Monitoring the Spatial and Temporal Evolution of the Self‐Potential Field Produced by Pumping in a Fractured Aquifer
Bibliographic record
Abstract
Self potential (SP) signals associated with groundwater flow through a fractured aquifer system were continuously monitored on the surface around a pumping well in the hydrogeologically complex Springdale well field, near Sussex, New Brunswick, Canada. The objective was to investigate whether anticipated azimuthal anisotropy in groundwater flow would be reflected in the SP anomaly expected to develop on surface. SP data were measured over a 40 by 70 m area using an irregular array of 34 non‐polarizing Pb‐PbCl2 electrodes referenced to a remote electrode placed 170 m away. SP data were logged at 3 second intervals using a high input impedance (20 Gohms) datalogger connected to the electrodes through a custom‐built 36‐channel passive filter box used to attenuate noise at frequencies higher than ∼ 1 Hz. The pumping well operated for 300 minutes at a flow rate of 8 L/sec, generating drawdowns of 1.4 m in the pumping well and 1.0 m in a monitoring well 27 m away. SP increased monotonically during pumping with clear anomaly amplitudes ranging from ∼ 1 to 8 mV amongst the 34 electrodes. Linear regression of the drawdown in the monitoring well versus SP of an adjacent electrode provides an apparent streaming potential correlation coefficient of −3.36 mV/m used to calculate the approximate drawdown over the field of the SP array. This drawdown approximation assumes a homogeneous and isotropic aquifer. Time‐lapse contour maps illustrate the progression and intensification of the SP field over the study area, with signal variations greatest to the northwest through to the southwest; unlike a radially symmetric pattern one would expect in an isotropic, homogeneous aquifer. This study has revealed that SP signals generated during pumping tests have the potential to rapidly identify variations in the self‐potential field associated with groundwater flow and aquifer drawdown.
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 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".