Using Water Level Responses to Atmospheric Pressure Variations to Measure and Monitor Vertical Leakage Through Confining Units, With Application to the Jurassic Shaximiao Crust, China
Bibliographic record
Abstract
Abstract The upper few hundreds of meters of the crust often hosts leaky aquifers. Quantifying leakage is important if those aquifers are used as a water resource. The responses of water level to external forcing such as tides and barometric pressure changes offer the opportunity to measure aquifer hydrogeological properties and monitor possible changes in those properties. Around the Huayingshan faults adjacent to Sichuan and Chongqing provinces, China, inclined fold‐and‐thrust belts form the crust, and frequent earthquakes might impact aquifers in the shallow crust that are used for drinking water. We introduce a new computational approach for continuous modeling of water level changes in response to barometric pressure variations to identify when the signals are reliable and then determine values of aquifer transmissivity and aquitard hydraulic diffusivity. Computed aquifer transmissivity agrees with values from well tests. We obtain horizontal and vertical hydraulic parameters for more than 10 years (from 2008 to 2019). Of the six wells studied, five have aquitard vertical hydraulic diffusivities at least two orders of magnitude greater than aquifer horizontal transmissivity. Although several regional and teleseismic earthquakes caused changes in water levels in one of the wells with relatively low vertical permeability, we do not see clear changes in hydraulic properties in response to the earthquakes. We also identify small long‐term trends and seasonal variations in hydrogeological properties.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".