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Article(id=1322566870612431402, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322566845215912204, articleNumber=null, orderNo=null, doi=10.14075/j.jgg.2025.03.089, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1742400000000, receivedDateStr=2025-03-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793254114057, onlineDateStr=2026-10-29, pubDate=1771084800000, pubDateStr=2026-02-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793254114057, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793254114057, creator=13701087609, updateTime=1793254114057, updator=13701087609, issue=Issue{id=1322566845215912204, tenantId=1146029695717560320, journalId=1281212831689347082, year='2026', volume='46', issue='2', pageStart='139', pageEnd='261', issueExtLink='null', onlineDate='null', pubDate='1771084800000', pubDateStr='2026-02-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793254108003, creator='13701087609', updateTime=1793254108003, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=148, endPage=154, ext={EN=ArticleExt(id=1322566872332096043, articleId=1322566870612431402, tenantId=1146029695717560320, journalId=1281212831689347082, language=EN, title=Inversion of Progressive Landslide Sliding Surface Parameters with Additional Time-Series InSAR Monitoring of Deformation Constraints, columnId=null, journalTitle=Journal of Geodesy and Geodynamics, columnName=null, runingTitle=null, highlight=null, articleAbstract=

This study integrates the high spatial-resolution monitoring advantages of InSAR with physics-based reduced-order modeling to invert sliding surface parameters. Focusing on the progressive Landslide 5# in Dangchuan, Heifangtai (Gansu province)—a typical loess landslide-prone area—we adapted the fault dislocation model from seismology to develop a novel inversion framework. By incorporating time-series InSAR deformation as surface constraints into the dislocation model, the method progressively inverts sliding surface geometry and position from a geodetic perspective. The results indicate that the sliding surface of the 5# landslide mass is located approximately 4.83±0.3 m beneath the slope surface, with a length of about 136.43±0.5 m, a width of around 47.82±0.3 m, and an aspect of approximately 272°±1.0°. The model inversion achieved a fitting degree of 99%, which is generally consistent with the results of geological surveys and statistical analyses.

, authors=Yanjing HU1, Wei QU1, 2, *, Zhihang WEI1, Rongtang XU1, Jiuyuan LI1, authorsList=Yanjing HU, Wei QU, Zhihang WEI, Rongtang XU, Jiuyuan LI, authorCompany=null, correspAuthors=Wei QU, authorNote=null, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322566874177589820, articleId=1322566870612431402, tenantId=1146029695717560320, journalId=1281212831689347082, language=CN, title=附加时序InSAR监测形变约束的渐进式滑坡滑动面参数反演, columnId=1322566785874908034, journalTitle=大地测量与地球动力学, columnName=特色栏目: 地震大地测量学, runingTitle=null, highlight=null, articleAbstract=

结合InSAR高空间覆盖监测优势与物理简化模型开展滑动面参数的反演研究, 以典型黄土滑坡多发区域甘肃黑方台党川5#渐进式滑坡体为实验区, 借鉴地震学研究领域内利用位错模型反演断层活动参数的思路, 以时序InSAR监测形变作为地表约束, 结合位错模型, 构建一种附加监测形变约束的渐进式滑动面参数反演模型, 从大地测量反演角度确定出党川5#典型滑坡体滑动面位置和几何形态。结果表明, 5#滑坡体滑面位于坡表下约4.83±0.3 m深处, 长度约136.43±0.5 m, 宽度约47.82±0.3 m, 坡向约272°±1.0°, 模型反演拟合度达99%, 与地质调查及统计结果基本吻合。

, authors=胡燕晶1, 瞿伟1, 2, *, 韦志航1, 徐荣堂1, 李久元1, authorsList=胡燕晶, 瞿伟, 韦志航, 徐荣堂, 李久元, authorCompany=null, correspAuthors=瞿伟, authorNote=

胡燕晶, 硕士生, 主要从事滑坡灾害高精度监测与参数反演, E-mail:

