«上一篇/Previous Article|本期目录/Table of Contents|下一篇/Next Article»

《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

卷:

第43卷

期数:

2021年第05期

页码:

917-925

栏目:

地球信息科学

出版日期:

2021-09-15

文章信息/Info

Title:

Chaotic Time Series Based Surface Displacement Prediction Method and Application to Loess Landslides

文章编号:

1672-6561(2021)05-0917-09

作者:

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

(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)

Author(s):

WANG Li¹; 2; 3;  YUE Cong⁴;  SHU Bao¹; 2; 3*;  ZHANG Yao-hui¹; 2; 3;  XU Hao¹; 2; 3;  YI Chen¹; 2; 3

(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)

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

Keywords:

loess landslide;  GNSS;  deformation prediction;  phase space reconstruction;  S-transformation;  noise suppression;  chaotic time series

分类号:

P642.22

DOI:

10.19814/j.jese.2021.03037

文献标志码:

A

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。

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.

参考文献/References:

[1] 邓聚龙.灰色系统基本方法[M].武汉:华中理工大学出版社,1987.
DENG Ju-long.The Primary Method of Grey System Theory[M].Wuhan:Huazhong University of Science and Technology Press,1987.
[2] 王朝阳,许 强,范宣梅,等.灰色新陈代谢GM(1,1)模型在滑坡变形预测中的应用[J].水文地质工程地质,2009,36(2):108-111.
WANG Chao-yang,XU Qiang,FAN Xuan-mei,et al.Application of Renewal Gray GM(1,1)Model to Prediction of Landslide Deformation with Two Case Studies[J].Hydrogeology and Engineering Geology,2009,36(2):108-111.
[3] 李晓红,靳晓光,亢会明,等.GM(1,1)优化模型在滑坡预测预报中的应用[J].山地学报,2001,19(3):265-269.
LI Xiao-hong,JIN Xiao-guang,KANG Hui-ming,et al.Application of GM(1,1)Majorized Model to Simulation-forecast of Landslide[J].Journal of Mountain Science,2001,19(3):265-269.
[4] 段功豪,牛瑞卿,赵艳南,等.基于动态指数平滑模型的降雨诱发型滑坡预测[J].武汉大学学报(信息科学版),2016,41(7):958-962.
DUAN Gong-hao,NIU Rui-qing,ZHAO Yan-nan,et al.Rainfall-induced Landslide Prediction Based on Dynamic Exponential Smoothing Model[J].Geoma-tics and Information Science of Wuhan University,2016,41(7):958-962.
[5] 黎锁平,刘坤会.平滑系数自适应的二次指数平滑模型及其应用[J].系统工程理论与实践,2004,24(2):95-99.
LI Suo-ping,LIU Kun-hui.Quadric Exponential Smoothing Model with Adapted Parameter and Its Applications[J].Systems Engineering—Theory & Practice,2004,24(2):95-99.
[6] 尹光志,张卫中,张东明,等.基于指数平滑法与回归分析相结合的滑坡预测[J].岩土力学,2007,28(8):1725-1728.
YIN Guang-zhi,ZHANG Wei-zhong,ZHANG Dong-ming,et al.Forecasting of Landslide Displacement Based on Exponential Smoothing and Nonlinear Regression Analysis[J].Rock and Soil Mechanics,2007,28(8):1725-1728.
[7] 高俊杰.混沌时间序列预测研究及应用[D].上海:上海交通大学,2013.
GAO Jun-jie.Study and Application of Chaotic Time Series Prediction[D].Shanghai:Shanghai Jiao Tong University,2013.
[8] 韩 敏.混沌时间序列预测理论与方法[M].北京:中国水利水电出版社,2007.
HAN Min.Prediction Theory and Method of Chaotic Time Series[M].Beijing:China Water and Power Press,2007.
[9] 李 超.混沌时序黄金期货价格预测研究:基于相空间重构和ARIMA-LSTM混合模型[D].广州:暨南大学,2018.
LI Chao.Research on Prediction of Chaotic Time Series Gold Futures Price:Based on Phase Space and ARIMA-LSTM Hybrid Model[D].Guangzhou:Jinan University,2018.
[10] JOKAR M,SALARIEH H,ALASTY A.On the Exi-stence of Proper Stochastic Markov Models for Statistical Reconstruction and Prediction of Chaotic Time Series[J].Chaos,Solitons and Fractals,2019,123:373-382.
[11] 田中大,李树江,王艳红,等.短期风速时间序列混沌特性分析及预测[J].物理学报,2015,64(3):246-257.
TIAN Zhong-da,LI Shu-jiang,WANG Yan-hong,et al.Chaotic Characteristics Analysis and Prediction of Short-term Wind Speed Time Series[J].Acta Physica Sinica,2015,64(3):246-257.
