Time-Lapse Tomography of a Groundwater Pumping Experiment
Bibliographic record
Abstract
Climate change significantly impacts groundwater resources by altering recharge rates and thus availability, making it crucial to manage these vital reserves sustainably to ensure long-term water security. In this study we seismically monitor a series of groundwater pumping tests in the municipality of Nickelsdorf (Burgenland, Austria).  Due to expected increasing demand for water due to population development, wells were installed to ensure a sustainable drinking water supply in the long term. Traditionally monitored through point-wise hydrological wells, our approach combines nodal seismic sensors and ambient noise to broaden insights into subsurface processes affected by pumping activity. Seismic ambient noise was continuously recorded over three months in early 2023, including periods before, during, and after pumping. Our study evaluates various ambient noise sources and seismic signals, especially those generated by passing trains. To gain broader understanding of the subsurface processes we perform a time-lapse tomography to identify the location and strength of the velocity variations. Based on our analysis, we resolve increases/decreases in seismic velocity of around 10 % in the uppermost meters of the subsurface during pumping operations related to local reduction in the water table. This holistic approach aims at unveiling the behavior of the subsurface during and post-pumping, potentially offering a comprehensive understanding beyond individual hydrological wells.
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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) | 0.001 | 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 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".