, correspAuthorsNote=
瞿伟, 博士, 教授, 博士生导师, 主要从事地壳形变大地测量反演与地质灾害高精度监测及成因机理研究, E-mail:
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胡燕晶, 硕士生, 主要从事滑坡灾害高精度监测与参数反演, E-mail:

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Experimental data

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序号日期升降轨日期升降轨
12020-01-04升轨2020-01-04降轨
22020-01-16升轨2020-01-16降轨
32020-01-28升轨2020-01-28降轨
42020-02-09升轨2020-02-09降轨
52020-02-21升轨2020-02-21降轨
62020-03-04升轨2020-03-04降轨
72020-03-16升轨2020-03-16降轨
82020-03-28升轨2020-03-28降轨
92020-04-09升轨2020-04-09降轨
102020-04-21升轨2020-05-03降轨
112020-05-03升轨2020-05-15降轨
122020-05-15升轨2020-05-27降轨
132020-05-27升轨2020-06-08降轨
142020-06-08升轨2020-06-20降轨
152020-08-07升轨2020-07-02降轨
162020-08-19升轨2020-07-14降轨
172020-08-31升轨2020-07-26降轨
182020-09-12升轨2020-08-07降轨
192020-09-24升轨2020-08-19降轨
202020-10-06升轨2020-08-31降轨
212020-10-18升轨2020-09-12降轨
222020-10-30升轨2020-09-24降轨
232020-11-11升轨2020-10-06降轨
242020-11-23升轨2020-10-18降轨
252020-12-05升轨2020-10-30降轨
262020-12-29升轨2020-11-11降轨
272021-01-10升轨2020-11-23降轨
282021-01-22升轨2020-12-05降轨
292021-02-03升轨2020-12-17降轨
302020-12-29降轨
312021-01-10降轨
322021-01-22降轨
332021-02-03降轨
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实验数据

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序号日期升降轨日期升降轨
12020-01-04升轨2020-01-04降轨
22020-01-16升轨2020-01-16降轨
32020-01-28升轨2020-01-28降轨
42020-02-09升轨2020-02-09降轨
52020-02-21升轨2020-02-21降轨
62020-03-04升轨2020-03-04降轨
72020-03-16升轨2020-03-16降轨
82020-03-28升轨2020-03-28降轨
92020-04-09升轨2020-04-09降轨
102020-04-21升轨2020-05-03降轨
112020-05-03升轨2020-05-15降轨
122020-05-15升轨2020-05-27降轨
132020-05-27升轨2020-06-08降轨
142020-06-08升轨2020-06-20降轨
152020-08-07升轨2020-07-02降轨
162020-08-19升轨2020-07-14降轨
172020-08-31升轨2020-07-26降轨
182020-09-12升轨2020-08-07降轨
192020-09-24升轨2020-08-19降轨
202020-10-06升轨2020-08-31降轨
212020-10-18升轨2020-09-12降轨
222020-10-30升轨2020-09-24降轨
232020-11-11升轨2020-10-06降轨
242020-11-23升轨2020-10-18降轨
252020-12-05升轨2020-10-30降轨
262020-12-29升轨2020-11-11降轨
272021-01-10升轨2020-11-23降轨
282021-01-22升轨2020-12-05降轨
292021-02-03升轨2020-12-17降轨
302020-12-29降轨
312021-01-10降轨
322021-01-22降轨
332021-02-03降轨
), ArticleFig(id=1322566881593119353, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322566870612431402, language=EN, label=Tab. 2, caption=

Optimal fitting parameters for the sliding surface model

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模型参数参数区间范围约束最优拟合参数
长/m[104.15, 197.32]136.43±0.5
宽/m[7.02, 49.91]47.82±0.3
深度/m[3.74, 9.33]4.83±0.3
走向/(°)[267.51, 282.30]272±1.0
X/m[12 800.49, 13 429.69]13 421.71±2.6
Y/m[2 315.75, 2 465.09]2 353.65±1.5
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滑动面模型的最优拟合参数

