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SBAS-InSAR Analysis of tectonic derived ground deformation and subsidence susceptibility mapping via machine learning in Quetta City, Pakistan

文献类型：期刊论文

作者Hussain, Sajid2; Pan, Bin2,5; Afzal, Zeeshan7; Sajjad, Meer Muhammad3,4; Kakar, Najeebullah1,9; Ahmed, Nisar8; Hussain, Wajid7; Ali, Muhammad6
刊名INTERNATIONAL JOURNAL OF DIGITAL EARTH
出版日期2025-12-31
卷号18期号:1页码:23
关键词Ground deformation time series InSAR tectonics Quetta City GPS subsidence susceptibility
ISSN号1753-8947
DOI10.1080/17538947.2024.2441926
产权排序4
英文摘要This study utilized Time-Series Synthetic Aperture Radar Interferometry (TSInSAR) to provide accurate and cost-effective monitoring of ground displacement in Quetta City, Pakistan - a seismically active and rapidly urbanizing region. Investigation into the influence of active fault line networks and lithological composition on ground movements and subsidence susceptibility mapping (SSM) has not yet been revealed, which is crucial for risk mitigation. Employing two years of Sentinel-1 images, this research assesses ground deformation using the Small Baseline Subset (SBAS) technique, while the Logistic Regression (LR) model was employed to assess subsidence susceptibility. Results indicate significant displacement in the central urban area with an average vertical subsidence velocity of - 166 mm/yr and an uplift rate of 48 mm/yr in the surrounding hilly terrain. A local Global Positioning System (GPS) station provided validation, confirming an average vertical velocity of - 163.3 mm/yr, underscoring the reliability of InSAR data. The LR model owns an accuracy of 0.92 in the Area Under Curve (AUC) approach and predicts the quaternary lithologies, constructed regions, and fault lines are the main triggers of subsidence. In sum, the findings suggest that tectonic activities are the main cause of the ground movement, while human-induced elements contribute significantly as a secondary influence.
WOS关键词EARTHQUAKE ; SAR
WOS研究方向Physical Geography ; Remote Sensing
语种英语
WOS记录号WOS:001388599600001
出版者TAYLOR & FRANCIS LTD
源URL[http://ir.igsnrr.ac.cn/handle/311030/211942]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Pan, Bin
作者单位1.Changan Univ, Sch Geol Engn & Geomat, Xian, Peoples R China
2.Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
5.Hubei Luojia Lab, Wuhan 430079, Peoples R China
6.Univ Napoli Parthenope, Dipartimento Ingn, Naples, Italy
7.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan, Peoples R China
8.Geol Survey Pakistan, Balochistan, Pakistan
9.Univ Balochistan, Dept Geol, Quetta, Pakistan
推荐引用方式
GB/T 7714		 Hussain, Sajid,Pan, Bin,Afzal, Zeeshan,et al. SBAS-InSAR Analysis of tectonic derived ground deformation and subsidence susceptibility mapping via machine learning in Quetta City, Pakistan[J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH,2025,18(1):23.
APA Hussain, Sajid.,Pan, Bin.,Afzal, Zeeshan.,Sajjad, Meer Muhammad.,Kakar, Najeebullah.,...&Ali, Muhammad.(2025).SBAS-InSAR Analysis of tectonic derived ground deformation and subsidence susceptibility mapping via machine learning in Quetta City, Pakistan.INTERNATIONAL JOURNAL OF DIGITAL EARTH,18(1),23.
MLA Hussain, Sajid,et al."SBAS-InSAR Analysis of tectonic derived ground deformation and subsidence susceptibility mapping via machine learning in Quetta City, Pakistan".INTERNATIONAL JOURNAL OF DIGITAL EARTH 18.1(2025):23.

入库方式： OAI收割

来源：地理科学与资源研究所

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