Measuring Tailings Storage Facility Bathymetry Using Sentinel-2 and Landsat-8/9 Multispectral Imagery and Machine Learning
Bibliographic record
Abstract
Abstract. Tailings, a byproduct of mining, consist of fine sediment particles suspended in water that are stored in tailings storage facilities (TSFs). The discharge of untreated TSF water into the environment is typically prohibited due to its contact with mine tailings and processing chemicals. TSF failures have caused damage to communities and the environment, prompting calls for better management practices and advanced monitoring tools. For operational mine water management, boat-based bathymetric surveys have been used. However, these technologies have limitations, especially when the surveying of large facilities is required. Advances in remote sensing, particularly satellite-based earth observation (SBEO), offer cost-effective solutions for monitoring TSFs. This study explores the use of machine learning models, including XGBoost and Convolutional Neural Networks (CNNs), applied to Sentinel-2 and Landsat-8/9 data to estimate TSF bathymetry. Surveyed bathymetry datasets were used for model training, testing, and results validation. The results of the experiments revealed that high-accuracy bathymetric estimates could be obtained with mean absolute errors between 6 and 12 cm depending on the source of the data (i.e. Sentinel-2 or Landsat-8/9) and the model used (XGBoost vs CNN). Limitations include mixed pixel effects on the pond-beach interface and lower accuracies obtained in shallow areas, notably when XGBoost is used. This research underscores the potential of using satellite data and machine learning for TSF bathymetric monitoring, with implications for enhancing environmental and safety standards in mining operations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".