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Monitoring tailings storage facilities with multi-temporal DInSAR: A systematic review.

Vanessa Sánchez1, Francisco Cabrera-Torres2, Susana Arciniegas3

  • 1Division of Sustainable Resources Engineering, Graduate School of Engineering, Hokkaido University, Nishi-8, Kita-13, Sapporo, 060-8628, Japan.

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Summary

Multi-Temporal Differential Interferometric Synthetic Aperture Radar (MT-DInSAR) effectively monitors tailings storage facilities for deformation. This review highlights its adaptability and the need for optimized processing and integrated monitoring for improved safety.

Keywords:
Failure behavior assessmentMulti-temporal interferometric SARStability monitoringTailings storage facility

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Area of Science:

  • Geotechnical Engineering
  • Remote Sensing
  • Environmental Monitoring

Background:

  • Rising global demand for raw materials increases tailings production, necessitating extensive land for Tailings Storage Facilities (TSFs).
  • TSFs pose significant environmental and human risks due to potential failures, making continuous monitoring crucial for long-term stability.
  • Multi-Temporal Differential Interferometric Synthetic Aperture Radar (MT-DInSAR) shows promise for detecting surface deformation in TSFs, but its application needs comprehensive evaluation.

Purpose of the Study:

  • To systematically review and evaluate the application of MT-DInSAR for monitoring Tailings Storage Facilities (TSFs).
  • To identify common methodologies, data sources, and validation techniques used in MT-DInSAR studies for TSFs.
  • To assess the effectiveness of MT-DInSAR in detecting surface deformation for both preventive monitoring and post-failure analysis of TSFs.

Main Methods:

  • Systematic literature review following PRISMA guidelines, analyzing 23 publications from 2269 entries across four databases.
  • Focus on case studies involving 18 different TSFs, including well-documented failures.
  • Analysis of data sources (predominantly Sentinel-1), processing techniques (SBAS, PSI), phase unwrapping methods (MCF, SNAPHU), and temporal coherence thresholds.

Main Results:

  • MT-DInSAR is adaptable to diverse TSF environments, with 61.11% of studies focusing on preventive monitoring and 38.89% on post-failure analysis.
  • Sentinel-1 imagery and combined SBAS/PSI techniques are prevalent for enhanced spatial coverage and measurement density.
  • While MT-DInSAR can detect pre-failure acceleration, only 21.74% of studies validate results with ground-based data, highlighting a gap in verification.

Conclusions:

  • MT-DInSAR is a valuable tool for TSF monitoring, capable of detecting surface deformation trends and identifying pre-failure acceleration.
  • Optimizing processing parameters and integrating MT-DInSAR with in-situ monitoring are essential for enhancing deformation detection and early warning systems.
  • Improved validation strategies and data integration are needed to fully leverage MT-DInSAR for robust geotechnical risk management in mining operations.