Hydraulic Tomography Analyses of Different Datasets for Subsurface Heterogeneity Characterization at Various Scales
Bibliographic record
Abstract
Hydraulic Tomography (HT) has been evaluated to be a robust approach for high-resolution characterization of subsurface heterogeneity. However, geostatistics-based HT may produce overly smooth distributions of hydraulic parameters such as hydraulic conductivity (K) and specific storage (Ss) when hydraulic head data used to constrain the inversion is limited. Furthermore, only a few HT studies have been performed for large-scale field problems due to the difficulty in conducting dedicated HT surveys at large-scale sites, as well as due to uncertainty regarding model conceptualization. This thesis documents four studies designed to investigate the performance of HT in characterizing the spatial distributions of K and Ss at various scales through the inclusion of different types of data for inverse modeling. Study 1 investigates the effect of prior geological information on K and Ss heterogeneity characterization through Transient Hydraulic Tomography (THT) analysis of laboratory sandbox data. Study 2 explores the feasibility of THT analysis of long-term municipal well records for large-scale heterogeneity characterization through synthetic experiments, while Study 3 extends the synthetic study to a field application utilizing data from a wellfield in Kitchener, Ontario, Canada by addressing uncertain initial and boundary conditions for inverse modeling. Finally, Study 4 evaluates the usefulness of field cross-hole flowmeter measurements in mapping spatial K distribution through Steady-State Hydraulic Tomography (SSHT) analysis at a highly heterogeneous field site located on the University of Waterloo. Results from these studies mainly reveal that: (1) the incorporation of prior geological information into geostatistical inverse models improves characterization significantly when pumping tests and drawdown measurements are sparse; however, attention must be paid when constructing geological models for reliable structure information, (2) existing municipal wellfield records could be utilized for large-scale heterogeneity characterization using the approach of HT when uncertainties regarding initial and boundary conditions are well addressed for inverse modeling, and (3) the integration of cross-hole flowmeter measurements with hydraulic head data improves characterization results in terms of revealing K heterogeneity details and predicting independent hydraulic test data. Overall, the body of work presented in this thesis advocates the inclusion of additional datasets that carry non-redundant heterogeneity information for geostatistical inverse modeling and demonstrates the feasibility of utilizing alternative datasets in HT for subsurface heterogeneity characterization at large-scale sites.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".