Nonlinear estimation of aquifer parameters from surficial resistivity measurements
Bibliographic record
Abstract
Abstract. The present study is focused on an examination of the correlation relationships for hydraulic permeability and transmissivity with electrical resistivity in a range of fractured and alluvial aquifers. The observed permeability data for fractured rock aquifers at some locations is correlated nonlinearly with electrical resistivity of the aquifers estimated from resistivity sounding data and it is found that the permeability of the aquifer in this region exponentially decreases with increase in resistivity. Permeability of the hard rock aquifer within the weathered zone and alluvium aquifers increases exponentially with increase in resistivity, and transmissivity decreases exponentially. However, in case of fracture rock and sandwiched aquifers, transmissivity increases exponentially with increase in resistivity. An attempt has been made to find general functional relationship between hydraulic parameters and resistivity of the aquifer, and therefore, published and observed data from India and other parts of the world has been taken under consideration. It is found that for fracture rock and alluvium aquifers, permeability and the transmissivity are best defined as the exponential functions of aquifer resistivity. The application of electrical parameters obtained from resistivity data for evaluation of hydraulic parameters has been demonstrated in detail within the Osmania University Campus, Hyderabad (India). The empirical relations between aquifer parameters and resistivity are established for transforming resistivity distribution into permeability and transmissivity of the aquifer. The information thus obtained from resistivity data on permeability of the aquifer and transmissivity distribution in the study area can be used for optimal use and assessment of water resources.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".