Modeling radio imaging (RIM) data with the Comsol RF module
Bibliographic record
Abstract
Summary The radio imaging method (RIM) employs the propagation of radio-frequency EM waves (100 kHz to 10 MHz) to image the conductivity distribution between the boreholes. We studied the wave propagation using the finite-element-modeling (FEM) algorithm implemented in the Comsol RF module. Appropriate element sizes are quantified by comparing the Comsol modeling results of 6 types of element sizes at 4 frequencies with analytical solutions for the homogeneous whole-space model. The comparison reveals that modeled data with 5 elements per wavelength have errors less than 5%; 7 to 8 elements per wavelength provide errors around 1%; and when there are 10 elements per wavelength, the errors are less than 1%. We also compared the solutions for spherical models, which shows the Comsol solutions are consistent with the analytical solutions and the solutions from a finite-difference time-domain algorithm. To illustrate the flexibility of Comsol package, we provide an example with two moderately conductive bodies between boreholes. The EM wave attenuation and reflection by the conductive bodies can be observed on the relative variation map. We used the synthetic data to reconstruct a tomographic image with the SIRT algorithm. The image shows that the location of the conductive anomalies are reconstructed fairly successfully, although, there are some artifacts. From our work, we conclude that Comsol modeling is helpful to study the radio wave propagation and tomographic imaging methods.
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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".