[12] 韩添祎,赵书健.需求响应下的电力负荷特性分析[J].吉林电力,2019,47(6):32-35.
HAN Tian-yi,ZHAO Shu-jian.Analysis of Power Load Characteristics Under Demand Response[J].Jilin Electric Power,2019,47(6):32-35.
[13] 唐 巍,李殿璞,陈学允.混沌理论及其应用研究[J].电力系统自动化,2000,24(7):67-70.
TANG Wei,LI Dian-pu,CHEN Xue-yun.Chaos Theory and Research on Its Applications[J].Automation of Electric Power Systems,2000,24(7):67-70.
[14] 王红瑞,宋 宇,刘昌明,等.混沌理论及在水科学中的应用与存在的问题[J].水科学进展,2004,15(3):400-407.
WANG Hong-rui,SONG Yu,LIU Chang-ming,et al.Application and Issues of Chaos Theory in Hydroscience[J].Advances in Water Science,2004,15(3):400-407.
[15] 徐 敏,曾光明,谢更新,等.混沌理论在河流溶解氧预测中的应用初探[J].环境科学学报,2003,23(6):776-780.
XU Min,ZENG Guang-ming,XIE Geng-xin,et al.Preliminary Research on the Application of Chaos Theory to Dissolved Oxygen Prediction[J].Acta Scientiae Circumstantiae,2003,23(6):776-780.
[16] 李世玺,孙宪坤,尹 玲,等.一种基于混沌理论和LSTM的GPS高程时间序列预测方法[J].导航定位学报,2020,8(1):65-73.
LI Shi-xi,SUN Xian-kun,YIN Ling,et al.A GPS Height Time Series Prediction Method Based on Chaos Theory and LSTM[J].Journal of Navigation and Positioning,2020,8(1):65-73.
[17] 黄发明,殷坤龙,杨背背,等.基于时间序列分解和多变量混沌模型的滑坡阶跃式位移预测[J].地球科学,2018,43(3):887-898.
HUANG Fa-ming,YIN Kun-long,YANG Bei-bei,et al.Step-like Displacement Prediction of Landslide Based on Time Series Decomposition and Multivariate Chaotic Model[J].Earth Science,2018,43(3):887-898.
[18] 张 英,齐 欢,王小平.新滩滑坡非线性动力学模型方法研究[J].长江科学院院报,2002,19(4):33-35.
ZHANG Ying,QI Huan,WANG Xiao-ping.Research on Nonlinear Dynamic Model Method of Xintan Landslide[J].Journal of Yangtze River Scientific Research Institute,2002,19(4):33-35.
[19] 秦四清.斜坡失稳的突变模型与混沌机制[J].岩石力学与工程学报,2000,19(4):486-492.
QIN Si-qing.Nonlinear Catastrophy Model of Slope Instability and Chaotic Dynamics Mechanism of Slope Evolution Process[J].Chinese Journal of Rock Mechanics and Engineering,2000,19(4):486-492.
[20] 黄志全,樊敬亮,王思敬.混沌时间序列预测的局域法在边坡变形分析中的应用[J].工程地质学报,2005,13(2):252-256.
HUANG Zhi-quan,FAN Jing-liang,WANG Si-jing.A Prediction Method of Chaotic Time Series for Slope Deformation[J].Journal of Engineering Geology,2005,13(2):252-256.
[21] 周 超,殷坤龙,黄发明.混沌序列WA-ELM耦合模型在滑坡位移预测中的应用[J].岩土力学,2015,36(9):2674-2680.
ZHOU Chao,YIN Kun-long,HUANG Fa-ming.Application of the Chaotic Sequence WA-ELM Coupling Model in Landslide Displacement Prediction[J].Rock and Soil Mechanics,2015,36(9):2674-2680.
[22] TAKENS F.Detecting Strange Attractors in Turbulence[M]∥RAND D,YOUNG L S.Dynamical Systems and Turbulence,Warwick 1980.Berlin:Springer,1981:366-381.
[23] 岳 聪,王 利,王智伟,等.基于S-变换的GNSS滑坡变形监测数据信息提取与抑噪[J].大地测量与地球动力学,2020,40(4):335-339.
YUE Cong,WANG Li,WANG Zhi-wei,et al.Information Extraction and Noise Suppression of GNSS Landslide Deformation Monitoring Data Based on S-transformation[J].Journal of Geodesy and Geodynamics,2020,40(4):335-339.
[24] 吕金虎.混沌时间序列分析及其应用[M].武汉:武汉大学出版社,2002.
LV Jin-hu.Chaotic Time Series Analysis and Its Application[M].Wuhan:Wuhan University Press,2002.
[25] 泥立丽,张艳兰.基于G-P法和Cao法的桥梁变形时间序列最佳嵌入维数的确定[J].北方交通,2017(5):5-7.
NI Li-li,ZHANG Yan-lan.The Determination of the Best Embedding Dimension of Bridge Deformation Time Series Based on G-P Method and Cao Method[J].Northern Communications,2017(5):5-7.
[26] 田 旦,许才军,鲁铁定.基于Lyapunov指数与神经网络融合的预测模型研究[J].测绘通报,2008(10):17-19,29.
TIAN Dan,XU Cai-jun,LU Tie-ding.Research on Lyapunov Exponent and Artificial Neural Network Fusion Prediction Model[J].Bulletin of Surveying and Mapping,2008(10):17-19,29.

