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Article(id=1322940705928249693, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322940449979244751, articleNumber=null, orderNo=null, doi=10.14075/j.jgg.2024.04.178, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1713110400000, receivedDateStr=2024-04-15, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793343243343, onlineDateStr=2026-10-30, pubDate=1741968000000, pubDateStr=2025-03-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793343243343, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793343243343, creator=13701087609, updateTime=1793343243343, updator=13701087609, issue=Issue{id=1322940449979244751, tenantId=1146029695717560320, journalId=1281212831689347082, year='2025', volume='45', issue='3', pageStart='221', pageEnd='330', issueExtLink='null', onlineDate='null', pubDate='1741968000000', pubDateStr='2025-03-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793343182320, creator='13701087609', updateTime=1793343182320, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=231, endPage=238, ext={EN=ArticleExt(id=1322940708876845406, articleId=1322940705928249693, tenantId=1146029695717560320, journalId=1281212831689347082, language=EN, title=Landslide Susceptibility Evaluation by Integrating InSAR and Machine Learning, columnId=null, journalTitle=Journal of Geodesy and Geodynamics, columnName=null, runingTitle=null, highlight=null, articleAbstract=

We comprehensively use InSAR and machine learning technology to evaluate landslide susceptibility in Jinyang county, Sichuan province. The data set is updated by interpreting landslides. Based on 12 evaluation factors, we use three models including random forest(RF), support vector machine(SVM) and extreme gradient boosting(XGBoost) for training in Python environment to complete landslide susceptibility mapping. We use ROC curve to verify prediction performance. We optimize the negative samples, obtain the landslide susceptibility evaluation results after sample optimization using machine learning and update the landslide susceptibility results using surface LOS deformation rate. The results show that the three machine learning models have good zoning effect, and the mapping effect of XGBoost model is best. The accuracy of XGBoost model after sample optimization is highest, and the AUC value reaches 0.95. The surface deformation rate obtained by SBAS-InSAR can reduce zoning errors and give timeliness to landslide susceptibility evaluation.

, authors=Ying JIA1, Caiyan WU1, *, Lijuan WANG2, Xinhan YING1, Qi MENG1, Yi YUAN3, Jun LIAO2, Shiqian MA1, authorsList=Ying JIA, Caiyan WU, Lijuan WANG, Xinhan YING, Qi MENG, Yi YUAN, Jun LIAO, Shiqian MA, authorCompany=null, correspAuthors=Caiyan WU, authorNote=

JIA Ying, postgraduate, majors in the application of 3S technology in geological hazard, E-mail:

, 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=1322940744536818080, articleId=1322940705928249693, tenantId=1146029695717560320, journalId=1281212831689347082, language=CN, title=融合InSAR与机器学习的滑坡易发性评价, columnId=1322506197874356701, journalTitle=大地测量与地球动力学, columnName=地壳运动, runingTitle=null, highlight=null, articleAbstract=

综合运用InSAR和机器学习技术，对四川省金阳县进行滑坡易发性评价。通过解译滑坡更新数据集，基于12个评价因子，在Python环境下使用随机森林(RF)、支持向量机(SVM)和极端梯度提升(XGBoost)3种模型进行训练，完成滑坡易发性制图，并采用ROC曲线等验证模型预测性能。对负样本进行优化，使用机器学习得到样本优化后的滑坡易发性评价结果，并利用地表LOS向形变速率对其进行更新。结果表明，3种机器学习模型均具有较好的分区效果，XGBoost模型制图效果最佳，样本优化后模型精度最高，AUC值达到0.95。通过SBAS-InSAR技术获取地表形变速率可以减少分区错误，同时赋予滑坡易发性评价结果时效性。

, authors=贾应1, 吴彩燕1, *, 王立娟2, 应欣翰1, 蒙齐1, 袁怡3, 廖军2, 马世乾1, authorsList=贾应, 吴彩燕, 王立娟, 应欣翰, 蒙齐, 袁怡, 廖军, 马世乾, authorCompany=null, correspAuthors=吴彩燕, authorNote=

