Comparison of travel-time and geostatistical inversion approaches for hydraulic tomography: A synthetic modeling study
Bibliographic record
Abstract
Hydraulic tomography (HT) has been proven to be an effective approach in mapping the heterogeneity of hydraulic parameters. The travel-time based inversion (TTI) and geostatistical inversion (GI) approaches are two of several HT methods. In particular, the GI approach is used to compute heterogeneous hydraulic conductivity ( K ) and specific storage ( S s ) tomograms, while the TTI approach yields diffusivity ( D = K/S s ) tomograms. The main objective of this paper is to evaluate these two methods through a synthetic study. Two cases are designed based on different monitoring configurations. Two independent scenarios are designed by: providing the same data sets and providing all available data for calibration, while data selection follows recommended strategies utilized by the two approaches. Then, the estimated tomograms are evaluated by visual comparison of estimated parameter distributions and assessments of model calibration and validation results. Results show that the advantages of TTI are: (1) imaging of structural features representing high D zones; (2) requirement of less data for inverse modeling; and (3) rapid computational times. In contrast, the advantages of the GI approach are: (1) the direct characterization of both K and S s distributions; (2) better drawdown predictions; and (3) a larger estimated area. Our study suggests that the TTI approach is suitable for rapid, coarse characterization of heterogeneity that could potentially be utilized for providing hydrogeological structures for an initial model for the GI approach. The GI approach, although significantly more computationally intensive, is more robust and preferable to applications that require higher accuracy in parameter estimation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".