Time-Series Monitoring and Analysis of Surface Deformation in Shiguilong Tailings Storage Using E-SBAS-InSAR

Tailings storage facility (TSF) failures have caused severe casualties and economic losses. This study used Enhanced Small Baseline Subset InSAR (E-SBAS-InSAR) and 88 Sentinel-1A images to retrieve the 2022–2024 surface deformation time series of the Shiguilong TSF, located in the Fe–Cu polymetallic metallogenic belt of the middle–lower Yangtze River. The reliability of the results was assessed through consistency comparisons with Small Baseline Subset InSAR (SBAS-InSAR) and Persistent Scatterer InSAR (PS-InSAR). A time-series decomposition model was applied to extract seasonal deformation components and analyze their lagged responses to temperature and intense rainfall events. The results show that: (1) E-SBAS-InSAR achieved a monitoring-point density nearly 7 times higher than SBAS-InSAR, enabling dense and long-term deformation characterization; (2) subsidence at Shiguilong continued to increase, with cumulative subsidence reaching −76.8 mm and a maximum annual mean subsidence rate of −22.78 mm/yr; (3) deformation was mainly controlled by long-term consolidation of loose tailings and creep of dam–tailings materials, while seasonal factors induced stage-dependent fluctuations; (4) seasonal deformation showed lagged responses of 6 days to temperature variations and 2 days to intense rainfall events, with rainfall exerting a more pronounced influence. This work is significant for TSFs monitoring under complex surface conditions.

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Publication Details

Journal
Remote Sensing
Published
2026-06-09
DOI
https://doi.org/10.3390/rs18121905
Primary Topic
Tailings Management and Properties
Type
article
Field-Weighted Citation Impact
0.00

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article

Time-Series Monitoring and Analysis of Surface Deformation in Shiguilong Tailings Storage Using E-SBAS-InSAR

Chuanzeng Shu, Haoxin Cui, Dongliang Han, Yibo Meng et al.
Remote Sensing
Tailings Management and Properties
article

Time-Series Monitoring and Analysis of Surface Deformation in Shiguilong Tailings Storage Using E-SBAS-InSAR

Chuanzeng Shu, Haoxin Cui, Dongliang Han, Yibo Meng, Zhiguo Meng, Qing Ding
article en

Abstract

Tailings storage facility (TSF) failures have caused severe casualties and economic losses. This study used Enhanced Small Baseline Subset InSAR (E-SBAS-InSAR) and 88 Sentinel-1A images to retrieve the 2022–2024 surface deformation time series of the Shiguilong TSF, located in the Fe–Cu polymetallic metallogenic belt of the middle–lower Yangtze River. The reliability of the results was assessed through consistency comparisons with Small Baseline Subset InSAR (SBAS-InSAR) and Persistent Scatterer InSAR (PS-InSAR). A time-series decomposition model was applied to extract seasonal deformation components and analyze their lagged responses to temperature and intense rainfall events. The results show that: (1) E-SBAS-InSAR achieved a monitoring-point density nearly 7 times higher than SBAS-InSAR, enabling dense and long-term deformation characterization; (2) subsidence at Shiguilong continued to increase, with cumulative subsidence reaching −76.8 mm and a maximum annual mean subsidence rate of −22.78 mm/yr; (3) deformation was mainly controlled by long-term consolidation of loose tailings and creep of dam–tailings materials, while seasonal factors induced stage-dependent fluctuations; (4) seasonal deformation showed lagged responses of 6 days to temperature variations and 2 days to intense rainfall events, with rainfall exerting a more pronounced influence. This work is significant for TSFs monitoring under complex surface conditions.

Remote SensingVol. 18(12)
Jilin University (CN), China National Commission for Disaster Reduction (CN), Jilin Province Science and Technology Department (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 10%
Tailings Management and Properties
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Time-Series Monitoring and Analysis of Surface Deformation in Shiguilong Tailings Storage Using E-SBAS-InSAR — Chuanzeng Shu, Haoxin Cui, et al. · Remote Sensing (2026) | TGRS Research Map | TGRS