贾应，硕士生，主要从事3S技术在地质灾害中的应用研究，E-mail：

, correspAuthorsNote=

吴彩燕，博士，教授，主要从事GIS与地质灾害研究，E-mail:

WU Caiyan, PhD, professor, majorsin GIS and geological hazard, E-mail:

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Data sources

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数据名称 数据来源 类型 时间 网址/备注
Sentinel-1A ASF Data Search 图像 2022-01—2023-12 https://search.asf.alaska.edu/
高分卫星影像 高分四川中心 图像 2021—2023 线下申请
DEM、土地利用、NDVI 地理空间数据云 栅格 2023 https://www.gscloud.cn/
地质图、断层 国家地质资料数据中心 矢量 2022 http://dc.ngac.org.cn/scientific
道路、水系 全国地理信息资源目录服务系统 矢量 2019 https://www.webmap.cn/
灾害点 县灾害普查/县应急局滑坡编录 矢量 2022-06 线下获取
), ArticleFig(id=1322940789055160885, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322940705928249693, language=CN, label=表1, caption=

数据源

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数据名称 数据来源 类型 时间 网址/备注
Sentinel-1A ASF Data Search 图像 2022-01—2023-12 https://search.asf.alaska.edu/
高分卫星影像 高分四川中心 图像 2021—2023 线下申请
DEM、土地利用、NDVI 地理空间数据云 栅格 2023 https://www.gscloud.cn/
地质图、断层 国家地质资料数据中心 矢量 2022 http://dc.ngac.org.cn/scientific
道路、水系 全国地理信息资源目录服务系统 矢量 2019 https://www.webmap.cn/
灾害点 县灾害普查/县应急局滑坡编录 矢量 2022-06 线下获取
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Landslide susceptibility grading results of each model

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模型 等级 面积/km2 滑坡点个数 滑坡点密度 SCAI值
RF 极低 617.69 5 0.01 11.58
低 324.39 1 0.01 30.41
中 282.16 6 0.02 4.41
高 280.61 53 0.19 0.51
极高 223.25 97 0.43 0.22
SVM 极低 896.95 2 0.01 42.04
低 173.44 9 0.05 1.81
中 138.63 10 0.07 1.30
高 143.43 62 0.43 0.22
极高 375.65 79 0.21 0.45
XGBoost 极低 663.23 5 0.01 12.44
低 288.29 2 0.01 13.51
中 240.84 10 0.04 2.21
高 229.74 29 0.12 0.73
极高 306.01 115 0.37 0.25
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各模型滑坡易发性分级结果

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模型 等级 面积/km2 滑坡点个数 滑坡点密度 SCAI值
RF 极低 617.69 5 0.01 11.58
低 324.39 1 0.01 30.41
中 282.16 6 0.02 4.41
高 280.61 53 0.19 0.51
极高 223.25 97 0.43 0.22
SVM 极低 896.95 2 0.01 42.04
低 173.44 9 0.05 1.81
中 138.63 10 0.07 1.30
高 143.43 62 0.43 0.22
极高 375.65 79 0.21 0.45
XGBoost 极低 663.23 5 0.01 12.44
低 288.29 2 0.01 13.51
中 240.84 10 0.04 2.21
高 229.74 29 0.12 0.73
极高 306.01 115 0.37 0.25
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Accuracy evaluation of each model

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模型 准确率 精确率 召回率 F1分数 AUC
RF 0.885 0.878 0.896 0.887 0.90
SVM 0.844 0.837 0.854 0.845 0.86
XGBoost 0.895 0.895 0.896 0.899 0.92
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模型精度评价

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模型 准确率 精确率 召回率 F1分数 AUC
RF 0.885 0.878 0.896 0.887 0.90
SVM 0.844 0.837 0.854 0.845 0.86
XGBoost 0.895 0.895 0.896 0.899 0.92
), ArticleFig(id=1322940792758731327, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322940705928249693, language=EN, label=Tab. 4, caption=

