054; 3. 长安大学 西部矿产资源与地质工程教育部重点实验室,陕西 西安 710054; 4. 自然资源部第一大地测量队,陕西 西安 710054) |
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| 摘 要： | 采用GNSS技术进行滑坡变形监测时,由于多路径等观测误差的存在,直接使用GNSS监测结果进行变形预测会影响预测结果的精度。为了探讨GNSS测量误差对变形预测结果的影响程度,考虑到滑坡系统的混沌特性,采用混沌理论对陕西泾阳地区庙店滑坡GNSS变形监测结果抑噪处理前后的时间序列进行了对比分析。首先,采用互信息量法确定监测序列的时间延迟、用改进的虚假邻近点法(Cao算法)确定嵌入维数,获取相空间重构参数; 然后使用最大Lyapunov指数对两种变形监测序列进行混沌特性识别; 最后,分别使用加权一阶局域预测方法、最大Lyapunov指数预测方法和BP神经网络预测方法对滑坡变形监测结果进行预测。结果表明:GNSS滑坡变形监测结果抑噪处理前后的时间序列满足混沌特性,说明滑坡系统具有混沌特性; 在3种混沌时间序列预测方法中,BP神经网络预测方法的效果较好,且该方法预测结果的平均绝对误差(MAE)和平均相对误差(MRE)分别为0.4 mm和11.9%,经过S-变换抑噪处理后,预测结果的平均绝对误差和平均相对误差分别为0.1 mm和4.1%,预测效果有明显改善。
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| 关 键 词： | 黄土滑坡 GNSS 变形预测 相空间重构 S-变换 抑噪 混沌时间序列 |
Chaotic Time Series Based Surface Displacement Prediction Method and Application to Loess Landslides |
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| WANG Li1,2,' target="_blank" rel="external">3,YUE Cong4,SHU Bao1,2,' target="_blank" rel="external">3,ZHANG Yao-hui1,2,' target="_blank" rel="external">3,XU Hao1,2,' target="_blank" rel="external">3,YI Chen1,2,' target="_blank" rel="external">3. Chaotic Time Series Based Surface Displacement Prediction Method and Application to Loess Landslides[J]. Journal of Yanbian University (Natural Science), 2021, 0(5): 917-925. DOI: 10.19814/j.jese.2021.03037 |
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| Authors: | WANG Li1,2,' target=" _blank" rel=" external" >3,YUE Cong4,SHU Bao1,2,' target=" _blank" rel=" external" >3,ZHANG Yao-hui1,2,' target=" _blank" rel=" external" >3,XU Hao1,2,' target=" _blank" rel=" external" >3,YI Chen1,2,' target=" _blank" rel=" external" >3 |
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| Affiliation: | (1. School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, Shaanxi, China; 2. State Key Laboratory of Geo-information Engineering, Xi'an 710054, Shaanxi, China; 3. Key Laboratory of Western China's Mineral Resources and Geological Engineering of Ministry of Education, Chang'an University, Xi'an 710054, Shaanxi, China; 4. The First Geodetic Surveying Brigade of MNR, Xi'an 710054, Shaanxi, China) |
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| Abstract: | Due to the existence of observation noise such as multi-path error, the accuracy of deformation prediction results are affected by using the data series of GNSS deformation monitoring. In order to examine the influence of measurement error on the deformation prediction results, the GNSS derived surface displacement time series of Miaodian landslide in Jingyang Area of Shaanxi, and those after noise suppression in combination with chaos theory were analyzed. Firstly, the mutual information method was used to determine the time delay of the surface displacement time series, and the Cao method was used to determine the embedding dimension to obtain the phase space reconstruction parameters. Secondly, the maximum Lyapunov exponent method was used to identify the chaotic characteristics of the two surface displacement time series. Finally, the weighted first-order local prediction method, the largest Lyapunov exponent prediction method, and the BP neural network prediction method were used to predict the landslide surface displacements. The results show that the GNSS landslide surface displacement time series and the time series after noise suppression have chaotic characteristics. The BP neural network prediction method has good prediction performance with an MAE of 0.4 mm and an MRE of 11.9%. After S-transform noise suppression, the MAE and MRE are 0.1 mm and 4.12%, respectively. Compared with the original time series, the prediction performance has been significantly improved after noise suppression. |
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| Keywords: | loess landslide GNSS deformation prediction phase space reconstruction S-transformation noise suppression chaotic time series |
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