基于数据融合的梯田型黄土滑坡隐患监测预警研究

毛正君, 王木楠, 马旭, 仲佳鑫, 张瑾鸽

毛正君, 王木楠, 马旭, 仲佳鑫, 张瑾鸽. 基于数据融合的梯田型黄土滑坡隐患监测预警研究[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20240129
引用本文: 毛正君, 王木楠, 马旭, 仲佳鑫, 张瑾鸽. 基于数据融合的梯田型黄土滑坡隐患监测预警研究[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20240129
MAO Zhengjun, WANG Munan, MA Xu, ZHONG Jiaxin, ZHANG Jinge. Research on Monitoring and Warning of Terraced Loess Potential Landslide Based on Data Fusion[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20240129
Citation: MAO Zhengjun, WANG Munan, MA Xu, ZHONG Jiaxin, ZHANG Jinge. Research on Monitoring and Warning of Terraced Loess Potential Landslide Based on Data Fusion[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20240129

基于数据融合的梯田型黄土滑坡隐患监测预警研究

基金项目: 

陕西省重点研发计划项目(2020SF-379)

宁夏回族自治区重点研发计划项目(2022BEG03059,2023BEG02072)

详细信息
    作者简介:

    毛正君,博士,副教授,研究方向为地质环境保护与国土空间生态修复。mzj@xust.edu.cn

Research on Monitoring and Warning of Terraced Loess Potential Landslide Based on Data Fusion

  • 摘要: 中国灾难型滑坡灾害频发,严重威胁人类生命财产安全,基于数据融合分析多源异构的滑坡隐患监测数据并提出预警判据,能够有效规避风险,减少经济损失和人员伤亡。本文以挂马沟梯田型黄土滑坡隐患为例,获取全球导航卫星系统(Global Navigation Satellite Systems,GNSS)地表位移、裂缝计位移以及降雨量监测数据,采用粗差剔除、数据插补和数据平滑预处理监测数据,然后在预处理的基础上进行了数据级、特征级、决策级数据融合及其效果评价,最终提出了梯田型黄土滑坡隐患预警判据及其分级。结果表明,数据预处理不仅显著提高了监测数据的质量,同时极大地增强了预警系统的准确性和可靠性;梯田型黄土滑坡隐患的位移-时间曲线呈现收敛型特征,即随着时间的推移,累积位移呈现出先快速增加、后慢速增长直至趋于稳定的状态,其变形速度最终趋近于“0”;数据融合能够准确捕捉梯田型黄土滑坡隐患的变形特征,且随着数据融合层次的提升,预测评价的误差呈递减趋势;切线角、累积加速度、降雨强度和裂缝分期配套特征,可作为梯田型黄土滑坡隐患的预警判据。
    Abstract: Objectives: The frequent occurrence of catastrophic landslide disasters in China seriously threatens the safety of human life and property. Based on data fusion, multi-source heterogeneous landslide hazard monitoring data are analyzed and early warning criteria are proposed, which can effectively avoid risks and reduce economic losses and casualties. Methods: We use the hidden danger of Guamagou terraced loess landslide as an example, the monitoring data of Global Navigation Satellite Systems (GNSS) surface displacement, crack meter displacement and rainfall are obtained. The monitoring data are preprocessed by gross error elimination, data interpolation and data smoothing. Then, on the basis of preprocessing, the data fusion and effect evaluation of data level, feature level and decision level are carried out. Finally, the early warning criterion and classification of hidden danger of terraced loess landslide are put forward. Results: The results show that: Data preprocessing not only significantly improves the quality of monitoring data, but also greatly enhances the accuracy and reliability of the early warning system; The displacement-time curve of the hidden danger of loess landslide in terrace type shows the characteristics of convergence, that is, with the passage of time, the cumulative displacement shows a state of rapid increase first, then slow growth until it tends to be stable, and its deformation rate finally tends to'0'; Data fusion can accurately capture the deformation characteristics of hidden dangers of terraced loess landslides, and the error of prediction and evaluation decreases with the improvement of data fusion level; The tangent angle, cumulative acceleration, rainfall intensity and fracture stage matching characteristics can be used as early warning criteria for hidden dangers of terraced loess landslides. Conclusions: The monitoring and early warning of the hidden danger of terraced loess landslide based on data fusion will provide theoretical and scientific basis for further promoting slope modification projects and protecting the existing terraces, as well as for increasing farmers' income, providing scientific and technological services for "three farmers" and promoting rural revitalization.
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出版历程
  • 收稿日期:  2024-10-27

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