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基于SBAS-INSAR技术的山区三维地表变形场监测及影响因素分析(中国天津)

Three-dimensional surface deformation field monitoring and influencing factors analysis in mountainous areas based on SBAS-INSAR technology (Tianjin, China).

作者信息

Shang Jun, Wang Mingyang, Wang Xin, Yang Mengyao, Wu Yifan, Du Wangze

机构信息

Tianjin Chengjian University, Tianjin, 300192, China.

出版信息

Sci Rep. 2025 Jul 16;15(1):25702. doi: 10.1038/s41598-025-10894-2.

DOI:10.1038/s41598-025-10894-2
PMID:40670532
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12267393/
Abstract

Synthetic aperture radar interferometry (InSAR) technology has emerged as a critical methodology for disaster reduction and prevention, offering unprecedented all-weather operational capabilities and extensive spatial coverage that effectively address the limitations of traditional detection methods. Despite the inherent challenges of temporal and spatial coherence in conventional time-series InSAR approaches, the small baseline subset InSAR (SBAS-InSAR) technique presents a sophisticated solution by significantly mitigating coherence-related uncertainties and enhancing measurement precision. While existing research predominantly focuses on urban environments, this study uniquely addresses the research gap in mountainous terrain deformation monitoring by utilizing Sentinel-1A and 1B single-look complex (SLC) data from ascending and descending orbits between January 2018 and May 2022. The comprehensive analysis of land subsidence in northern Tianjin's mountainous region revealed multi-directional surface deformation characteristics, with validation against GNSS Kriging interpolation data demonstrating root mean square errors of 5.74 mm and 5.09 mm in vertical and east-west directions, respectively. The investigation exposed predominantly horizontal deformation influenced by large-scale engineering activities, topographic conditions, and precipitation patterns, with notable findings including a maximum north-south deformation of 54.62 mm in the Maojiayu landslide area and vertical cumulative deformations of 21.10 mm and - 10.31 mm in Maojiayu and Taoosi landslide areas. These results substantiate the efficacy of InSAR technology in monitoring surface deformation in mountainous regions, offering critical insights for regional geological disaster prevention and mitigation strategies.

摘要

合成孔径雷达干涉测量(InSAR)技术已成为减灾防灾的关键方法,具有前所未有的全天候作业能力和广泛的空间覆盖范围,有效克服了传统探测方法的局限性。尽管传统时间序列InSAR方法存在时间和空间相干性方面的固有挑战,但小基线子集InSAR(SBAS-InSAR)技术通过显著减轻与相干性相关的不确定性并提高测量精度,提供了一种复杂的解决方案。现有研究主要集中在城市环境,本研究则通过利用2018年1月至2022年5月期间 Sentinel-1A和1B 单视复数(SLC)数据,从升轨和降轨两个方向,独特地解决了山区地形变形监测方面的研究空白。对天津北部山区地面沉降的综合分析揭示了多方向的地表变形特征,与GNSS克里金插值数据验证显示,垂直方向和东西方向的均方根误差分别为5.74毫米和5.09毫米。调查发现,地表变形主要受大规模工程活动、地形条件和降水模式的影响,显著的发现包括毛家峪滑坡区域最大南北向变形为54.62毫米,毛家峪和陶寺滑坡区域的垂直累积变形分别为21.10毫米和 -10.31毫米。这些结果证实了InSAR技术在监测山区地表变形方面的有效性,为区域地质灾害防治策略提供了关键见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/045a/12267393/8fcb90425608/41598_2025_10894_Fig12_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/045a/12267393/732da2887a2c/41598_2025_10894_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/045a/12267393/1bc16d44c214/41598_2025_10894_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/045a/12267393/85c6bacf77d5/41598_2025_10894_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/045a/12267393/74df4a9e98e2/41598_2025_10894_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/045a/12267393/8aec38afb10e/41598_2025_10894_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/045a/12267393/4c534f13803a/41598_2025_10894_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/045a/12267393/0352f512f05a/41598_2025_10894_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/045a/12267393/ada2bfd4ff68/41598_2025_10894_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/045a/12267393/8fcb90425608/41598_2025_10894_Fig12_HTML.jpg

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本文引用的文献

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Sensors (Basel). 2024 Oct 21;24(20):6760. doi: 10.3390/s24206760.
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Engineering and environmental assessment of soilbag-based slope stabilisation for sustainable landslide mitigation in mountainous area.基于土袋的边坡稳定工程与环境评估:山区可持续滑坡减灾
J Environ Manage. 2024 May;359:120970. doi: 10.1016/j.jenvman.2024.120970. Epub 2024 Apr 26.
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Identification of Landslides in Mountainous Area with the Combination of SBAS-InSAR and Yolo Model.
利用 SBAS-InSAR 和 Yolo 模型识别山区滑坡。
Sensors (Basel). 2022 Aug 19;22(16):6235. doi: 10.3390/s22166235.