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Monitoring tailings storage facilities with multi-temporal DInSAR: A systematic review
Journal
Science of The Total Environment
ISSN
0048-9697
Date Issued
2026-01
Author(s)
Vanessa Sánchez
Francisco Cabrera Torres
Susana Arciniegas
Natsuo Okada
Yoko Ohtomo
Fidelis Suorineni
Youhei Kawamura
Abstract
The rising global demand for raw materials has intensified tailings production, requiring extensive land use for Tailings Storage Facilities (TSFs), which pose significant environmental and human risks if they fail.
Continuous monitoring throughout their lifecycle is essential to ensure long-term stability, and Multi-Temporal Differential Interferometric Synthetic Aperture Radar (MT-DInSAR) has emerged as a complementary tool for detecting surface deformation, particularly in post-failure assessments and trend identification. However, comprehensive evaluation of its application in TSF monitoring remains limited.
This study presents a systematic literature review following PRISMA guidelines, identifying 23 relevant publications from 2269 entries across four indexed databases. These works encompass 18 different TSFs case studies, including well-documented examples such as Córrego do Feijão (Brazil), Cadia Valley Operations (Australia), Żelazny Most (Poland) and Kennecott (USA). Among the reviewed cases, 38.89 % focused on post-failure analysis and 61.11 % on preventive monitoring. Sentinel-1 imagery dominated usage (87.50 %), with the Small Baseline Subset (SBAS) technique often combined with Persistent Scatterer Interferometry (PSI) to enhance spatial coverage and measurement density.
Methodological diversity strongly influenced the quality of the results. Phase unwrapping methods, particularly Minimum Cost Flow (MCF) and SNAPHU, were the most common, ensuring phase continuity across large or topographically complex TSFs. Temporal coherence thresholds ranged from 0.40 to 0.60, reflecting adjustments to terrain, sensor, and monitoring objectives.
Only 21.74 % of studies validate results using ground-based data, while 43.48 % verified deformation trends through documented TSF failure records, demonstrating MT-DInSAR's capacity to detect pre-failure acceleration.
Overall, the findings confirm MT-DInSAR's adaptability across diverse TSF environments and emphasize the need to optimize processing parameters and to integrate satellite-based and in-situ monitoring to enhance deformation detection, support early warning systems, and improve geotechnical risk management in mining operations.
Continuous monitoring throughout their lifecycle is essential to ensure long-term stability, and Multi-Temporal Differential Interferometric Synthetic Aperture Radar (MT-DInSAR) has emerged as a complementary tool for detecting surface deformation, particularly in post-failure assessments and trend identification. However, comprehensive evaluation of its application in TSF monitoring remains limited.
This study presents a systematic literature review following PRISMA guidelines, identifying 23 relevant publications from 2269 entries across four indexed databases. These works encompass 18 different TSFs case studies, including well-documented examples such as Córrego do Feijão (Brazil), Cadia Valley Operations (Australia), Żelazny Most (Poland) and Kennecott (USA). Among the reviewed cases, 38.89 % focused on post-failure analysis and 61.11 % on preventive monitoring. Sentinel-1 imagery dominated usage (87.50 %), with the Small Baseline Subset (SBAS) technique often combined with Persistent Scatterer Interferometry (PSI) to enhance spatial coverage and measurement density.
Methodological diversity strongly influenced the quality of the results. Phase unwrapping methods, particularly Minimum Cost Flow (MCF) and SNAPHU, were the most common, ensuring phase continuity across large or topographically complex TSFs. Temporal coherence thresholds ranged from 0.40 to 0.60, reflecting adjustments to terrain, sensor, and monitoring objectives.
Only 21.74 % of studies validate results using ground-based data, while 43.48 % verified deformation trends through documented TSF failure records, demonstrating MT-DInSAR's capacity to detect pre-failure acceleration.
Overall, the findings confirm MT-DInSAR's adaptability across diverse TSF environments and emphasize the need to optimize processing parameters and to integrate satellite-based and in-situ monitoring to enhance deformation detection, support early warning systems, and improve geotechnical risk management in mining operations.