Hydraulic conductivity estimation and lithological classification of an esker aquifer system using surface electrical resistivity surveys and a neural network
Bibliographic record
Abstract
Empirical and theoretical relationships between hydraulic conductivity, lithology, and electrical resistivity provide a basis for the use of electrical resistivity for aquifer characterization in unconsolidated sediments. This study demonstrates a meaningful field-scale correlation between vertically distributed hydraulic conductivity obtained from packer-based borehole hydraulic tests, and electrical resistivity obtained from surface-based geophysical surveys over the Vars-Winchester esker aquifer system, Ontario, Canada. Electrical resistivity alone has order-of-magnitude predictive capacity for hydraulic conductivity, but is insufficient to reliably discriminate between aquifer and aquitard lithology. An alternative methodology is developed that takes advantage of the observed correlation between hydraulic conductivity and elevation, and the separability of lithology in terms of elevation. Electrical resistivity and elevation are combined as predictor variables for hydraulic conductivity using both multiple linear regression and nonlinear neural network regression, and for neural network classification of lithology. Neural network regression results in prediction accuracy for log-transformed hydraulic conductivity of 0.38–0.52 with clear definition of vertical and lateral heterogeneity. Classification accuracy for lithology is 83–84% with high probability of discrimination between the unconsolidated aquifer and aquitard sediments, and lower probability identification of the bedrock surface due to fewer samples at depth and limited penetration depth of the resistivity survey.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".