Travel Time Uncertainty Reduction by Multiobjective Optimisation of Isotope Age Tracer Models
Bibliographic record
Abstract
Quantification of the travel time distribution and transport parameters in fractured aquifers is crucial for understanding contaminant transport in fractured systems. Although knowledge about the travel time distribution is a helpful tool to assess the solute transport, it can’t be measured directly. Travel time is typically back-computed from different tracers in groundwater by applying well established analytical methods. However, in fractured aquifers diffusive exchange with the rock matrix, intersection of streamtubes and associated mixing, as well as other processes can cause deviation of the estimated travel time from the mean advective travel time. Direct numerical modelling of the tracer’s reactive behavior with the travel time as one of the calibrated parameters can lead to non-uniqueness of the result. These non-unique solutions typically lead to a high level of parametric uncertainty especially on catchment scale. In this work, we address the reduction of uncertainty in mean travel time, shape parameter of travel time distribution, fracture aperture, and porosity by means of multiobjective optimization enhanced by surrogate modelling. For pre-selection of potentially plausible model runs Gaussian Processes Emulation (GPE) was applied within four-parametric space. We use the GPE with multitracer conditioning for pre-selection of plausible parameter combinations. Posterior distributions were employed to estimate the mean groundwater travel times at sampling locations, to distinguish between different rock facies of captured streamlines, and to get an estimate of fracture apertures. We confirm the hypothesis that using tritium and helium isotopes together with radiogenic helium measurements helps to achieve a unimodal posterior distribution and reduces uncertainty significantly.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".