Assessment of Surface Displacement at Ash Dam Facility Utilising InSAR
Bibliographic record
Abstract
This research delineates the assessment of surface displacement at the Ash Dam Facility in South Africa utilizing InSAR technology.ADF may fail owing to several circumstances, including structural instability, seepage, or seismic activity.Consequently, the application of InSAR technology necessitates the rapid identification and response to hazards in order to limit repercussions such as loss of life and property resulting from dam failures.Interferometric Synthetic Aperture Radar (InSAR) is an advanced remote sensing technology that is essential for assessing the safety and integrity of ash dams.The study utilized Vertex, the Alaska Satellite Facility's (ASF) data search application for remotely sensed imagery, facilitating efficient identification and download of SAR data, along with direct access to thematic datasets.ASF Data Search is a userfriendly tool for locating SAR data and efficiently processing advanced SAR products, including InSAR and Auto-RIFT, through ASF's services.The research performed a time series analysis with Mintpy on the OpenSAR Lab server.The Mintpy toolbox is a Python 3 application designed for modest baseline InSAR time series analysis.The input consists of a series of differential interferograms that create a completely interconnected network.The results indicated that the ash dam facility has experienced a total vertical displacement of 200 cm and a lateral displacement of 310 cm.The coordinates of the impacted area are (7145360, 739280) and (7160480, 714480) in terms of latitude and longitude.In conclusion, satellite remote sensing provides a cost-effective and time-efficient means to monitor extensive infrastructure assets, a task that would otherwise need significant resources through traditional approaches.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".