On the use of the ground water fluxes for hydraulic tomography: Theoretical and field-based assessments
Bibliographic record
Abstract
Hydraulic tomography is known for imaging hydraulic conductivity of aquifers. In hydraulic tomography, the aquifer is stressed sequentially at several locations with pumping or slug tests while hydraulic heads are observed in different points. These hydraulic head data along with a numerical model are then used to reconstruct the hydraulic conductivity distribution of the aquifer through inversion process. The reconstructed distribution usually represents smooth-low resolution model of hydraulic conductivity which may be suitable for representation of groundwater flow with limited applicability to transport problems. Here, we investigate the added value of using groundwater fluxes measurement for the reconstruction of hydraulic conductivity in tomographic experiment. Vertical profile of groundwater flux may be estimated using active fiber optic distributed temperature sensor (FO-DTS) methods with FO cables installed by direct push so as it is in direct contact with formation. In active FO-DTS, FO cable is heated and heat is transported by conduction and convection. So different water fluxes result in different temperature behavior. This study is carried out in two parts. First, we conducted a synthetic analyze where we used a sequence of synthetic multivariate Gaussian aquifers with different tomographic configurations and datasets. This analysis showed that joint inversion of groundwater fluxes and hydraulic heads leads to better hydraulic conductivity resolution than using hydraulic heads solely. Inversion of groundwater fluxes alone is also superior than using only hydraulic heads. Then, insights gained from the synthetic study were used to guide the implementation of a field study at the Saint-Lambert experimental site located 40 km south of Quebec City, Canada. The tomography experiment was performed between 3 wells closely spaced (between 5 and 9 m) and two active FO-DTS cables. FO cables were installed vertically by a direct push drilling technique at mid-point between the central pumping well and two observation wells. Discrete intervals along the observation wells were also isolated with packers to monitor temperature and hydraulic heads at different depths in these two screened observational wells. First, the aquifer was constrained to pumping continuously for 24 hours at a constant rate of 10 LPM with simultaneously recording temperature (passive mode) and hydraulic heads in 8 discrete well intervals and in the pumping well itself as well as along the 2 FO-DTS with approximate resolution of 25 cm. Then, by analyzing the piezo-metric heads and making sure that steady-state conditions were achieved, the pumping was held at the same rate but heat was injected to fiber optic cables (active mode) for another 64-hour period. After this period, heating and pumping were stopped. Preliminary results show the feasibility of the active FO-DTS in capturing varying groundwater fluxes with depth, as reflected in the different temporal temperature trend. These temperature trends will be used to estimate the vertical groundwater flux profile from these temperature temporal trends at a vertical resolution of approximately 25 cm. Then estimated fluxes will be used for hydraulic tomography. Those experimental results along with the synthetic analyze are shown to be promising in improving characterization of hydraulic conductivity of aquifers.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".