Imaging Temporal Changes with Time-Lapse Seismic and GPR Methods – Rybnik Dam Survey
Bibliographic record
Abstract
Summary The Rybnik reservoir and its main dam, built in 1971, serve as a key element in power generation and flood mitigation. Over the years, the dam has faced multiple flood events, including the catastrophic 1997 flood. In 2024, it withstood southern Poland’s largest flood of the past decade. Two seismic campaigns were conducted in 2023 and 2024 to support dam monitoring. Both used 3C seismic acquisition geometries and were enhanced by Distributed Acoustic Sensing (DAS) and Spectral Ground Penetrating Radar (SGPR). Various fibre-optic cables, interrogators, and seismic sources, including sledgehammers and industrial sources, were tested in dense 5-meter spacing to develop a cost- and time-efficient methodology for dam investigation. The resulting dataset enables an innovative, high-resolution approach to monitoring structural conditions. SGPR proved critical for imaging the uppermost 8 meters, beyond the capability of seismic methods. The DAS and 3C data captured seasonal variations and revealed the underlying geological structure, including remnants of the original riverbed. These findings are particularly relevant for detecting water seepage beneath the dam, which must be monitored precisely. The work is crucial in the context of ageing hydrotechnical infrastructure and increasing environmental stressors.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".