相似文献/References:

[1]李同录,徐 平,李 萍.一种用于非饱和黄土钻探的双管螺旋钻[J].地球科学与环境学报,2009,31(02):185.
　LI Tong-lu,XU Ping,LI Ping.A Dual Tube Auger for Unsaturated Loess Drilling[J].Journal of Earth Sciences and Environment,2009,31(05):185.
[2]郑亚萌,戴福初.灌溉淋滤对原状黄土物理力学性质的影响[J].地球科学与环境学报,2017,39(04):575.
　ZHENG Ya-meng,DAI Fu-chu.Effect of Irrigation Leaching on Physical and Mechanical Properties of Undisturbed Loess[J].Journal of Earth Sciences and Environment,2017,39(05):575.
[3]孟振江,曹一迪,康尘云,等.降雨促发黄土滑坡的启动机制模拟[J].地球科学与环境学报,2023,45(03):474.[doi:10.19814/j.jese.2022.11004]
　MENG Zhen-jiang,CAO Yi-di,KANG Chen-yun,et al.Simulation of the Initiation Mechanism of Loess Landslide Promoted by Rainfall[J].Journal of Earth Sciences and Environment,2023,45(05):474.[doi:10.19814/j.jese.2022.11004]
[4]王 利,张懿恺,舒 宝*,等.基于特征优选和逐步回归的黄土滑坡监测数据融合改进方法[J].地球科学与环境学报,2023,45(03):511.[doi:10.19814/j.jese.2022.09064]
　WANG Li,ZHANG Yi-kai,SHU Bao*,et al.Improved Method for Fusion of Loess Landslide Monitoring Data Based on Feature Selection and Stepwise Regression[J].Journal of Earth Sciences and Environment,2023,45(05):511.[doi:10.19814/j.jese.2022.09064]
[5]瞿 伟,刘祥斌,李久元,等.改进哈里斯鹰优化算法与BP神经网络组合的滑坡位移高精度预测模型[J].地球科学与环境学报,2023,45(03):522.[doi:10.19814/j.jese.2022.11062]
　QU Wei,LIU Xiang-bin,LI Jiu-yuan,et al.High-precision Landslide Displacement Prediction Model Based on IHHO Algorithm Combined with BP Neural Network[J].Journal of Earth Sciences and Environment,2023,45(05):522.[doi:10.19814/j.jese.2022.11062]

备注/Memo

备注/Memo:

收稿日期:2021-03-24; 修回日期:2021-06-17投稿网址:http:∥jese.chd.edu.cn/
基金项目:国家自然科学基金项目(41877289,41731066,41604001,42004024); 国家重点研发计划项目(2018YFC1504805,2018YFC1505102); 中国博士后科学基金项目(2020M673321)
作者简介:王 利(1975-),男,新疆奇台人,教授,博士研究生导师,工学博士,E-mail:wangli@chd.edu.cn。
*通讯作者:舒 宝(1990-),男,湖北随州人,讲师,工学博士,E-mail:baos613@163.com。

常用功能

更新日期/Last Update: 2021-09-30