Assessing tailings consolidation and changes in supernatant pond area using InSAR and the normalised difference moisture index
Bibliographic record
Abstract
Monitoring of tailings storage facilities (TSF) is a critical component of sustainable mining practices. The primary goal of monitoring programmes is to ascertain the correct performance of the tailings facility, thus preventing impoundment failures that may lead to fatalities, severe environmental consequences, and substantial financial losses. Field geotechnical investigations and displacement analysis at tailings impoundments are usually spatially limited as their implementation over a large spatial extent would be cost-prohibitive. Interferometric Synthetic Aperture Radar (InSAR) is a widely used remote sensing monitoring tool that provides a synoptic view of displacement by utilising a high density, high frequency and high-precision network of measurement points. Tailings undergo consolidation settlement as they desaturate over time. Although the movement is mainly vertical, the detection of lateral movement towards the east or west may indicate preferential desaturation pathways. The following study describes the use of an advanced multi-temporal InSAR algorithm to process both commercial high-resolution TerraSAR-X and publicly available lower-resolution Sentinel-1 satellite radar imagery to monitor tailings consolidation. To characterise saturation changes over the same period, optical images from the Sentinel-2 satellite are employed. The specific goals of the study are to: (i) monitor motion within TSFs, focusing on changes in rates of consolidation and any lateral movement over time, (ii) identify areas of higher magnitudes of settlement along with any lateral (east–west) movement, suggesting preferential desaturation pathways, and (iii) correlate changes in consolidation behaviour with the changes of the supernatant pond area. The conclusion of the study suggests that the combined use of both technologies over tailings facilities provides valuable insight into governing dynamics of tailings. The results indicate a probable correlation between the consolidation rates with the supernatant pond area changes. This information can be implemented with the operational plans for a better characterisation of the dynamics within TSF’s.
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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".