一种基于滑坡易发性优化SBAS-InSAR解译结果的方法

唐璐瑶; 曾斌; 袁晶晶; 艾东; 许汇源

doi:10.19509/j.cnki.dzkq.tb20240412

一种基于滑坡易发性优化SBAS-InSAR解译结果的方法

doi: 10.19509/j.cnki.dzkq.tb20240412

唐璐瑶^1,,
曾斌^1, ,,
袁晶晶²,
艾东²,
许汇源²

1 .
中国地质大学（武汉）环境学院，武汉 430074
2 .
湖北省地质局第七地质大队，湖北宜昌 443100

基金项目: 湖北省地质局第七地质大队科技项目（DQKJ2022-1）

详细信息

作者简介:
唐璐瑶：E-mail：1063954230@qq.com

通讯作者:
E-mail：zengbin_19@126.com

计量
- 文章访问数: 169
- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2024-07-24
- 录用日期: 2024-11-28
- 修回日期: 2024-11-27
- 网络出版日期: 2025-07-09

A method for optimizing SBAS-InSAR interpretation results based on landslide susceptibility

1 .
School of Environmental Studies, China University of Geoscience (Wuhan), Wuhan 430078, China
2 .
The Seventh Geological Brigade of Hubei Geological Bureau, Yichang Hebei 443100, China

More Information

Author Bio:
E-mail：1063954230@qq.com

Corresponding author: E-mail：zengbin_19@126.com

摘要

摘要:
SBAS-InSAR解译结果具有多解性，直接利用解译的形变点识别滑坡隐患区具有较大的不确定性。为此，以清江北岸（长阳段）为研究区，提出了一种利用滑坡易发性等综合优化SBAS-InSAR解译结果的方法。首先将SBAS-InSAR解译的形变点进行聚类和异常值分析（Anselin Local Moran's I工具），保留低值聚类形变点；然后，选取高程、坡度、坡向、工程地质岩组、距断层距离、距水泵距离、距道路距离、地形湿度指数8个指标，采用信息量法评价并得到滑坡易发性分区图，利用受试者工作特征曲线（ROC）验证得到ROC曲线下面积（AUC）值为0.844，表明易发性评价结果可靠；最后，通过设置阈值（地表形变速率v≤−10 mm/a）和易发性结果对低值聚类形变点进行筛选，得到优化后的SBAS-InSAR结果图。选取部分区域进行野外验证，结果显示：优化后的形变点数量减少，其分布特征与研究区历史滑坡的发育规律更一致。此外，以渔坪村一组滑坡与偏山滑坡作为典型实例，比较SBAS-InSAR与GNSS在同一时刻监测到的地表形变量。其中渔坪村一组滑坡显示的SBAS-InSAR与GNSS在同一时刻监测到的地表形变量差值范围为0～7.87 mm，平均差值约为2.23 mm，均方根误差（RMSE）为3.67。研究证明，对SBAS-InSAR解译结果的优化方法具有较好的实用性及可靠性，可为InSAR技术应用于地质灾害领域提供有益参考。
- 滑坡 /
- SBAS-InSAR /
- Anselin Local Moran's I /
- 易发性 /
- 优化方法
Abstract: Objective
The interpretation results of SBAS-InSAR exhibit multiple solutions, making it uncertain to directly use the interpreted deformation points for identifying potential landslide-prone areas. Therefore, taking the north bank of the Qingjiang River (Changyang Section) as the study area, this study proposed a method to comprehensively optimize SBAS-InSAR interpretation results by incorporating landslide susceptibility evaluation.
Methods
First, the deformation points interpreted by SBAS-InSAR were analyzed using clustering and outlier detection (Anselin Local Moran's I), and low-value cluster deformation points were retained. Subsequently, eight factors, including elevation, slope, and slope aspect, were selected to evaluate and generate a landslide susceptibility zoning map using the information value method. The reliability of the landslide susceptibility evaluation was confirmed by an ROC curve, with an AUC value of 0.844.
Results
The optimized SBAS-InSAR results were obtained by filtering low-value cluster deformation points based on a threshold value (v ≤ -10 mm/a) and incorporating the landslide susceptibility zoning map. Field verification in selected areas shows that the number of deformation points was reduced after optimization, and their distribution characteristics were more consistent with the historical landslide development in the study area. Additionally, taking the Yupingcun landslide group and the Pianshan landslide as typical cases, the surface deformation values monitored by SBAS-InSAR and GNSS at the same time were compared. In the case of the Yupingcun landslide, the difference between surface displacement values monitored by SBAS-InSAR and GNSS ranged from 0 to 7.87 mm, with an average difference of approximately 2.23 mm and an RMSE of 3.67.
Conclusion
The proposed optimization method for SBAS-InSAR interpretation was demonstrated to be both practical and reliable, providing valuable insights into the application of InSAR technology in the field of geological disasters.
- landslide /
- SBAS-InSAR /
- Anselin Local Moran's I /
- susceptibility /
- optimization method

