Capturing aquifer heterogeneity: Comparison of approaches through controlled sandbox experiments
Bibliographic record
Abstract
Groundwater modeling has become a vital component to water supply and contaminant transport investigations. These models require representative hydraulic conductivity ( K ) and specific storage ( S s ) estimates, or a set of estimates representing subsurface heterogeneity. Currently, there are a number of approaches for characterizing and modeling K and S s heterogeneity in varying degrees of detail, but there is a lack of consensus for an approach that results in the most robust groundwater models with the best predictive ability. The main goal of this study is to compare different heterogeneity modeling approaches (e.g., effective parameters, geostatistics, geological models, and hydraulic tomography) when input into a forward groundwater model and used to predict 16 independent cross‐hole pumping tests. We first characterize a sandbox aquifer through single‐ and cross‐hole pumping tests, and then use these data to construct forward groundwater models of various complexities (both homogeneous and heterogeneous distributions). Two effective parameter models are constructed: (1) by taking the geometric mean of single‐hole test K and S s estimates and (2) calibrating effective K and S s estimates by simultaneously matching the response at all ports during a cross‐hole test. Heterogeneous models consist of spatially variable K and S s fields obtained via (1) kriging single‐hole data; (2) calibrating a geological model; and (3) conducting transient hydraulic tomography (Zhu and Yeh, 2005). The performance of these parameter fields are then tested through the simulation of 16 independent cross‐hole pumping tests. Our results convincingly show that transient hydraulic tomography produces the smallest discrepancy between observed and simulated drawdowns.
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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 | 0.001 | 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 | 0.001 |
| 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 | 0.000 |
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".