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模型参数参数区间范围约束最优拟合参数
长/m[104.15, 197.32]136.43±0.5
宽/m[7.02, 49.91]47.82±0.3
深度/m[3.74, 9.33]4.83±0.3
走向/(°)[267.51, 282.30]272±1.0
X/m[12 800.49, 13 429.69]13 421.71±2.6
Y/m[2 315.75, 2 465.09]2 353.65±1.5
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附加时序InSAR监测形变约束的渐进式滑坡滑动面参数反演
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胡燕晶 1 , 瞿伟 1, 2, * , 韦志航 1 , 徐荣堂 1 , 李久元 1
大地测量与地球动力学 | 特色栏目: 地震大地测量学 2026,46(2): 148-154
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大地测量与地球动力学 |特色栏目: 地震大地测量学 2026 , 46 (2) : 148 -154
附加时序InSAR监测形变约束的渐进式滑坡滑动面参数反演
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胡燕晶1 , 瞿伟1, 2, * , 韦志航1, 徐荣堂1, 李久元1
作者信息
  • 1 长安大学地质工程与测绘学院, 西安, 710054
  • 2 西部矿产资源与地质工程教育部重点实验室, 西安, 710054
通讯作者:
瞿伟, 博士, 教授, 博士生导师, 主要从事地壳形变大地测量反演与地质灾害高精度监测及成因机理研究, E-mail:
作者简介:

胡燕晶, 硕士生, 主要从事滑坡灾害高精度监测与参数反演, E-mail:

Inversion of Progressive Landslide Sliding Surface Parameters with Additional Time-Series InSAR Monitoring of Deformation Constraints
Yanjing HU1 , Wei QU1, 2, * , Zhihang WEI1, Rongtang XU1, Jiuyuan LI1
Affiliations
  • 1 College of Geology Engineering and Geomatics, Chang'an University, Xi'an 710054, China
  • 2 Key Laboratory of Western China's Mineral Resources and Geological Engineering, Ministry of Education, Xi'an 710054, China
出版时间: 2026-02-15 doi: 10.14075/j.jgg.2025.03.089
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摘要
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结合InSAR高空间覆盖监测优势与物理简化模型开展滑动面参数的反演研究, 以典型黄土滑坡多发区域甘肃黑方台党川5#渐进式滑坡体为实验区, 借鉴地震学研究领域内利用位错模型反演断层活动参数的思路, 以时序InSAR监测形变作为地表约束, 结合位错模型, 构建一种附加监测形变约束的渐进式滑动面参数反演模型, 从大地测量反演角度确定出党川5#典型滑坡体滑动面位置和几何形态。结果表明, 5#滑坡体滑面位于坡表下约4.83±0.3 m深处, 长度约136.43±0.5 m, 宽度约47.82±0.3 m, 坡向约272°±1.0°, 模型反演拟合度达99%, 与地质调查及统计结果基本吻合。

渐进式滑坡  /  滑动面  /  SBAS-InSAR  /  位错模型  /  参数反演

This study integrates the high spatial-resolution monitoring advantages of InSAR with physics-based reduced-order modeling to invert sliding surface parameters. Focusing on the progressive Landslide 5# in Dangchuan, Heifangtai (Gansu province)—a typical loess landslide-prone area—we adapted the fault dislocation model from seismology to develop a novel inversion framework. By incorporating time-series InSAR deformation as surface constraints into the dislocation model, the method progressively inverts sliding surface geometry and position from a geodetic perspective. The results indicate that the sliding surface of the 5# landslide mass is located approximately 4.83±0.3 m beneath the slope surface, with a length of about 136.43±0.5 m, a width of around 47.82±0.3 m, and an aspect of approximately 272°±1.0°. The model inversion achieved a fitting degree of 99%, which is generally consistent with the results of geological surveys and statistical analyses.