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图 1 研究区位置

Figure 1. Location of the study area

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图 2 技术路线图

Figure 2. Technical flowchart of the paper

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图 3 SBAS-InSAR技术流程图

Figure 3. Technical flowchart of SBAS-InSAR

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图 4 SBAS-InSAR技术处理过程图

a. 时空基线图；b. 干涉像对图；c. 干涉图；d.解缠图

Figure 4. Technical processing flowchart for SBAS-InSAR

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图 5 评价因子选取及重分类

Figure 5. Selection and reclassification of evaluation factors

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图 6 研究区SBAS-InSAR技术解译形变速率图

Figure 6. SBAS-InSAR-derived deformation velocity map of the study area

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图 7 研究区滑坡易发性评价分区图

Figure 7. Landslide susceptibility zoning map of the study area

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图 8 ROC曲线

Figure 8. ROC curve

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图 9 聚类和异常值分析结果图（LL，LH，HL，HH，No Signifiant分别为形变点类型，其含义见第2.3节）

Figure 9. Results of clustering and outlier analysis

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图 10 研究区沉降速率区间统计（315，0.54%分别代表形变点数目和沉降速率区间形变点数量占总形变点数量的比例）

Figure 10. Statistical distribution of surface subsidence rate interval in the study area

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图 11 研究区SBAS-InSAR解译结果优化图

Figure 11. Optimization of SBAS-InSAR interpretation results in the study area

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图 12 部分区域优化前后形变速率对比及野外调查现象（No.1～No.6为变形区域编号）

Figure 12. Comparison of deformation rates before and after optimization and field survey phenomena in some regions

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图 13 优化前后集镇形变速率对比

a. 渔峡口镇形变速率（优化前）；b. 渔峡口镇形变速率（优化后）；c. 资丘镇形变速率（优化前）；d. 资丘镇形变速率（优化后）

Figure 13. Comparison of deformation points in towns before and after optimization

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图 14 渔坪村一组滑坡SBAS-InSAR解译点累计形变量和GNSS监测累计形变量对比

Figure 14. Comparison of cumulative deformation between SBAS-InSAR interpreted points and GNSS monitoring of Yupingcun landslide

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图 15 渔坪村一组滑坡形变分析

a. 已修复的裂缝；b. GNSS；c. 公路裂缝；d. 渔坪村一组滑坡形变速率（优化前）；e. 渔坪村一组滑坡形变速率（优化后）；f，g. 居民房屋墙体裂缝

Figure 15. Yupingcun landslide deformation analysis

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图 16 偏山滑坡SBAS-InSAR解译点累计形变量和GNSS监测累计形变量对比

Figure 16. 6 Comparison of cumulative deformation between SBAS-InSAR interpreted points and GNSS monitoring of Pianshan landslide

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图 17 偏山滑坡形变分析

a，b，e. 裂缝；c. 偏山滑坡形变速率（优化前；d. 偏山滑坡形变速率（优化后）；f. 土体溜滑

Figure 17. Pianshan landslide deformation analysis

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