Groundwater Flow Quantification in Fractured Rock Boreholes Using Active Distributed Temperature Sensing Under Natural Gradient Conditions
Bibliographic record
Abstract
Abstract Detection and quantification of groundwater flow in fractures is challenging due to its irregular distribution and fine scale, requiring intensive and depth‐discrete field data collection along boreholes. This study presents a new method using fiber optic active distributed temperature sensing (A‐DTS) in sealed boreholes to efficiently quantify depth‐discrete flow rates along the full length of a bedrock borehole. The method combines field data and numerical modeling to quantify groundwater flow rates under natural gradient conditions, which is important for assessing groundwater flow and contaminant transport. An empirical relationship between enhanced heat dissipation and groundwater flow rates is determined using a numerical model of groundwater flow and heat transport for a system of idealized parallel plate fractures in a homogeneous porous rock with negligible flow through the rock matrix. The empirical relationship is applied to a detailed profile of apparent thermal conductivity measured using A‐DTS that combines the effect of rock thermal properties and groundwater flow. In zones with no flow, the A‐DTS‐derived apparent thermal conductivity matches the laboratory effective rock thermal conductivity values measured independently. Local increases of A‐DTS apparent thermal conductivity relative to the rock matrix thermal conductivity can be used to estimate groundwater flow rates using the empirical relationship. The results are in reasonable agreement with straddle pacer tracer dilution tests in the same borehole, which helps to validate the approach. This new approach allows identification of active flow zones and quantification of flow rates and can be efficiently applied in single or multiple boreholes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".