Estimation of Saturated Hydraulic Conductivity during Infiltration Test with the Aid of ERT and Level‐Set Method
Bibliographic record
Abstract
Core Ideas A new technique for estimating the saturated hydraulic conductivity is developed. It uses a combination of hydrogeophysical and numerical methods. Electrical resistivity tomography and the instantaneous profile method are used. The technique is validated using simple and complex numerical hydrogeological models. Many hydrogeological and geophysical tools have been developed to determine subsoil properties, but they are often limited by sparse datasets and by the portability of the method from one site to another and often underestimate the complexity of the medium. We present a saturated hydraulic conductivity ( K s ) estimation scheme, named the KES method, based on hydrogeophysical and numerical methods. The targeted medium of investigation is an unsaturated and heterogeneous soil. Estimation of K s is accomplished by estimating the position of the wetting front and the distribution and velocity of flow lines during an infiltration test. Using numerical modeling, K s is determined by minimizing the velocity difference between the measured flow lines and the modeled flow lines. Surface and buried electrodes are used as part of the electrical resistivity survey in determining the position of the wetting front. An instantaneous profile method is used to determine the water retention curve of the medium. The KES method has been tested and validated using data produced from simple and more complex geological models from published case studies. We obtained good reconstruction of the saturated hydraulic conductivity. We have found that the estimated value of K s in log scale has a mean error <2.5%. Error increases along the boundaries of different hydrofacies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".