progressive landslide  /  sliding surface  /  SBAS-InSAR  /  dislocation model  /  parameter inversion
胡燕晶, 瞿伟, 韦志航, 徐荣堂, 李久元. 附加时序InSAR监测形变约束的渐进式滑坡滑动面参数反演. 大地测量与地球动力学, 2026 , 46 (2) : 148 -154 . DOI: 10.14075/j.jgg.2025.03.089
Yanjing HU, Wei QU, Zhihang WEI, Rongtang XU, Jiuyuan LI. Inversion of Progressive Landslide Sliding Surface Parameters with Additional Time-Series InSAR Monitoring of Deformation Constraints[J]. Journal of Geodesy and Geodynamics, 2026 , 46 (2) : 148 -154 . DOI: 10.14075/j.jgg.2025.03.089
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滑动面参数的确定对于滑坡稳定性的评价与滑坡防灾预警研究具有重要的参考价值[1-2]。利用接触式技术确定滑动面参数的方法主要有地球物理领域的时域反射测试技术(time domain reflectometry, TDR)与工程地质领域的钻孔测斜技术等[3]。但这些方法可能会受到探测技术本身适应性的限制，也存在铺设钻孔成本高、探测精度较低且难以较好探测出滑动面整体位置与几何形态的问题。利用滑坡外部监测信息反演滑动面参数，是一种研究滑动面参数的非接触式强有力的手段[4-5]，如利用InSAR监测形变场推断滑面深度及其几何形状[6]，基于InSAR监测值确定滑面几何形状和体积的几何方法[7]等。InSAR监测技术可为反演滑动面参数提供有效的地表约束，在反演滑动面几何形状与运动特征方面展现出了极大的潜力[8]。
据此，本文针对我国黄土滑坡灾害最为典型的甘肃黑方台党川近年发生的典型渐进式滑坡事件仍未定量确定滑坡体滑动面位置和几何形态的问题，借鉴地震学研究领域内利用位错模型反演断层活动参数的思路，开展结合InSAR时序监测结果与物理简化模型的滑动面参数反演研究，拟为深入理解区域滑坡成因机制与滑坡灾害预警防灾提供有价值的理论与实际参考。
1 研究区域
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黑方台位于甘肃省临夏州永靖县盐锅峡镇，是我国西部黄土高原最为典型的渐进式滑坡多发区域，被誉为研究滑坡灾害的天然实验场[9-10]。黑方台由黑台与方台共同构成，两者以虎狼沟为界，其中黑台面积较大。黑方台地处陇西中新生代盆地边缘的次级高山带构造单元，地貌组合呈现典型的高原浅山丘陵与黄土地貌复合特征。受黄河水系长期侵蚀切割作用影响，黑方台区域发育多级阶地系统、黄土残塬和基岩山地等3大主导地貌类型。第四系中更新统风积黄土广泛覆盖于新近系泥岩基底之上，形成厚度逾百米的连续堆积层。在构造抬升与流水侵蚀耦合作用下，区内形成以深切河谷为主体、黄土丘陵为过渡带、基岩山岭为边界的复合型盆地地貌格局。在区域地质构造作用下，黑方台形成了独立黄土台塬地貌单元，整体地形平缓且起伏较小，其中黑台台塬呈现东低西高的海拔变化趋势，方台台塬则表现为南低北高的地形特征。近年来在黑方台，尤其是在党川地区发生了多起滑坡事件，如2021-01-27党川5#滑坡垮塌事件等，本文针对此次滑坡事件，结合InSAR时序监测结果与物理简化模型对滑坡滑动面参数进行反演。
2 SAR数据处理与结果分析
