Transient hydraulic tomography in a fractured dolostone: Laboratory rock block experiments
Bibliographic record
Abstract
The accurate characterization of fractured geologic medium, imaging of fracture patterns and their connectivity have been a challenge for decades. Recently, hydraulic tomography has been proposed as a new method for imaging the hydraulic conductivity (K) and specific storage (Ss) distributions of fractured geologic media. While encouraging results have been obtained in the field, the method has not been rigorously assessed in a controlled laboratory setting. In this study, we assess the performance of transient hydraulic tomography (THT) in a fractured dolomitic rock block. The block is characterized through flow‐through tests and multiple pumping tests. The pumping test data were then analyzed with the THT code of Zhu and Yeh (2005) to image the fracture patterns and their connectivity through the delineation of K and Ss distributions (or tomograms). Results show that the THT analysis of pumping tests yields high‐K and low‐Ss zones that capture the fracture pattern and their connectivity quite well and those patterns become more vivid as additional pumping test data are added to the inverse model. The performance of the estimated K and Ss tomograms are then assessed by: (1) comparing the tomograms obtained from synthetic to real data; (2) comparing the tomograms from two different pumping configurations; (3) comparing the estimated geometric mean of the hydraulic conductivity (KG) from the K tomogram to the effective hydraulic conductivity (Keff) estimated from the flow‐through tests; and (4) predicting five independent pumping tests not used in the construction of the K and Ss tomograms. The performance assessment of the K and Ss tomograms reveals that THT is able to image high‐K and low‐Ss zones that correspond to fracture locations in the fractured rock block and that the tomograms can be used to predict drawdowns from pumping tests not used in the construction of the tomograms with reasonable fidelity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".