Landslide susceptibility grading results after sample optimization

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模型 等级 面积/km2 滑坡点个数 滑坡点密度 SCAI值
样本优化
XGBoost		 极低 642.44 2 0.01 29.71
低 262.14 6 0.02 4.04
中 223.01 8 0.04 2.58
高 186.15 21 0.11 0.82
极高 407.60 125 0.31 0.30
), ArticleFig(id=1322940793207521857, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322940705928249693, language=CN, label=表4, caption=

样本优化后滑坡易发性分级结果

, figureFileSmall=null, figureFileBig=null, tableContent=
模型 等级 面积/km2 滑坡点个数 滑坡点密度 SCAI值
样本优化
XGBoost		 极低 642.44 2 0.01 29.71
低 262.14 6 0.02 4.04
中 223.01 8 0.04 2.58
高 186.15 21 0.11 0.82
极高 407.60 125 0.31 0.30
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Accuracy evaluation of each model

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模型 准确率 精确率 召回率 F1分数 AUC
样本优化
XGBoost		 0.906 0.898 0.916 0.907 0.95
), ArticleFig(id=1322940794121880132, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322940705928249693, language=CN, label=表5, caption=

模型精度评价

, figureFileSmall=null, figureFileBig=null, tableContent=
模型 准确率 精确率 召回率 F1分数 AUC
样本优化
XGBoost		 0.906 0.898 0.916 0.907 0.95
), ArticleFig(id=1322940798232298057, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322940705928249693, language=EN, label=Tab. 6, caption=

Optimized matrix

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易发性等级 形变速率/mm·a-1
|10~20| |20~30| |30~40| |40~50| |＞50|
1 0 +1 +2 +3 +4
2 0 0 +1 +2 +3
3 0 0 0 +1 +2
4 0 0 0 0 +1
5 0 0 0 0 0
), ArticleFig(id=1322940798634951244, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322940705928249693, language=CN, label=表6, caption=

优化矩阵

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易发性等级 形变速率/mm·a-1
|10~20| |20~30| |30~40| |40~50| |＞50|
1 0 +1 +2 +3 +4
2 0 0 +1 +2 +3
3 0 0 0 +1 +2
4 0 0 0 0 +1
5 0 0 0 0 0
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贾应 1 , 吴彩燕 1, * , 王立娟 2 , 应欣翰 1 , 蒙齐 1 , 袁怡 3 , 廖军 2 , 马世乾 1
大地测量与地球动力学 | 地壳运动 2025,45(3): 231-238
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大地测量与地球动力学 |地壳运动 2025 , 45 (3) : 231 -238
融合InSAR与机器学习的滑坡易发性评价
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贾应1 , 吴彩燕1, * , 王立娟2, 应欣翰1, 蒙齐1, 袁怡3, 廖军2, 马世乾1
作者信息
  • 1 西南科技大学环境与资源学院, 四川省绵阳市青龙大道中段59号, 621010
  • 2 四川省安全科学技术研究院, 成都市武科西四路18号, 610045
  • 3 西南科技大学土木工程与建筑学院, 四川省绵阳市青龙大道中段59号, 621010
通讯作者:

吴彩燕，博士，教授，主要从事GIS与地质灾害研究，E-mail:

WU Caiyan, PhD, professor, majorsin GIS and geological hazard, E-mail:

作者简介:

贾应，硕士生，主要从事3S技术在地质灾害中的应用研究，E-mail：

Landslide Susceptibility Evaluation by Integrating InSAR and Machine Learning
Ying JIA1 , Caiyan WU1, * , Lijuan WANG2, Xinhan YING1, Qi MENG1, Yi YUAN3, Jun LIAO2, Shiqian MA1
Affiliations
  • 1 School of Environment and Resource, Southwest University of Science and Technology, 59 Mid-Qinglong Road, Mianyang 621010, China
  • 2 Sichuan Academy of Safety Science and Technology, 18 Fourth West-Wuke Road, Chengdu 610045, China
  • 3 School of Civil Engineering and Architecture, Southwest University of Science and Technology, 59 Mid-Qinglong Road, Mianyang 621010, China

