Measuring Fracture Flow Changes in a Bedrock Aquifer Due to Open Hole and Pumped Conditions Using Active Distributed Temperature Sensing
Bibliographic record
Abstract
Abstract Efficiently measuring groundwater flow in bedrock aquifers is inherently challenging due to the irregular distribution and fine scale of fractures. Recent advances in Active Distributed Temperature Sensing (A‐DTS) in boreholes temporarily sealed with liners have made it possible to quantify flow rates in such aquifers at many different depths using heat as a tracer, but until now only data collected under a single hydraulic condition have been published. This paper presents the first field data from multiple A‐DTS field tests conducted under different hydraulic conditions to quantify groundwater flow redistribution within a bedrock aquifer. Three separate quasi steady state A‐DTS tests were collected in a sealed borehole: (1) natural gradient condition where all boreholes were sealed with flexible and impermeable liners, (2) cross‐connected condition where a nearby borehole was open allowing vertical flow within the borehole, and (3) forced gradient condition where the nearby open borehole was pumped at a constant rate of 54 L/min. The depth‐discrete hydraulic head responses were also measured during the three tests using a string of transducers in a sealed borehole. Results provide quantifiable insights as to how the bedrock aquifer responds, including A‐DTS‐derived measurements of flow changes in fractures at multiple depths driven by changes in gradients. The results confirm that a single open borehole or long‐screened well can significantly alter the site hydraulics and demonstrate that not all large or transmissive fractures show evidence of active flow and thus, transmissivity and aperture should not be used alone to infer active flow zones.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".