Data driven deep learning method for quantifying groundwater flux in deep fractured aquifers with the fractured rock passive flux meter
Bibliographic record
Abstract
• Groundwater fluxes in fractured aquifers were measured using the Fractured Rock Passive Flux Meter (FRPFM). • A deep learning model (YOLOv8) was applied to identify dye marks on fabric and quantify flux parameters from laboratory experiments. • YOLOv8 achieved high accuracy with precision (P) = 0.99 and recall (R) = 0.75 for object detection and mask predictions. • Groundwater fluxes were estimated with relative errors of ±23 % ( Δ z d y e ) and ±16 % ( A d y e ), yielding an overall ±20 % error. • The Precision-Recall curve analysis suggests that model accuracy can be further improved with a larger training dataset. Movement of groundwater in fractured aquifers is highly variable and depends on many factors besides fracture apertures. Hence, downhole techniques that directly map fracture locations, orientations, apertures, and measure groundwater fluxes are valuable tools. Here, we explored the possibility of using the Fractured Rock Passive Flux Meter (FRPFM) with visible dye component to measure groundwater fluxes and identify geometric fracture parameters through laboratory experiments. For this purpose, we used the deep learning model YOLOv8 to accurately identify the dye marks and to measure their areas A d y e and widths Δ z d y e from images of the dyed fabric. Results showed that groundwater fluxes were measured with relative errors of ±23 % and ±16 % based on Δ z d y e and A d y e , respectively, with an overall relative error of ±20 %. The YOLOv8 model showed very good accuracy by achieving high precision P = 0.99 and recall R = 0.75 for both object detection and mask predictions. The P - R -curve showed that accuracy can be improved by using more images to train the model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".