SBAS-InSAR Analysis of tectonic derived ground deformation and subsidence susceptibility mapping via machine learning in Quetta City, Pakistan
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...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2024.2441926 |
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| author | Sajid Hussain Bin Pan Zeeshan Afzal Meer Muhammad Sajjad Najeebullah Kakar Nisar Ahmed Wajid Hussain Muhammad Ali |
| author_facet | Sajid Hussain Bin Pan Zeeshan Afzal Meer Muhammad Sajjad Najeebullah Kakar Nisar Ahmed Wajid Hussain Muhammad Ali |
| author_sort | Sajid Hussain |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8ada434138894e17b4920e7bf9e7fb3a |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-8ada434138894e17b4920e7bf9e7fb3a2025-01-02T04:26:48ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-12-0118110.1080/17538947.2024.2441926SBAS-InSAR Analysis of tectonic derived ground deformation and subsidence susceptibility mapping via machine learning in Quetta City, PakistanSajid Hussain0Bin Pan1Zeeshan Afzal2Meer Muhammad Sajjad3Najeebullah Kakar4Nisar Ahmed5Wajid Hussain6Muhammad Ali7School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, People’s Republic of ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, People’s Republic of ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, People’s Republic of ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing, People’s Republic of ChinaSchool of Geological Engineering and Geomatics, Chang’an University, Xi’an, People’s Republic of ChinaGeological Survey of Pakistan, Balochistan, PakistanState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, People’s Republic of ChinaDipartimento di Ingegneria, Università degli Studi di Napoli Parthenope, Naples, ItalyThis 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.https://www.tandfonline.com/doi/10.1080/17538947.2024.2441926Ground deformationtime series InSARtectonicsQuetta CityGPSsubsidence susceptibility |
| spellingShingle | Sajid Hussain Bin Pan Zeeshan Afzal Meer Muhammad Sajjad Najeebullah Kakar Nisar Ahmed Wajid Hussain Muhammad Ali SBAS-InSAR Analysis of tectonic derived ground deformation and subsidence susceptibility mapping via machine learning in Quetta City, Pakistan International Journal of Digital Earth Ground deformation time series InSAR tectonics Quetta City GPS subsidence susceptibility |
| title | SBAS-InSAR Analysis of tectonic derived ground deformation and subsidence susceptibility mapping via machine learning in Quetta City, Pakistan |
| title_full | SBAS-InSAR Analysis of tectonic derived ground deformation and subsidence susceptibility mapping via machine learning in Quetta City, Pakistan |
| title_fullStr | SBAS-InSAR Analysis of tectonic derived ground deformation and subsidence susceptibility mapping via machine learning in Quetta City, Pakistan |
| title_full_unstemmed | SBAS-InSAR Analysis of tectonic derived ground deformation and subsidence susceptibility mapping via machine learning in Quetta City, Pakistan |
| title_short | SBAS-InSAR Analysis of tectonic derived ground deformation and subsidence susceptibility mapping via machine learning in Quetta City, Pakistan |
| title_sort | sbas insar analysis of tectonic derived ground deformation and subsidence susceptibility mapping via machine learning in quetta city pakistan |
| topic | Ground deformation time series InSAR tectonics Quetta City GPS subsidence susceptibility |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2024.2441926 |
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