Recent developments, challenges, and future research directions in tomographic characterization of fractured aquifers
Bibliographic record
Abstract
This work reviews various studies of hydraulic and pneumatic tomography for estimation of flow properties of fractured geologic media with hydraulic and pneumatic tomography. The underlying conceptual inversion models can be broadly classified as continuum and discrete fracture network models and deterministic and stochastic approaches. A heterogeneous continuum method applies porous media parameters, while a DFN approach utilizes structural and hydraulic properties of fractures. An overview of field, laboratory, and synthetic studies with applications of hydraulic, pneumatic, or tracer tomography for the characterization of fractured geologic media shows that most studies rely on a heterogeneous continuum conceptual model and geostatistical methods to achieve a solution to the inverse problem. The application of a heterogeneous continuum model results in hydraulic properties that are representative of both fracture and matrix. Therefore, this approach may be more operationally useful for large scale sites with a non-negligible hydraulic conductivity of the rock matrix and high fracture intensity. The flow properties of single fractures can be estimated by applying a discrete fracture network (DFN) model. However, assumptions concerning fracture patterns and corresponding flow properties can lead to an oversimplified geological model. Possibilities for future research include integrating additional data and results from other inversion methods, the application of neural networks for inversion, the implementation of inversion results for the prediction and optimization of processes according to the planned application at the site, and opportunities for real-time inversion.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".