    • JIA Ying, postgraduate, majors in the application of 3S technology in geological hazard, E-mail:

出版时间: 2025-03-15 doi: 10.14075/j.jgg.2024.04.178
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摘要
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综合运用InSAR和机器学习技术，对四川省金阳县进行滑坡易发性评价。通过解译滑坡更新数据集，基于12个评价因子，在Python环境下使用随机森林(RF)、支持向量机(SVM)和极端梯度提升(XGBoost)3种模型进行训练，完成滑坡易发性制图，并采用ROC曲线等验证模型预测性能。对负样本进行优化，使用机器学习得到样本优化后的滑坡易发性评价结果，并利用地表LOS向形变速率对其进行更新。结果表明，3种机器学习模型均具有较好的分区效果，XGBoost模型制图效果最佳，样本优化后模型精度最高，AUC值达到0.95。通过SBAS-InSAR技术获取地表形变速率可以减少分区错误，同时赋予滑坡易发性评价结果时效性。

滑坡易发性评价  /  SBAS-InSAR  /  机器学习  /  样本优化

We comprehensively use InSAR and machine learning technology to evaluate landslide susceptibility in Jinyang county, Sichuan province. The data set is updated by interpreting landslides. Based on 12 evaluation factors, we use three models including random forest(RF), support vector machine(SVM) and extreme gradient boosting(XGBoost) for training in Python environment to complete landslide susceptibility mapping. We use ROC curve to verify prediction performance. We optimize the negative samples, obtain the landslide susceptibility evaluation results after sample optimization using machine learning and update the landslide susceptibility results using surface LOS deformation rate. The results show that the three machine learning models have good zoning effect, and the mapping effect of XGBoost model is best. The accuracy of XGBoost model after sample optimization is highest, and the AUC value reaches 0.95. The surface deformation rate obtained by SBAS-InSAR can reduce zoning errors and give timeliness to landslide susceptibility evaluation.

