Application of Sentinel-1 InSAR to monitor tailings dams and predict geotechnical instability: practical considerations based on case study insights
Bibliographic record
Abstract
Abstract Tailings storage facilities (TSFs) impound mining waste behind dams to ensure public safety, but failure incidents have prompted calls for more robust monitoring programs. Satellite-based interferometric synthetic aperture radar (InSAR) has grown in popularity due to its ability to remotely detect millimeter-scale displacements in most urban and some natural terrains. However, there remains a limited understanding of whether InSAR can be as accurate or representative as on-the-ground instruments, whether failures can be predicted in advance using InSAR, and what variables govern the quality and reliability of InSAR results. To address these gaps, we analyze open-source, medium-resolution Sentinel-1 data to undertake a ground-truth assessment at a test site and a forensic analysis of five failure cases. We use a commercial software with an automated Persistent Scatterer (PS) workflow (SARScape Analytics) for all case study sites except one and a proprietary algorithm (SqueeSAR) with a dual PS and Distributed Scatterer (DS) algorithm for the ground-truth site and one forensic case. The main goal is to deliver practical insights regarding the influence of algorithm/satellite selection, environmental conditions, site activity, coherence thresholds, satellite-dam geometry, and failure modes. We conclude that Sentinel-1 InSAR can serve as a hazard-screening tool to help guide where to undertake targeted investigations; however, most potential failure modes may not exhibit InSAR-detectable accelerations that could assist with time-of-failure prediction in real time. As such, long-term monitoring programs should ideally be integrated with a combination of remote sensing and field instrumentation to best support engineering practice and judgment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".