Geostatistical simulations of the full 3D block hydraulic conductivity tensor considering inner local-scale variability
Bibliographic record
Abstract
In regional hydrostratigraphic systems, heterogeneities in hydraulic conductivity (K) play a central role for flow velocities and contaminant transport modelling. Typical field measurements of K usually only represent one or at most a few local hydrofacies of a single hydrostratigraphic unit (HSU). At the regional scale, numerous HSUs exist, each showing a range of K-measurements spanning several orders of magnitude. For realistic numerical modelling, an intermediate scale, i.e. at the block scale, is required between the local and regional scales. Each HSU block must therefore be equipped with an equivalent 3D conductivity tensor that accounts for the connectivity of hydrofacies and conductivity variations at the local scale. We propose a four-step approach: 1- local-scale simulation of K for each HSU, 2- upscaling of local (quasi-point) realizations into the full 3D block K-tensors using a block by block finite element flow simulator, 3- definition of the block K-tensor spatial covariance, and 4- direct simulation of block K-tensors at the regional scale. This approach was applied for regional groundwater flow modelling on a complex 3D deterministic model, the Innisfil Creek watershed, Ontario, Canada. The K database was built using 1,086 transmissivity measurements extracted from public wells, 32 HSU borehole samples for laboratory permeability tests and 1,694 grain size analyses from high-resolution sampling of 15 boreholes located in the study area. The latter were used to characterize local-scale variability of K. A non-conditional turning bands method was used to simulate the local-scale scalar K fields. Block 3D hydraulic conductivity tensors were obtained by upscaling the local-scale simulation to the block scale using the Saltflow finite element flow simulator. Analysis of the upscaled K-tensor components revealed a clear control of principal components with strong correlations between them. The K-tensors showed correlation ranges of a few to several blocks depending of the HSU. The tensor covariance of the principal components was combined in a linear model of coregionalisation to simulate a series of block K-tensor fields within a fixed hydrostratigraphic model. Groundwater flow simulated using the ensemble K field was compared to a deterministic flow field using a single calibrated K-tensor per HSU. The uncalibrated fields simulated with the K tensor showed a match to available hydraulic head data which was comparable to the match of the calibrated deterministic model. The impact of K-tensor uncertainty on a typical flow simulation response was assessed and proved non-negligible. The approach can easily be applied to include uncertainty of the hydrostratigraphic model itself to obtain a better assessment of the complete uncertainty on the flow 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".