Full Waveform Inversion of Electric Conductivity with Radio-Frequency Electromagnetic Waves
Bibliographic record
Abstract
Summary Radio-frequency imaging (RIM) is a cross-borehole technique to image electromagnetic subsurface properties from measurements of radio-frequency waves. RIM operates at mid-range frequencies and has most applications in mining. Traditionally, RIM problem has been solved by straight ray tomography. Recently, an inverse scattering method has been proposed demonstrating the potential for higher-resolution images by incorporating more exact physics into the inversion process. We present an application of full waveform inversion (FWI) to conductivity imaging with RIM data. FWI is a high resolution technique, in which the physical property is updated iteratively to minimize the misfit between the measured and modelled wavefields. The full waveform modelling with Maxwell’s equations is efficiently implemented in the frequency domain. The model update is calculated by the L-BFGS method, where the gradient is evaluated by the adjoint state technique. We show that the resolution of a half-width of the first Fresnel zone is achievable to correctly recover the shape and location of conductive targets. Large conductivity contrasts are underestimated due to attenuation of the wavefields in highly conductive zones. The method can be extended to include electric permittivity inversion.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".