Heterogeneous aquifer characterization from ground-penetrating radar tomography and borehole hydrogeophysical data using nonlinear Bayesian simulations
Bibliographic record
Abstract
Abstract It is known that the heterogeneity of hydraulic conductivity drives the groundwater flow and the transport of contaminants. However, in conventional characterization methods, the lack of densely sampled hydrological data does not permit us to describe the aquifer heterogeneity at an appropriate scale. In this study, we integrate ground-penetrating radar (GPR) tomographic data with hydraulic conductivity logs to estimate the hydraulic conductivity of a heterogeneous unconsolidated aquifer at a decimetric scale between two boreholes. The integration of these different data sets is achieved using a nonlinear Bayesian simulation algorithm. The prior hydraulic conductivity distribution is estimated, under Gaussian hypothesis, by simple kriging of the hydraulic well data. The likelihood of hydraulic conductivity given the relative permittivity and the electrical conductivity functions is obtained from a kernel probability density function estimator that describes the in-situ relationship between the electric and the hydraulic properties measured along boreholes. The proposed method is tested on a synthetic heterogeneous model of permeability to validate the methodology. We show that permeability realizations obtained from the proposed algorithm present a higher correlation with the synthetic model than other classical simulation methods. The method is then applied on data acquired over an unconsolidated aquifer located in Saint-Lambert-de-Lauzon, Quebec, Canada. The data set consists of measurements from (i) GPR crosshole acquisition, (ii) cone penetration testing with pressure measurement combined with soil moisture resistivity, and (iii) a borehole electromagnetic flowmeter. By using the presented Bayesian approach, we generated multiple hydraulic conductivity realizations that are in good agreement with the hydrogeological model of the area.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".