Analytically Enhanced Random Walk Approach for Rapid Concentration Mapping in Fractured Aquifers
Bibliographic record
Abstract
The efficient management and remediation of contaminated fractured aquifers necessitate an accurate prediction of the spatial distribution of contaminant concentration within the system. Related existing analytical solutions are only applicable to single fractures and have not yet been extrapolated to the aquifer scale where a network of connected fractures exists. The Random Walk Particle Tracking (RWPT) method has been extensively adopted for concentration mapping in Discrete Fracture Networks (DFNs), albeit at exorbitant computational costs and without efficiently accommodating complex physical processes (e.g., two-site kinetics). This study introduces an analytically enhanced Spatiotemporal Random Walk (STRW) approach that facilitates the efficient time-dependent mapping of contaminant concentration in DFNs. The STRW approach employs a distribution function to simultaneously estimate the displacement of particles released through the system either instantaneously or over time. The STRW approach efficiently reproduced the contaminant concentration, calculated using available analytical solutions under a range of fate and transport mechanisms. The efficacy of the STRW approach is also confirmed in a synthetic impermeable DFN through replicating the concentration maps produced using the RWPT method. The developed approach represents an accurate and computationally efficient dynamic concentration mapping technique that can support the effective operation, management, and remediation of fractured aquifers under contamination events.
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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) | 0.000 | 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 | 0.001 |
| 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".