基于保序回归的滑坡变形与诱发因素关联分析*

doi:10.16265/j.cnki.issn1003-3033.2026.03.1209

中国安全科学学报 ›› 2026, Vol. 36 ›› Issue (3): 121-129.doi: 10.16265/j.cnki.issn1003-3033.2026.03.1209

基于保序回归的滑坡变形与诱发因素关联分析^*

叶霄¹^,²(), 沈琳轩², 余以强³, 陈笑予², 詹伟³^,^**(), 朱鸿鹄⁴

¹ 南京信息工程大学公共安全治理研究院, 江苏南京 210044
² 南京信息工程大学应急管理学院, 江苏南京 210044
³ 浙江省交通运输科学研究院浙江省道桥检测与养护技术研究重点实验室, 浙江杭州 310023
⁴ 南京大学地球工程与科学学院, 江苏南京 210023

收稿日期:2025-09-30 修回日期:2025-12-05 出版日期:2026-03-31
通信作者:
^** 詹伟(1983—),男,浙江杭州人,博士,教授级高级工程师,主要从事边坡灾害监测预警技术等方面的研究。E-mail: zhanwei@zju.edu.cn。

作者简介:

叶霄 (1993—),男,江苏南京人,博士,副教授,主要从事滑坡灾变演化机理与智能感知预警、极端气候工程地质界面效应等方面的研究。E-mail: yexiao@nuist.edu.cn。

余以强,高级工程师。

朱鸿鹄,教授。

基金资助:
国家自然科学基金资助(42507243); 浙江省道桥检测与养护技术研究重点实验室开放基金资助(202501Z); 南京信息工程大学人才启动经费项目(2025r064)

Linking landslide deformation to triggering factors via isotonic constraints of displacement measurements

YE Xiao¹^,²(), SHEN Linxuan², YU Yiqiang³, ZHAN Wei², ZHU Honghu³^,^**()

¹ Institute of Public Safety Governance, Nanjing University of Information Science & Technology, Nanjing Jiangsu 210044, China
² School of Emergency Management, Nanjing University of Information Science & Technology, Nanjing Jiangsu 210044, China
³ Key Laboratory of Road and Bridge Detection and Maintenance Technology in Zhejiang Province, Zhejiang Scientific Research Institute of Transport, Hangzhou Zhejiang 310023, China
⁴ School of Earth Sciences and Engineering, Nanjing University, Nanjing Jiangsu 210023, China

Received:2025-09-30 Revised:2025-12-05 Published:2026-03-31

摘要/Abstract

摘要：

为精准判识包含大量噪声数据的滑坡变形特征及诱发因素,提出一种基于保序回归的位移监测数据降噪处理方法,构建考虑时间滞后效应下不同分区变形与多级水文气象条件的关联规则。以重庆市奉节县渣口石滑坡为例,对比分析保序回归处理前后的位移监测数据,初步探讨不同位置的滑坡变形特征,计算各监测点位移相对降雨量与库水位高程的滞后时间,挖掘不同分区的滑坡变形与水文气象因子关联规则,明确该滑坡的长期变形特征及其诱发因素。结果表明:保序回归算法能够有效剔除非物理噪声、保留本征信号,显著提升数据质量;渣口石滑坡变形呈现显著空间异质性,前缘受库水下降与降雨共同控制变形最大,后缘受地形增强的降雨补给影响次之;库水位骤降(>0.5 m/d)与强降雨(>30 mm/d)协同作用,产生指向坡外的渗透力并削弱基质吸力,是该滑坡失稳的主要触发机制。

关键词: 全球卫星导航系统(GNSS)位移, 保序回归, 滑坡变形, 诱发因素, 关联分析

Abstract:

To accurately identify landslide deformation characteristics and triggering factors from monitoring data containing excessive noise, this study proposed a noise-reduction method for displacement monitoring data based on isotonic regression. It further established correlation rules linking deformation in different subzones to multi-level hydrometeorological conditions, incorporating time-lag effects. Using the Zhakoushi landslide in Fengjie County, Chongqing as a case study, displacement monitoring data before and after isotonic regression processing were comparatively analyzed to preliminarily investigate deformation patterns at different locations. The time delays between displacement at each monitoring station and rainfall and elevation of reservoir water level were calculated, enabling the extraction of association rules between deformation in these subzones and hydrometeorological factors, thus clarifying long-term deformation characteristics and its triggering mechanism of the landslide. The results demonstrate that the isotonic regression algorithm effectively removes non-physical noise while preserving intrinsic deformation information, considerably enhancing data quality. The landslide movements exhibit significant spatial heterogeneity, with the front part experiencing the most extensive deformation controlled jointly by reservoir drawdown and rainfall, followed by the rear part influenced by topography-enhanced rainfall recharge. The synergistic effect of rapid drawdown of reservoir water (>0.5 m/d) and intense rainfall (>30 mm/d), which generate an outward-directed seepage force and reduce matrix suction, is the primary triggering mechanism for the landslide.

Key words: global navigation satellite system (GNSS) displacement, isotonic regression, landslide deformation, triggering factor, association analysis

中图分类号:

X935

叶霄, 沈琳轩, 余以强, 陈笑予, 詹伟, 朱鸿鹄. 基于保序回归的滑坡变形与诱发因素关联分析^*[J]. 中国安全科学学报, 2026, 36(3): 121-129.

YE Xiao, SHEN Linxuan, YU Yiqiang, ZHAN Wei, ZHU Honghu. Linking landslide deformation to triggering factors via isotonic constraints of displacement measurements[J]. China Safety Science Journal, 2026, 36(3): 121-129.

图/表 9

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序号	T_R/d	PCC_R	NMI_R	T_RWL/d	PCC_RWL	NMI_RWL
G1	4	0.21	0.20	33	-0.33	-0.33
G2	12	0.35	0.34	49	-0.20	-0.20
G3	13	0.32	0.32	40	-0.21	-0.28
G4	18	0.33	0.22	64	-0.21	-0.17
G5	11	0.32	0.32	47	-0.22	-0.21
G6	2	0.30	0.30	26	-0.28	-0.28

规则	前项	后项	支持度	置信度
1	活跃态	大雨、慢速下降	0.01	1.00
2	活跃态	高库水位、慢速下降	0.01	1.00
3	活跃态	大雨、高库水位	0.01	1.00
4	活跃态	慢速下降	0.01	1.00
5	缓动态	高库水位、小幅波动	0.01	0.71
6	稳定态	小幅波动	0.80	0.80
7	稳定态	小雨	0.91	0.92

规则	前项	后项	支持度	置信度
1	缓动态	大雨	0.01	0.73
2	波动态	小雨	0.01	0.80
3	波动态	小雨、小幅波动	0.01	0.60
4	稳定态	小雨	0.90	0.91
5	稳定态	小幅波动、小雨	0.73	0.74

规则	前项	后项	支持度	置信度
1	活跃态	小幅波动	0.01	1.00
2	活跃态	大雨、高库水位	0.01	0.5
3	活跃态	暴雨	0.01	0.5
4	活跃态	高库水位	0.01	1.00
5	缓动态	小幅波动	0.02	0.82
6	稳定态	小幅波动	0.79	0.80
7	稳定态	小雨	0.90	0.92

基于保序回归的滑坡变形与诱发因素关联分析^*

Linking landslide deformation to triggering factors via isotonic constraints of displacement measurements

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