综合地表与深部位移监测数据的滑坡多目标加权位移反分析方法

doi:10.11947/j.AGCS.2022.20210163

测绘学报 ›› 2022, Vol. 51 ›› Issue (10): 2149-2159.doi: 10.11947/j.AGCS.2022.20210163

综合地表与深部位移监测数据的滑坡多目标加权位移反分析方法

戴粤^1,2, 戴吾蛟^1,2, 余文坤^1,2

1. 中南大学测绘与遥感科学系,湖南长沙 410083;
2. 湖南省精密工程测量与形变灾害监测重点实验室,湖南长沙 410083

收稿日期:2021-03-30 修回日期:2021-08-09 发布日期:2022-11-05
通讯作者: 戴吾蛟 E-mail:daiwujiao@163.com
作者简介:戴粤(1997—),男,博士生,研究方向为滑坡监测预警。E-mail:yuedai@csu.edu.cn
基金资助:
国家自然科学基金(41074004;42174053);湖南省自然科学基金(2021JJ30805);中南大学研究生创新项目(206021703)

A landslide multi-objective weighted displacement back analysis method synthesizing ground and underground displacement monitoring data

DAI Yue^1,2, DAI Wujiao^1,2, YU Wenkun^1,2

1. Department of Surveying Engineering & Geo-Informatics, Central South University, Changsha 410083, China;
2. Key Laboratory of Precise Engineering Surveying & Deformation Disaster Monitoring of Hunan Province, Changsha 410083, China

Received:2021-03-30 Revised:2021-08-09 Published:2022-11-05
Supported by:
The National Natural Science Foundation of China(Nos. 41074004;42174053);The Hunan Natural Science Foundation(No. 2021JJ30805);The Innovation Fund Designated for Graduate Students of Central South University(No. 206021703)

摘要/Abstract

摘要： 针对滑坡参数反演存在的多目标优化问题,同时为弥补滑坡深部位移监测点位稀疏的不足,提出了一种综合地表与深部位移监测数据的滑坡多目标加权位移反分析方法。该方法利用地表与深部位移监测数据构建多目标位移反分析模型,采用抗差Helmert方差分量估计法计算各类位移观测量的抗差验后随机模型,优化反演模型的权参数。试验结果表明:深部位移信息量不足会导致位移反分析结果出现严重偏差,综合地表与深部位移信息的反演结果更为准确;基于抗差Helmert方差分量估计的多目标加权位移反分析方法不仅可以合理确定不同类型观测数据的权重,还能够有效抵制异常粗差对反演结果的影响,提高了反演计算精度。

关键词: 滑坡变形监测, 多目标优化问题, 加权位移反分析模型, 抗差Helmert方差分量估计

Abstract: In view of the multi-objective optimization problem of landslide parameter inversion, and to compensate for the lack of sparse landslide displacement monitoring point, a landslide multi-objective weighted displacement back analysis method synthesizing ground and underground displacement monitoring data is proposed. Firstly, the multi-objective weighted displacement back analysis model is constructed by ground and underground displacement information. Secondly, the robust post-test random model of various observations is calculated by the robust Helmert variance component estimation method, and then it is used to optimize the inversion model. Finally, the equivalent mechanical parameters are solved by iteration computation. Experimental results show that insufficient amount of underground displacement information will lead to serious deviations in the displacement back analysis results, and the inversion results that integrate ground and underground displacement information are more accurate; the multi-objective weighted displacement back analysis method based on robust Helmert variance component estimation can not only reasonably determine the weight of different types of observation data, but also effectively resist the influence of abnormal gross errors on the inversion results, and improve the inversion calculation accuracy.

Key words: landslide deformation monitoring, multi-objective optimization problem, weighted displacement back analysis model, robust Helmert variance component estimation

中图分类号:

TU452∶P207

戴粤, 戴吾蛟, 余文坤. 综合地表与深部位移监测数据的滑坡多目标加权位移反分析方法[J]. 测绘学报, 2022, 51(10): 2149-2159.

DAI Yue, DAI Wujiao, YU Wenkun. A landslide multi-objective weighted displacement back analysis method synthesizing ground and underground displacement monitoring data[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(10): 2149-2159.

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综合地表与深部位移监测数据的滑坡多目标加权位移反分析方法

A landslide multi-objective weighted displacement back analysis method synthesizing ground and underground displacement monitoring data

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