Safeguarding Tailings Dams from Space Using L-Band SAR Soil Moisture Analysis
Bibliographic record
Abstract
Recent commercial uses of L-Band SAR in the water industry for the detection of water leaks have led to further developments in its application within the ground engineering industry to determine the concentrations of sub-surface soil moisture which can be indicative of potential problems within earthwork assets, road pavements, and rail beds. L-Band SAR has also been explored for use in tailings storage facilities (TSF) and earth dams monitoring and safety. Internal erosion of TSF and earth dams is one of the major causes of failure; the consequences of which are far reaching, including loss of life, environmental disaster, loss of reputation, and significant financial penalties. Signs of internal erosion that do not often manifest themselves at surface, hence visual inspection and non-targeted investigations or instrumentation, are unlikely to detect the tell-tale signs that seepage or erosion is present. L-Band SAR has the ability to detect moisture below the ground surface and determine the presence of high soil moisture concentrations which may be indicative of areas of internal seepage, in advance of this manifesting itself at the surface. This crucially provides the operator with time. The time required for targeted inspection, investigation, and the design of intervention measures, to prevent potential catastrophic failure of their assets. Following the devastating failure of the Brumadinho TSF in Brazil in 2019, the mining industry and indeed those responsible for any earth dams are keen to demonstrate their full understanding of their assets, relative to safety and risk. L-Band SAR can assist these asset owners to address their responsibilities in terms of dam safety, enabling appropriate direction of resources and funding to the areas needed, and targeted interventions and maintenance for the prevention of future failures and the increase in safety.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".