Research Note: Simple formulas for pseudo‐position for electrical resistivity and IP in vertical boreholes based on mean positions of the sensitivity
Bibliographic record
Abstract
ABSTRACT The electrical conductivity method in boreholes has been applied for exploration as well as engineering and environmental investigations. The simplest deployment involves placing electrodes at varying heights within a single borehole. Borehole surveys differ from surface surveys using colinear arrays in that the ground surface is in the line of the electrodes and so it influences the measured potential in the ground differently. Multiple electrodes can be deployed on a single multichannel cable resulting in measurements from non‐standard array configurations. The choice of the plot point for pseudo‐sections can be difficult for these non‐standard arrays. The mean of the sensitivity function of a constant resistivity half space has been shown to yield simple and useful formulas for pseudo‐positions for four electrode surface arrays. In this contribution, I first derive the sensitivity function for electrodes in a vertical borehole and then calculate the vertical and horizontal sensitivity functions. I then derive simple formulas for the vertical and horizontal positions of the mean of the sensitivity function for electrodes in a vertical borehole. Pseudo‐sections for synthetic data are shown to be more easily interpretable than pseudo‐sections plotted using averages of the electrode positions. The simple formulas will be useful for plotting pseudo‐sections for initial data visualization and for survey planning.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".