Hydraulic tomography in fractured granite: Mizunami Underground Research site, Japan
Bibliographic record
Abstract
Two large‐scale cross‐hole pumping tests were conducted at depths of 191–226 m and 662–706 m in deep boreholes at the Mizunami Underground Research Laboratory (MIU) construction site in central Japan. During these two tests, induced groundwater responses were monitored at many observation intervals at various depths in different boreholes at the site. We analyze the two cross‐hole pumping tests using transient hydraulic tomography (THT) based on an efficient sequential successive linear estimator to compute the hydraulic conductivity ( K ) and specific storage ( S s ) tomograms, as well as their uncertainties in three dimensions. The equivalent K and S s estimates obtained using asymptotic analysis treating the medium to be homogeneous served as the mean parameter estimates for the 3‐D stochastic inverse modeling effort. Results show several, distinct, high K and low S s zones that are continuous over hundreds of meters, which appear to delineate fault zones and their connectivity. The THT analysis of the tests also identified a low K zone which corresponds with a known fault zone trending NNW and has been found to compartmentalize groundwater flow at the site. These results corroborate well with observed water level records, available fault information, and coseismic groundwater level responses during several large earthquakes. The successful application of THT to cross‐hole pumping tests conducted in fractured granite at this site suggests that THT is a promising approach to delineate large‐scale K and S s heterogeneities, fracture connectivity, and to quantify uncertainty of the estimated fields.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".