landslide susceptibility evaluation  /  SBAS-InSAR  /  machine learning  /  sample optimization
贾应, 吴彩燕, 王立娟, 应欣翰, 蒙齐, 袁怡, 廖军, 马世乾. 融合InSAR与机器学习的滑坡易发性评价. 大地测量与地球动力学, 2025 , 45 (3) : 231 -238 . DOI: 10.14075/j.jgg.2024.04.178
Ying JIA, Caiyan WU, Lijuan WANG, Xinhan YING, Qi MENG, Yi YUAN, Jun LIAO, Shiqian MA. Landslide Susceptibility Evaluation by Integrating InSAR and Machine Learning[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (3) : 231 -238 . DOI: 10.14075/j.jgg.2024.04.178
正文
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滑坡易发性评价的核心是构建反映滑坡与其地质环境关系的预测模型，包括基于专家经验的定性分析和基于数据驱动的定量分析。当前主流方法是定量分析中的机器学习和深度学习，其中机器学习模型因能处理复杂数据以及提供更高的预测准确度而被广泛采用。林荣福等[1]采用随机森林对信息量模型进行赋权，以提高信息量模型精度；周天游等[2]利用多种机器学习模型对九寨沟县进行滑坡易发性评价，并对比不同模型的性能。在以往基于机器学习的滑坡易发性研究中，常会忽略时间因素的影响，但随时间不断变化的地质环境和气候条件可能直接引起滑坡。合成孔径雷达干涉测量(InSAR)技术作为一种先进的时间序列地面观测方法，已经迅速发展成为对地形变监测的重要手段[3]，可适用于滑坡监测识别。张林梵[4]利用时序InSAR技术对白鹿塬西南区进行监测，在黄土滑坡及隐患快速识别方面取得较好效果。
以往滑坡易发性评价未考虑时间因素的影响，评价结果缺乏时效性，且结合光学遥感影像和仅依赖InSAR技术获取的形变数据解译滑坡也具有一定局限性，特别是在植被茂密区，InSAR失相干区域大，光学遥感影像存在一定盲区，工作效率和识别精度有待提高，对于大范围的灾害识别更为困难[5]。
本文将InSAR技术得到的形变数据与基于滑坡孕灾条件的易发性评价模型相结合，以此来更新滑坡易发性评价结果，使得传统的滑坡易发性评价具有实时更新的能力，能够提供动态的易发性区划图，从而实现对金阳县滑坡易发性的动态监测。
1 研究区概况与数据源
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1.1 研究区概况
金阳县隶属四川省凉山彝族自治州，位于102°56′30″~103°30′20″E、27°22′10″~27°57′40″N，最高海拔4 076 m，最低海拔440 m，北部为凉山山原，海拔多在2 100 m以上，南部为西南山地，海拔为1 500~2 000 m，地形由西北向东南倾斜(图1)。研究区属于绿汁江深大断裂的平行次级碰撞带——小江断裂的波及范围，主要地质构造为金沙江断裂带，金沙江流经境内98.8 km。复杂的地形地貌和高程差为滑坡的发生提供了有利条件，对当地居民的生命财产安全构成潜在威胁。
1.2 数据源
本文数据源主要包括Sentinel-1A雷达数据、高分卫星光学遥感数据、地质构造与DEM数据以及灾害点、道路、水系矢量数据等，详细数据见表1。
2 研究方法
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2.1 研究流程
首先采用SBAS-InSAR技术获取金阳县LOS向地表形变速率和累积形变量，并结合高分卫星图像解译滑坡和潜在滑坡，更新滑坡编录；根据研究区地形地貌、灾害分布以及专家研究经验，从水文特征、植被土壤、断层分布、人类活动等多个方面初选14种易发性评价指标并分级，通过皮尔逊相关系数法对初选指标进行相关性检验，剔除强相关性因子，建立滑坡易发性评价指标体系。然后选择等量的滑坡点和非滑坡点作为训练数据集(70%用于训练模型，30%用于检验)，在Python环境下构建滑坡易发性评价模型，采用机器学习完成金阳县滑坡易发性评价，并通过ROC曲线对模型预测性能进行精度验证。最后在第一次机器学习评价结果的基础上，在极低易发区筛选非滑坡点进行样本优化，获得优化后的滑坡易发性评价结果，并结合地表形变速率对第二次滑坡易发性评价结果进行更新，最终得到金阳县滑坡易发性评价结果，具体流程如图2所示。
2.2 精度验证
