Three-dimensional hydraulic tomography analysis of long-term municipal wellfield operations: Validation with synthetic flow and solute transport data
Bibliographic record
Abstract
This study proposes the utilization of municipal well records as an alternative dataset for large-scale heterogeneity characterization of hydraulic conductivity () and specific storage () using hydraulic tomography (HT). To investigate the performance of HT and the feasibility of utilizing municipal well records, a three-dimensional aquifer/aquitard system is constructed and synthetic groundwater flow and solute transport experiments are conducted to generate data for inverse modeling and validation of results. In particular, we simultaneously calibrate four groundwater models with varying parameterization complexity using five datasets consisting of different time durations and periods. Calibration and validation results are qualitatively and quantitatively assessed to evaluate the performance of investigated models. The estimated and tomograms from different model cases are also validated through the simulation of independently conducted pumping tests and conservative solute transport. Our study reveals that: 1) the HT analysis of municipal well records is feasible and yields reliable heterogeneous and distributions where drawdown records are available; 2) accurate geological information is of critical importance when data density is low and should be incorporated for geostatistical inversions; 3) the estimated and tomograms from the geostatistical model with geological information are capable in providing robust predictions of both groundwater flow and solute transport. Overall, this synthetic study provides a general framework for large-scale heterogeneity characterization using HT through the interpretation of municipal well records, and provides guidance for applying this concept to field problems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".