Tomographical field investigation of hydraulic properties of a fractured aquifer using active thermal tracer testing
Bibliographic record
Abstract
Compared to porous media, fractured aquifers are generally characterized by a more pronounced hydraulic heterogeneity. To describe hydraulic properties of fractured subsurface, investigation methods such as hydraulic tests, tracer tests and hydrogeophysical tests have been widely used. In recent years, thermal tracer tests are obtaining more attention because thermal response signals can be easily and economically obtained at a high resolution, e.g. using distributed temperature sensing (DTS) systems. Some studies have even employed the travel-time-based thermal tracer tomography (TTT) to reconstruct the aquifer heterogeneity (Somogyvári M. et al., 2016; Somogyvári M. and Bayer P., 2017). In this study, we further develop and apply the TTT method for a field scale investigation of the hydraulic properties at a geothermal test site in Göttingen, Germany, equipped with five instrumented experimental wells. Presently, using travel-time-based thermal tracer tomography to describe the hydraulic connectivity or conductivity is limited to the condition that the heat transfer must be convection dominated. Thus, the field experiments have to be divided into two steps. A full length well warm water injection test is firstly conducted to obtain information about the basic hydrogeological conditions, such as the fracture insertion depth and the connectivity between the wells. Subsequently, four multilevel thermal tracer tests are performed. The temperature changes in all five wells are recorded using a DTS system. Finally, based on the travel-time-based inversion method, the hydraulic conductivity distribution of the fractured aquifer can be obtained. Preliminary test results showed that the orientation of transmissive fractures is mainly along the E-W direction at our test site. Given the good hydraulic connectivity, the first thermal tracer tomographical tests in a fractured aquifer were performed between two wells positioned along this direction. As next, we will work on the reconstruction of the fracture distribution between those two wells.
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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 | 0.000 | 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".