混淆矩阵是一种用于衡量分类模型性能的矩阵，通过混淆矩阵计算出准确率、精确率、召回率和F1分数可全面评估分类模型的性能。ROC(receiver operating characteristic)曲线是评估模型分类效果的重要工具，通过计算曲线下面积(AUC值)可衡量模型性能。种子单元面积指数(seed cell area indexes, SCAI)是衡量滑坡易发性评价准确性的重要指标，可表示为分区面积占总面积的比值与滑坡点面积占分区面积的比值之比。低易发性等级区的SCAI值较高，高易发性等级区的SCAI值较低，两者比值越大，差异则越明显，分区效果越好[6]。
3 SBAS-InSAR形变监测与滑坡识别
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3.1 SAR数据
本文使用Sentinel-1A卫星C波段SLC影像，包含升降轨数据，数据模式为宽幅干涉模式(IW)，幅宽为250 km，并采用VV极化方式。为提升雷达数据的轨道精度并降低系统误差，使用POD精密定轨星历数据进行轨道校正。研究时段为2022-01—2024-01，选用128轨道升轨数据和62轨道降轨数据。为消除地形相位的影响，使用由地理空间数据云提供的GDEM V3作为外部数字高程模型，分辨率为30 m。本次研究影像处理使用SARscape软件，时间基线阈值设为120 d，空间基线阈值设为3%，基线具有较好的空间连接性。使用最小费用流法(MCF)进行相位解缠，解缠分解等级设为1，解缠相关性阈值设为0.3，滤波方法为Goldstein法。
3.2 SBAS-InSAR形变监测
对金阳县2022-01—2024-01期间Sentinel-1A升降轨数据进行SBAS-InSAR处理，获得研究区年平均LOS向形变速率，结果如图3所示。
由升轨数据可知，研究区内共有648.15 km2地表形变被探测到，占金阳县面积的37.01%，涵盖大多数已知的滑坡区域。升轨结果表明，金阳县在升轨方向的年平均形变速率为-101.1~79.2 mm/a，相对远离地面(速率为负)的形变速率较高区域主要位于北部的丙底镇和东北部的洛觉镇，均为山谷地区；而相对靠近地面(速率为正)的形变速率较高区域主要位于县城东西两侧的百草坡镇和德溪镇。在较平坦的城乡区域，地表较为稳定，形变速率在-15~15 mm/a之间。
由降轨数据可知，研究区内共有606.49 km2地表形变被探测到，占金阳县面积的34.59%。降轨结果表明，金阳县在降轨方向的年平均形变速率为-118.6~107.2 mm/a，形变速率较大区域的分布特征与升轨相似，主要为县城北部的丙底镇和东北部的洛觉镇，这些地区由于地形变化大、河流侵蚀严重加上人为因素，生态环境被破坏，地表稳定性差，容易发生地质灾害。
综上可知，无论升轨还是降轨数据，金阳县部分地区均显示出高形变速率，而在县城区域及研究区中部，降轨数据显示出低形变速率，表明这些地区地表更为稳定。
3.3 滑坡识别结果分析
基于以往经验识别潜在滑坡，识别规则如下：1)形变速率绝对值大于40 mm/a；2)形变点坡度大于5°；3)形变点在空间上呈现聚集分布[7]。基于升降轨数据和高分图像，本研究共识别出24处滑坡或潜在滑坡区。将上述形变点加载到2022年高分影像上，根据聚集点整体形状判断是否存在长条状、圆状、椭圆状以及不规则多边形，若存在则进行局部判断，观察是否存在明显滑坡体、滑坡后壁以及滑坡边界等。通过形变速率和光学影像可知，这些区域表现为裸露的基岩及光学影像中的明显滑坡迹象(岩土碎裂、裂缝、结构松散和植被稀疏)，具体如图4所示。
通过部分滑坡点实地验证可知，SBAS-InSAR升降轨形变速率结合高分图像解译滑坡的可信度较高，因此可以将上述24处滑坡用于滑坡编录更新，构建训练样本数据集，也可以将LOS向形变速率结果用于修正滑坡易发性评价制图。
4 滑坡易发性评价
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4.1 评价指标
将研究区138个历史滑坡点和解译的24个滑坡点作为正样本，在ArcGIS中以正样本为中心建立半径为1 km的圆形缓冲区，在缓冲区外掩膜河流，使用ArcGIS中“创建随机点”功能生成162个随机点作为负样本，构建机器学习样本数据集，选取其中70%的样本点作为训练样本，30%的样本点作为验证样本。本文采用30 m×30 m栅格单元作为基础评价单元，最终共划分为1 946 731个格网，同时使用高程、坡度、坡向等14个评价因子构建滑坡易发性评价指标体系，将连续性因子距河流距离、距道路距离、距断层距离按照间距进行分级处理，对地层年代、坡向等离散型因子按实际状态进行分级，其余因子均采用自然断点法进行分级处理。对各评价因子进行分类，结果如图5所示。
4.2 评价因子选取
