The effect of dielectric permittivity on the fields radiated from a radio-frequency electric dipole in a homogeneous whole space
Bibliographic record
Abstract
ABSTRACT The radio imaging method (RIM) is an electromagnetic cross-borehole method with applications in mineral exploration, as well as in the coal industry, where it is used across mine drives. Attenuation of the signal from conductive regions may indicate areas of mineralization, and these conductive effects in general dominate the response. In an effort to better understand the effect of a material’s dielectric permittivity on the response of the RIM, we have developed a simple program to model an electric dipole in a homogeneous whole space. When increasing the dielectric permittivity, the amplitude peak broadened and increased, whereas the phase peak sharpened and shifted negatively. To showcase the effect of dielectric permittivity on RIM data, data recorded from two transmitter positions in a moderately homogeneous zone in the Sudbury Basin were curve fit, and we concluded that despite the stronger effect that conductivity has on the signal, RIM is still sensitive to dielectric permittivity, and appropriate values must be used when developing conductivity tomograms. In addition, we found that for the given situation and frequencies used, an increase in either the conductivity or dielectric permittivity could be accounted for by a decrease of approximately the same factor in the other variable. However, the low-conductivity, high-permittivity case seemed to fit the shape of the amplitude and phase curves better. For the sulfide impregnated crystalline rocks at our field site, relative dielectric constants of 26.4 and 31 at 1250 and 625 kHz, respectively, were inferred.
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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 | 0.000 | 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) | 0.001 | 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".