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为获取党川5#典型滑坡体形变时间序列，采用SBAS-InSAR技术对黑方台党川地区地表时序形变进行监测。实验数据包含29景升轨与33景降轨Sentinel-1A影像(表 1)，覆盖时段为2020-01-04—2021-02-03。数据处理流程主要包含4个核心环节：1)经过预处理获得单视复数影像，采用研究区域边界进行空间裁剪，并选取2020-01-04影像作为主影像，采用方位向与距离向多视比为10∶2的参数进行多视处理，通过设定升轨96 d、降轨72 d的时间基线阈值及±150 m空间基线阈值(图 2)，构建符合时空基线约束的干涉对组合[11]；2)引入外部DEM数据模拟地形相位，通过差分处理消除地形相位的影响，并结合Goldstein滤波技术，有效提升区域的相干性[12]；3)采用最小费用流法开展相位解缠处理[13]，设定0.3作为解缠阈值以获取真实的形变相位；4)经大气校正与地理编码处理后，最终获取黑方台党川地区地表累积时序形变数据集(图 3、4)。
图 3和4分别为党川段5#滑坡体升降轨时序形变监测结果。可以看出，在影像时间段内党川大部分区域地表较稳定，较明显的形变信号主要集中在5#滑坡体附近，升轨时序形变显示形变主要发生在坡体前缘和中部，年平均形变速率约为-4~40.15 mm/a，最大变化差异为44.15 mm/a，LOS向最大累积变形约为49.92 mm。降轨时序形变显示滑坡体中部形变速率较快，最大形变发生在中部，年平均形变速率约为-61.35~3 mm/a，最大变化差异为64.35 mm/a，LOS向最大累积变形约为-75.25 mm。此外，图 3、4中还识别出了其他滑坡体，但这些滑坡体在监测时段内均未发生垮塌。
升降轨形变差异的原因主要有：1)InSAR技术对地表形变监测特性的差异，由于升降轨雷达卫星在观测方位角、入射角和航向角等参数存在系统性差异，会导致同一地表监测点的三维形变位移场在沿卫星视线方向的几何投影存在差异；2)地形起伏的影响，鉴于SAR卫星特有的侧视斜距投影成像机制，地形起伏会导致升降轨SAR影像中呈现出不同程度的几何畸变，这种系统性偏差也会导致形变结果的差异性。
从图 3、4中可以看出，2020-01-04—08-19党川5#滑坡体处于稳定状态；2020-12-05开始呈现出一定的形变量，且累积形变量级较小(＜20 mm)；2020-12-29—2021-01-22滑坡体进入持续变形阶段，位移速率也呈现出渐进增长的趋势，且滑坡体的变形范围已扩展至坡体后缘，形变量级也显著增加(接近约50 mm)；2021-02-03滑坡体已发生了失稳垮塌。党川5#滑坡体具有的上述形变演化特征，也符合黑方台周缘黄土滑坡体典型的渐进式运动变化特征[14-15]。
由于未能获取到研究区域其他外部监测成果以验证本文时序InSAR监测结果的精度，无法开展监测结果外附合精度的验证。因此，基于双轨道监测数据的特点，根据相同基准条件，通过统计区域公共点差值开展内符合精度的评定。分析升降轨重叠区域形变差值的统计分布(图 5)可知，升降轨时序监测结果的残差呈近似正态分布的特征，形变速率差值的标准差为2.64 mm/a。从图 3和4中也可以看出，研究区域内最大形变速率为61.35 mm/a，而升降轨时序监测结果残差标准差仅为最大形变速率的4.3%(小于10%[16])，上述特征也表明本文利用2个轨道数据得到的形变监测结果具较好的可靠性。
3 基于时序InSAR监测形变约束的滑动面参数反演
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3.1 反演算法
在大地测量反演过程中，观测数据与模型参数的函数关系可表示为[17]：
$\boldsymbol{d}=G(m)+\boldsymbol{\varDelta}$
式中，d表示观测值；m表示模型参数；G表示观测值与模型的格林函数关系；Δ表示观测数据误差。利用蒙特卡罗法对滑动面参数进行反演，目标函数可表示为：
$\min \varphi(m)=\sum\limits_{i=1}^n\left[l_j-G_j(m)\right]^2$
式中，m表示模型参数，待反演的参数分别为滑坡位错滑动面的长度、宽度、深度、倾角，沿滑坡走向位错、沿滑坡倾向位错和张性错动分量；lj表示地表监测数据。