对上述14个因子进行相关性分析，计算皮尔逊相关系数，并绘制滑坡因子热力图，用以表示评价因子之间的线性相关程度。由图6可知，地形起伏度和地表粗糙度与坡度之间具有最强的正相关性，说明这3个因素在空间分布上高度一致。其余因子之间的相关系数都在可接受的阈值范围之内(-0.6~0.6)，显示出较好的独立性，适合作为机器学习模型中的滑坡预测因子。利用随机森林算法计算得到因子重要性图，结果如图7所示。根据3个影响因子的重要性大小，剔除地形起伏度和地表粗糙度影响因子，将剩余12个因子继续用于后续滑坡易发性评价。
4.3 机器学习滑坡易发性评价
在Python中调用Scikit-learn库，使用随机森林(RF)、支持向量机(SVM)和极端梯度提升(XGBoost)3种模型进行训练，将预测结果导入ArcGIS后采用自然断点法进行制图，结果如图8所示。各模型滑坡易发性评价结果在空间分布上显示出较高的一致性，高和极高易发区位于金阳县东南部和中部，主要分布在河流两岸。这些区域因地处河流下游、频繁的人类活动、高落差以及断层发育等，为滑坡的触发创造了有利条件。相反，极低到低易发区多分布于金阳县西部、西南部和北部，这些区域人烟稀少，远离水体，因此土壤状况较好且具有较强的抗剪切能力，不利于滑坡发育。通过表2可知，滑坡点数量与滑坡易发性等级呈正比关系，即滑坡易发性等级越高，对应的滑坡点数量也越多。
4.4 精度检验
数据集经RF、SVM、XGBoost模型训练后，得到3种模型的易发性评价结果，通过绘制ROC曲线和计算准确率、精确率、召回率和F1分数进行精度验证。从图9和表3可以看出，3个模型的准确率和F1分数均大于0.8，AUC值均大于0.85，表明3个模型均具有良好的拟合精度和预测性能，可有效评价金阳县滑坡易发性。同时，3个模型的召回率稍大于精确率，即预测正确的滑坡样本比例大于预测正确的非滑坡样本比例。3个模型的准确率、精确率、F1分数和AUC值大小排序均为XGBoost>RF>SVM，对应的AUC值分别为0.92、0.90、0.86。XGBoost模型表现最优，其准确率、精确率、召回率和F1分数分别为0.895、0.895、0.896和0.899。
4.5 样本优化后机器学习滑坡易发性评价
针对非滑坡样本可能分布不合理的问题，基于最佳模型(XGBoost)得到的初步滑坡易发性分区结果，引入样本优化策略，以改善滑坡易发性评价结果。在XGBoost模型易发性评价结果中的极低易发区内，随机选取162个滑坡点作为改进后的非滑坡样本集，以此认为挑选的负样本更加合理；然后同样按照70%训练集和30%测试集的方法，对优化后的样本重新进行模型训练并制图[8]。使用优化后的数据集和适用于本研究区的最优模型，不改变模型参数，再次对金阳县进行滑坡易发性评价，在ArcGIS软件中根据自然断点法将评价结果分为5个等级，结果如图10所示。
将历史滑坡位置叠加到4种不同模型生成的滑坡易发性分区图上，并统计各易发区内的滑坡点比例及密度(表2、4)。结果表明，各模型的滑坡点密度均呈现出从极低易发区到极高易发区递增的趋势，说明所有模型的评估结果总体上与滑坡实际发生情况一致。
在极高易发区，样本优化后XGBoost模型预测的滑坡点数量超过初始XGBoost模型，而在极低和低易发区，滑坡点数量则低于初始XGBoost模型。此外，SCAI值从极低易发区到极高易发区呈递减趋势，且样本优化后XGBoost模型的SCAI值比值最大，也说明样本优化后的XGBoost模型分区效果最佳，其评估结果能更加准确地反映实际情况。由图11和表5可知，样本优化后的XGBoost模型表现最优，AUC值达到0.95，高于未进行样本优化的3个模型。
4.6 滑坡易发性结果修正
通过对比形变速率分析结果与滑坡易发性评价结果可知，LOS向形变速率能准确标注研究区内潜在滑坡的位置，与实际地表形变更加吻合。因此，本研究通过SBAS-InSAR技术获取LOS向形变速率数据，并构建一个优化矩阵，以提升样本优化后XGBoost模型滑坡易发性评价效果。LOS向形变速率在进行地理编码时采用30 m×30 m栅格单元，以保证结果的空间分辨率一致。
为匹配形变速率与滑坡易发性级别，使用ArcGIS软件的重分类功能，对形变速率进行5级分类处理。然后根据LOS向形变速率分类结果，制定一个优化矩阵(表6)，用以融合形变速率和滑坡易发性评估结果。改进后的结果如图12所示。
由图12可知，滑坡易发性等级有所提高的区域面积达到45.50 km2，具体而言，20.18 km2提升1级，11.20 km2提升2级，5.89 km2提升3级，8.23 km2提升4级。该方法不仅能有效降低假阴性的错误率，减少误判区域，同时也能提高滑坡易发性评价的实时性，使得易发性分区图能够持续更新，实现对金阳县滑坡易发性的动态准确评估。
5 结语
