Monitoring and modelling of pumping-induced self-potentials for transmissivity estimation within a heterogeneous confined aquifer
Bibliographic record
Abstract
Variations in self-potentials (SP) measured at surface during pumping of a heterogeneous confined fractured rock aquifer have been monitored and modelled in order to investigate capabilities and limitations of SP methods in estimating aquifer hydraulic properties. SP variations were recorded around a pumping well using an irregular grid of 31 non-polarizing Pb-PbCl2 that were referenced to a remote electrode and connected to a commercial multiplexer and digitizer/data logger through a passive lowpass filter on each channel. The lowpass filter reduced noise by a factor of 10 compared to levels obtained using the data logger's integration-based sampling method for powerline noise suppression alone. SP signals showed a linear relationship with water levels observed in the pumping and monitoring wells over the pumping period, with an apparent electrokinetic coupling coefficient of −3.4 mV · m−1. Following recent developments in SP methodology, variability of the SP response between different electrodes is taken as a proxy for lateral variations in hydraulic head within the aquifer and used to infer lateral variations in the aquifer's apparent transmissivity. In order to demonstrate the viability of this approach, SP is modelled numerically to determine its sensitivity to (i) lateral variations in the hydraulic conductivity of the confined aquifer and (ii) the electrical conductivity of the confining layer and conductive well casing. In all cases, SP simulated on the surface still varies linearly with hydraulic head modelled at the base on the confining layer although the apparent coupling coefficient changes to varying degrees. Using the linear relationship observed in the field, drawdown curves were inferred for each electrode location using SP variations observed over the duration of the pumping period. Transmissivity estimates, obtained by fitting the Theis model to inferred drawdown curves at all 31 electrodes, fell within a narrow range of (2.0–4.2) × 10−3 m2 · s−1 and were consistent with values measured in the pumping and monitoring wells. This approach will be of particular interest where monitoring wells are lacking for direct measurement, and SP on the surface can be used to quickly estimate hydraulic properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".