3.2 滑动面参数反演与结果分析
本文分别以党川5#滑坡升降轨时序监测结果作为模型反演的地表约束。InSAR技术提供的是高时空分辨率连续的形变场数据，可有效提升参数反演的空间分辨率，其cm级位移的监测精度可为滑动面几何参数的确定提供有效的约束。此外，基于时序InSAR数据构建的动态形变序列，可完整呈现出滑坡形变的渐进式演化特征，从而使反演参数更符合滑坡体实际力学行为特征。为了提升参数反演效率，需要对时序InSAR监测结果进行降采样处理。本文采用四叉树降采样法，基于二维空间自适应分割机制，预定义形变梯度阈值参数，当窗口形变均值超过该阈值时执行四叉树递归分割操作，直至各子空间形变梯度特征参数满足预设的收敛条件。文中根据升降轨SAR影像数据特征，设定的升降轨数据采样阈值分别为0.0142 m、0.0222 m。
选定研究区左下方稳定区域作为坐标参考点(经度、纬度采用十进制度表示)，通过设定矩形研究区对角顶点坐标确定空间计算域边界。同时，基于野外地质调查数据对滑动面各个参数设置合理的物理量值区间(表 2)。在参数优化过程中，采用基于贝叶斯推断的马尔可夫链-蒙特卡罗(MCMC)算法联合Metropolis-Hastings算法，通过迭代计算实现物理约束条件下的参数空间全局优化。该过程同步完成参数后验概率密度分布估计，最终基于模型模拟值与观测数据的最优拟合准则，确定出滑动面的最佳几何参数组合(表 2)。
在具体反演过程中引入滑面运动拉普拉斯平滑约束条件，对所有子滑块构建平滑约束方程，利用L曲线法确定平滑因子(图 6)。
在参数反演过程中设置104次迭代，最终获取模型最优拟合参数(表 2)并生成参数反演收敛曲线(图 7)。由图 7可以看出，无论是初始阶段参数的快速收敛过程，还是后期参数微调阶段，各个参数均保持着良好的平稳变化趋势，未出现明显的振荡或发散现象，且当参数达到稳定状态后，数值波动幅度也始终维持在0.5%的阈值范围内。上述参数反演的收敛特征，也进一步验证了本文模型对于观测数据集的良好拟合能力。
根据甘肃省地质环境监测院开展的党川地区野外调查统计资料显示，党川地区发育的滑坡平均长度为180 m、平均宽度为50 m，本文反演获得的党川区5#滑坡的长度为136.43±0.5 m、宽度为47.82±0.3 m。魏丽娟[18]对党川地区滑坡体厚度的统计调查结果显示，该区域滑坡的厚度均值为4 m，本文反演得到5#滑坡深度为4.83±0.3 m。杨冬冬[19]总结的党川段滑坡坡向的范围为147°~273°，本文反演获得5#滑坡坡向为272°±1.0°。由上述对比分析可知，本文反演结果与实际地质调查和统计分析结果基本相符。
为进一步评估模型反演参数的可靠性，利用滑动面模型正演计算升降轨形变场，与实际InSAR观测数据(图 8)进行对比分析，并计算模拟与实测RMSE。
$\text { RMSE }=\sqrt{\frac{\sum\limits_{i=1}^n\left(v_i-v_i^{\prime}\right)^2}{n}}$
式中，vi表示InSAR监测形变值；v′i表示模型正演形变值；n表示形变点的个数。
从图 8中可以看出，模型正演出的滑坡地表形变数据与InSAR观测数据的整体分布特征、形变量级基本一致。残差的量级大多集中在-10~10 mm/a范围内，升降轨整体残差较小，其中升轨最大残差为仅为5.6 mm/a，最小残差为-0.9 mm/a，中误差为3.4 mm/a；降轨最大残差为仅为1.6 mm/a，最小残差为-4.3 mm/a，中误差为2.3 mm/a。上述残差量级与时序InSAR监测精度相当，模型拟合度达99%，进一步表明了反演模型的合理性。
在坡表不同位置呈现出的非显著残余分量(图 8(c)、8(f))主要是2个方面因素的综合作用：1)滑坡滑面在实际中会呈现出一定的曲面形态，本文为简化计算模型将其近似为二维平面进行表征，该几何形态的简化处理可能会引入理论模型与真实场景的偏差；2)本文基于弹性介质位错理论构建的滑动面参数反演模型，无法较好地表征出滑坡变形过程中可能发生的塑性变形情况，从而导致参数反演精度可能受到一定的影响。