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本文以四川省金阳县为例，利用Sentinel-1A卫星升降轨数据和高分辨率遥感影像识别滑坡区域。采用3种机器学习模型进行滑坡易发性评价，获得分区评价结果，然后进行样本优化后的易发性评价，并通过形变速率数据对滑坡易发性结果进行修正，得到以下结论：
1) 采用SBAS-InSAR技术监测金阳县及其周边地区2022-01—2024-01形变信息，结果表明，升轨LOS向形变速率为-101.1~79.2 mm/a，降轨LOS向形变速率为-118.6~107.2 mm/a，高形变区主要位于研究区北部和东北部的丙底镇、洛觉镇和百草坡镇。结合高分辨率遥感图像，共识别出24个滑坡区域，并对滑坡数据集进行更新。
2) 采用RF、SVM和XGBoost三种机器学习模型对金阳县进行滑坡易发性评价，XGBoost模型预测准确率最高。样本优化后XGBoost模型的AUC值达到0.95，SCAI值也由极低易发区到极高易发区逐渐递减，比值达到最大，表明优化后模型的分区效果更好，优于单一模型。
3) 结合SBAS-InSAR技术对滑坡易发性评估结果进行修正，滑坡易发性等级有所提高的区域面积达到45.50 km2。采用修正策略能够显著降低误判为非滑坡区的概率，减少分类不准确的区域，实现对金阳县滑坡易发性的持续动态评估。
采用SBAS-InSAR技术和机器学习方法对金阳县进行滑坡易发性评价，尽管取得初步成果，但仍有可以提升之处：C波段对植被穿透能力较弱，可引入L波段SAR数据，从而在一定程度上扩大地表形变监测的范围；考虑滑坡体特征，将斜坡单元作为评价单元。
基金
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  • 国家自然科学基金(41301587)
  • 重大危险源测控四川省重点实验室开放课题(KFKT-2023-01)
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1
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2025年第45卷第3期
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引用本文
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文章信息
doi: 10.14075/j.jgg.2024.04.178
  • 接收时间：2024-04-15
  • 首发时间：2026-10-30
  • 出版时间：2025-03-15
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  • 收稿日期：2024-04-15
基金
National Natural Science Foundation of China(41301587)
国家自然科学基金(41301587)
Open Fund of Sichuan Key Laboratory of Measurement and Control of Major Hazard Sources(KFKT-2023-01)
重大危险源测控四川省重点实验室开放课题(KFKT-2023-01)
作者信息
    1 西南科技大学环境与资源学院, 四川省绵阳市青龙大道中段59号, 621010
    2 四川省安全科学技术研究院, 成都市武科西四路18号, 610045
    3 西南科技大学土木工程与建筑学院, 四川省绵阳市青龙大道中段59号, 621010

通讯作者:

吴彩燕，博士，教授，主要从事GIS与地质灾害研究，E-mail:

WU Caiyan, PhD, professor, majorsin GIS and geological hazard, E-mail:

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https://castjournals.cast.org.cn/joweb/ddcl/CN/10.14075/j.jgg.2024.04.178
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2种不同金属材料的力学参数
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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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