需要说明的是，InSAR监测精度会受到SAR图像质量、相干性、卫星视角等多种因素的影响，导致反演结果存在一定的不确定性[20]。如本文滑坡区域的监测数据有一定的缺失，这是由于区域形变量过大或植被覆盖导致的失相干现象造成的。此外，需要特别指出的是，党川5#滑坡体滑动面是一个渐进发展的近似圆弧的曲面，本文为了便于建模与计算简便，在形态上将其简化为一个平面，这一几何形态上的简化也可能是造成模型模拟与实测有一定残差的原因(图 8(c)与8(f))。但本文利用InSAR实测结果作为地表约束, 通过构建滑动面简化模型的参数反演方法，可为渐进式滑动面参数反演提供一种新的思路，在后续非接触式滑动面拓展研究中，拟结合更多的数据源(如倾斜仪深部位移、多个北斗/GNSS监测时序位移)。一方面尽可能地增加模型的内部与外部位移约束条件，提高参数反演的可靠性，另一方面尽可能构建出多个连续位错滑动面以更精确地逼近滑动面形状，增强模型的完备性。
4 结语
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本文利用时序InSAR技术获取了我国典型黄土滑坡多发区域甘肃黑方台党川区域5#渐进式滑坡体地表形变，并以此作为地表约束条件，结合物理简化模型开展了滑动面参数的反演研究，从大地测量反演角度确定出了滑动面位置信息与几何形态，获得了如下初步结论：
1) 党川5#滑坡体2020-01-04—2021-02-03升轨最大累积形变量约为49.92 mm，降轨最大累积形变量约为-75.25 mm。升降轨时序InSAR形变结果均较好地揭示出了党川5#滑坡体具有显著渐进式运动变形特征。
2) 党川5#滑坡体滑动面位于坡表下约4.83±0.3 m深处，长度约136.43±0.5 m，宽度约47.82±0.3 m，坡向约272°± 1.0°，模型拟合度达99%，与地质调查和统计结果相符合。
基金
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  • 国家自然科学基金(42174006)
  • 国家自然科学基金(42090055)
  • 陕西省杰出青-科学基金(2022JC-18)
  • 中央高校基本科研业务费专项(CHD300102263201)
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2026年第46卷第2期
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doi: 10.14075/j.jgg.2025.03.089
  • 接收时间：2025-03-20
  • 首发时间：2026-10-29
  • 出版时间：2026-02-15
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  • 收稿日期：2025-03-20
基金
国家自然科学基金(42174006)
国家自然科学基金(42090055)
陕西省杰出青-科学基金(2022JC-18)
中央高校基本科研业务费专项(CHD300102263201)
作者信息
    1 长安大学地质工程与测绘学院, 西安, 710054
    2 西部矿产资源与地质工程教育部重点实验室, 西安, 710054

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瞿伟, 博士, 教授, 博士生导师, 主要从事地壳形变大地测量反演与地质灾害高精度监测及成因机理研究, E-mail:
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https://castjournals.cast.org.cn/joweb/ddcl/CN/10.14075/j.jgg.2025.03.089
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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